Thinking in Pandas. How to Use the Python Data Analysis Library the Right Way (Stepanek) 1 ed (2020)

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Thinking in
Pandas
How to Use the Python Data
Analysis Library the Right Way

Hannah Stepanek

Thinking in Pandas
How to Use the Python Data
Analysis Library the Right Way
Hannah Stepanek

Thinking in Pandas
ISBN-13 (pbk): 978-1-4842-5838-5 ISBN-13 (electronic): 978-1-4842-5839-2
https://doi.org/10.1007/978-1-4842-5839-2
Copyright © 2020 by Hannah Stepanek
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or
part of the material is concerned, specifically the rights of translation, reprinting, reuse of
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Printed on acid-free paper
Hannah Stepanek
Portland, OR, USA

iii
Table of Contents
Chapter 1: Intr oduction  1
About pandas �������������������������������������������������������������������������������������������������������� 1
Ho w pandas helped build an image of a black hole ��������������������������������������������� 4
How pandas helps financial institutions make more informed predictions
about the future market ���������������������������������������������������������������������������������������� 6
How pandas helps improve discoverability of content ����������������������������������������� 6
Chapter 2: Basic Data Access and Mer ging  9
DataFrame creation and access ��������������������������������������������������������������������������� 9
The iloc method �������������������������������������������������������������������������������������������������� 11
The loc method ��������������������������������������������������������������������������������������������������� 14
Combining DataFrames using the merge method ���������������������������������������������� 17
Combining DataFrames using the join method ��������������������������������������������������� 25
Combining DataFrames using the concat method ���������������������������������������������� 27
Chapter 3: How pandas W orks Under the Hood  31
Python data structures ���������������������������������������������������������������������������������������� 32
The perfor mance of the CPython interpreter, Python, and NumPy ���������������������� 37
About the Author  vii
About the T echnical Reviewer  ix
Intr oduction  xi

iv
An introduction to pandas performance ������������������������������������������������������������� 49
Choosing the right DataFrame ���������������������������������������������������������������������������� 55
Chapter 4: Loading and Normalizing Data  65
pd �read_csv �������������������������������������������������������������������������������������������������������� 67
pd �read_json ������������������������������������������������������������������������������������������������������� 92
pd �read_sql, pd �read_sql_table, and pd �read_sql_query �������������������������������� 101
Chapter 5: Basic Data T ransformation in pandas  109
Pivot and pivot table ����������������������������������������������������������������������������������������� 109
Stac k and unstack �������������������������������������������������������������������������������������������� 113
Melt ������������������������������������������������������������������������������������������������������������������� 116
Transpose ���������������������������������������������������������������������������������������������������������� 117
Chapter 6: The apply Method  121
When not to use apply �������������������������������������������������������������������������������������� 121
When to use apply �������������������������������������������������������������������������������������������� 128
Improving performance of apply using Cython ������������������������������������������������� 131
Chapter 7: Gr oupby  135
Using groupby correctly ������������������������������������������������������������������������������������ 135
Inde xing ������������������������������������������������������������������������������������������������������������ 137
Avoiding groupby ���������������������������������������������������������������������������������������������� 139
Chapter 8: Performance Improvements Beyond pandas  141
Computer architecture �������������������������������������������������������������������������������������� 141
Ho w NumExpr improves performance �������������������������������������������������������������� 146
BLAS and LAPACK ��������������������������������������������������������������������������������������������� 150
Table of Con Ten Ts Table of Con Ten Ts

v
Chapter 9: The Futur e of pandas  157
pandas 1 �0 �������������������������������������������������������������������������������������������������������� 157
Conclusion �������������������������������������������������������������������������������������������������������� 168
Appendix: Useful Reference Tables  171
Index  181
Table of Con Ten Ts Table of Con Ten Ts

vii
About the Author
Hannah Stepanek  is a software developer
with a passion for performance and is an
open source advocate. She has over seven
years of industry experience programming
in Python and spent about two of those years
implementing a data analysis project using
pandas.
Hannah was born and raised in Corvallis,
OR, and graduated from Oregon State
University with a major in Electrical Computer Engineering. She enjoys
engaging with the software community, often giving talks at local meetups
as well as larger conferences. In early 2019, she spoke at PyCon US about
the pandas library and at OpenCon Cascadia about the benefits of open
source software. In her spare time, she enjoys riding her horse Sophie and
playing board games.

ix
About the Technical Reviewer
Jaidev Deshpande  is a senior data scientist
at Gramener, where he works on automating
insight generation from data. He has a decade
of experience in delivering machine learning
solutions with the scientific Python stack.
His research interests lie at the intersection of
machine learning and signal processing.

xi
Using the pandas Python library requires a shift in thinking that is not
always intuitive for those who use it. For beginners, pandas’ rich API can
often be overwhelming and unclear when determining which solution is
optimal. This book aims to give you an intuition for using pandas correctly
by explaining how its operations work underneath. We will establish a
foundation of knowledge covering information such as Python and NumPy
data structures, computer architecture, and performance differences
between Python and C. With this foundation, we will then be able to
explain why certain pandas operations perform the way they do under
certain circumstances. We’ll learn when to use certain operations and
when to use a more performant alternative. And near the end we’ll cover
what improvements can be and are being made to make pandas even more
performant.
Introduction

1 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_1
CHAPTER 1
Introduction
We live in a world full of data. In fact, there is so much data that it’s
nearly impossible to comprehend it all. We rely more heavily than ever
on computers to assist us in making sense of this massive amount of
information. Whether it’s data discovery via search engines, presentation
via graphical user interfaces, or aggregation via algorithms, we use
software to process, extract, and present the data in ways that make sense
to us. pandas has become an increasingly popular package for working
with big data sets. Whether it’s analyzing large amounts of data, presenting
it, or normalizing it and re-storing it, pandas has a wide range of features
that support big data needs. While pandas is not the most performant
option available, it’s written in Python, so it’s easy for beginners to learn,
quick to write, and has a rich API.
About pandas
pandas is the go-to package for working with big data sets in Python. It’s
made for working with data sets generally below or around 1 GB in size,
but really this limit varies depending on the memory constraints of the
device you run it on. A good rule of thumb is have at least five to ten times
the amount of memory on the device as your data set. Once the data set
starts to exceed the single-digit gigabyte range, it’s generally recommended
to use a different library such as Vaex.

2
The name pandas came from the term panel data referring to tabular
data. The idea is that you can make panels out of a larger panel of the data,
as shown in Figure  1-1 .
restaurant location
Diner (4, 2)
Diner (4, 2)
Pandas (5, 4)
Pandas (5, 4)
date score
02/18 90
05/18 100
04/18 55
01/18 76
Figure 1-1. Panel data
When pandas was first implemented, it was tightly coupled to
NumPy, a popular Python package for scientific computing providing
an n-dimensional array object for performing efficient matrix math
operations. Using the modern implementation of pandas today, you can
still see evidence of its tight coupling in the exposition of the Not a Number
(NaN) type and its API such as the dtype parameter.
pandas was a truly open source project from the start. The original
author Wes McKinney in the Python Podcast.__init__ admitted, in order
to foster an open source community and encourage contributions, pandas
was tied perhaps a little too closely to the NumPy Python package, but
looking back, he wouldn’t have done it any different. NumPy was and still
is a very popular and powerful Python library for efficient mathematical
arithmetic. At the time of pandas inception, NumPy was the main
Chapter 1 Introdu CtIon

3
data computation package of the scientific community, and in order
to implement pandas quickly and simply in a way that was familiar to
its existing user and contributor base, the NumPy package became the
underlying data structure of the pandas DataFrame. NumPy is built on
C extensions, and while it supplies a Python API, the main computation
happens almost entirely in C, which is why it is so efficient. C is much
faster than Python because it is a low-level language and thus doesn’t
consume the memory and CPU overhead that Python does in order
to provide all the high-level niceties such as memory management.
Even today, developers still rely heavily on NumPy and often perform
exclusively NumPy-based operations in their pandas programs.
The difference in performance between Python and C is often not
very significant to the average developer. Python is generally fast enough
in most cases, and the nicety of Python’s high-level language qualities
(built-in memory management and pseudo-code like syntax, to name a
few) generally outweighs the headaches of having to manage the memory
yourself. However, when operating on huge data sets with thousands of
rows, these subtle performance differences compound into a much more
significant difference. For the average developer, this may seem absolutely
outrageous, but it isn’t unusual for the scientific research community
to spend days waiting for big data computations to run. Sometimes the
computations do really take this long; however, other times the programs
are simply written in an inefficient way. There are many different ways to
do the same thing in pandas which makes it flexible and powerful but also
means it can lead developers down less efficient implementation paths
that result in very slow data processing.
As developers, we live in an age where compute resources are
considered cheap. If a program is CPU heavy, it’s easier for us to simply
upgrade our AWS instance to a larger machine and pay an extra couple
bucks than it is to invest our time to root cause our program and address
the overtaxing of the CPU. While it is wonderful to have such readily
available compute resources, it also makes us lazy developers. We often
Chapter 1 Introdu CtIon

4
forget that 50 years ago computers took up whole rooms and took several
seconds just to add two numbers together. A lot of programs are simply
fast enough and still meet performance requirements even though they
are not written in the most optimal way. Compute resources for big data
processing take up a significant amount of energy compared to a simple
web service; they require large amounts of memory and CPU, often
requiring large machines to run at their resource limits over multiple
hours. These programs are taxing on the hardware, potentially resulting
in faster aging, and require a large amount of energy both to keep the
machines cool and also to keep the computation running. As developers
we have a responsibility to write efficient programs, not just because
they are faster and cost less but also because they will reduce compute
resources which means less electricity, less hardware, and in general more
sustainability.
It is the goal of this book in the coming chapters to assist developers in
implementing performant pandas programs and to help them develop an
intuition for choosing efficient data processing techniques. Before we deep
dive into the underlying data structures that pandas is built on, let’s take a
look at how some existing impactful projects utilize pandas.
How pandas helped build an image
of a black hole
pandas was used to normalize all the data collected from several large
telescopes to construct the first image of a black hole. Since the black hole
was so far away, it would have required a telescope as big as the Earth to
capture an image of the black hole directly, so, instead, scientists came up
with a way to piece one together using the largest telescopes we have today.
In this international collaboration, the largest telescopes on Earth were used
as a representative single mirror of a larger theoretical telescope that would
be needed to capture the image of a black hole. Since the Earth turns,
Chapter 1 Introdu CtIon

5
each telescope could act as more than one mirror, filling in a significant
portion of the theoretical larger telescope image. Figure  1-2 demonstrates
this technique. These pieces of the larger theoretical image were then passed
through several different image prediction algorithms trained to recognize
different types of images. The idea was if each of these different image
reproduction techniques outputs the same image, then they could be confident
that the image of the black hole was the real image (or reasonably close).
Figure 1-2. Using the telescopes on Earth to represent pieces of a
larger theoretical telescope
The library is open source and posted on GitHub. 1 The images from
radio telescopes were captured on hard disks and flown across the world to
a lab at the Massachusetts Institute of Technology where they were loaded
into pandas. The data was then normalized, synchronizing the captures
from the telescopes in time, removing things like interference from the
Earth’s atmosphere, and calculating things like absolute phase of a single
telescope over time. The data was then sent into the different image
prediction algorithms, and finally the first image of a black hole was born. 2
1 https://github.com/achael/eht-imaging
2 https://solarsystem.nasa.gov/resources/2319/first-image-of-a-black-hole/
Chapter 1 Introdu CtIon

6
How pandas helps financial institutions
make more informed predictions about
the future market
Financial advisors are always looking for an edge up on the competition.
Many financial institutions use pandas along with machine learning
libraries to determine whether new data points may be relevant in helping
financial advisors make better investment decisions. New data sets
are often loaded into pandas, normalized, and then evaluated against
historical market data to see if the data correlates to trends in the market.
If it does, the data is then passed along to the advisors to be used in
making financial investment decisions. It may also be passed along to their
customers so they can make more informed decisions as well.
Financial institutions also use pandas to monitor their systems. They
look for outages or slowness in servers that might impact their trade
performance.
How pandas helps improve discoverability
of content
Companies collect tons of data on users every day. For broadcast companies'
viewership, data is particularly relevant both for showing relevant
advertisements and for bringing the right content in front of interested
users. Typically, the data collected about users is loaded into pandas and
analyzed for viewership patterns in the content they watch. They may look
for patterns such as when they watch certain content, what content they
watch, and when they are finished watching certain content and looking
for something new. Then, new content or relevant product advertisements
are recommended based on those patterns. There has been a lot of work
recently to also improve business models so that users don’t get put into
Chapter 1 Introdu CtIon

7
a bubble (i.e., recommended content isn’t just the same type of content
they’ve been watching before or presenting the same opinions). Often this is
done by avoiding content silos from the business side.
Now that we’ve looked at some interesting use cases for pandas, in
Chapter 2 we’ll take a look at how to use pandas to access and merge data.
Chapter 1 Introdu CtIon

9 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_2
CHAPTER 2
Basic Data Access
and Merging
There are many ways of accessing and merging DataFrames with pandas.
This chapter will go over the basic methods for getting data out of a
DataFrame, creating a sub-DataFrame, and merging DataFrames together.
DataFrame creation and access
pandas has a dictionary-like syntax that is very intuitive for those familiar
with Python but not with pandas. Each column name is treated as a key,
and the row values are returned as the value. The DataFrame object
constructor also accepts a dictionary as a way of creating a DataFrame.
Note when you get the column from a DataFrame, it points back to the
original DataFrame, and this is what allows us to make modifications to
the original. This happens despite the syntax that implies we are storing it
into a subset of the original as demonstrated near the bottom of Listing 2- 1.
This is great for memory-based performance since we aren’t constantly
creating copies of the data.

10
Listing 2-1. Example of dictionary syntax
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info["name"]
0 Bob
1 Mary
2 Mita
Name: name, dtype: object
>> account_info["name"] = ["Smith", "Jane", "Patel"]
>> account_info
name account balance
0 Smith 123846 123
1 Jane 123972 3972
2 Patel 347209 7209
Similarly, a sub-DataFrame can be created by passing in a list of
columns as in Listing 2-2 .
Listing 2-2. Example of creating a sub-DataFrame
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info[["name", "balance"]]
Chapter 2 Basi C Data aCC ess an D Merging

11
name balance
0 Bob 123
1 Mary 3972
2 Mita 7209
The dictionary syntax can lead to confusion later on if you create a
sub- DataFrame from the original DataFrame and modify the sub-DataFrame
expecting the original to be untouched. pandas makes no guarantees outside
of the simple cases presented in Listings 2-1 and 2-2 about whether the
resulting object returned by the dictionary syntax is a view or a copy.
This is why the loc method is preferred over the dictionary syntax for
DataFrames that have multi-indexes or multi-level columns. The loc
method, which we’ll discuss in the next section, guarantees that you are
operating on the original DataFrame and not a copy. Similarly, if you truly
want a copy of the DataFrame, you should explicitly create one.
The iloc method
A DataFrame’s rows can be accessed via the iloc method which uses a list- lik e
syntax. Listing 2-3 demonstrates this.
Listing 2-3. Example of accessing rows in a DataFrame using iloc
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info.iloc[1]
name Mary
account 123972
balance 3972
Chapter 2 Basi C Data aCC ess an D Merging

12
>> account_info.iloc[0:2]
name account balance
0 Bob 123846 123
1 Mary 123972 3972
>> account_info.iloc[:]
name account balance
0 Bob 123846 123
1 Mary 123972 3972
2 Mita 347209 7209
iloc is used to index a DataFrame via integer position-based indexing.
The first position in the iloc function specifies the row indexes, while the
second position specifies the column indexes. This means we can select
rows as well as columns like in Listing 2-4 .
Listing 2-4. Example of accessing rows and columns in a
DataFrame using iloc
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info.iloc[1, 2]
3972
>> account_info.iloc[1, 2] = 3975
>> account_info.iloc[1, 2]
3975
>> account_info.iloc[:, [0, 2]]
name balance
0 Bob 123
1 Mary 3975
2 Mita 7209
Chapter 2 Basi C Data aCC ess an D Merging

13
iloc also accepts a Boolean array. In Listing 2-5 , we grab all odd rows
by taking the modulus of each row index and converting it to a Boolean.
Listing 2-5. Example of accessing rows and columns in a
DataFrame using iloc
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info.iloc[account_info.index % 2 == 1]
name account balance
1 Mary 123972 3972
iloc also accepts a function; however, this function is called once with
the entire DataFrame, and there’s little difference between passing it in
and simply calling the function beforehand so we won’t go over that here.
iloc can come in quite handy when working with multi-indexed and
multi-level column DataFrames since levels are integer values. Let’s
review an example and break it down. Here we specify the rows we want
to grab as “:” meaning we want all rows, and we use a Boolean array to
specify the columns. We grab the values for the multi-level column “data”
which are [“score”, “date”, “score”, “date”] and then create a Boolean array
by specifying that the value must equal “score”. This is broken down into
stages in Listing 2-6 so it is easier to follow.
Listing 2-6. Extracting a sub-DataFrame from a multi-indexed
multi-level column DataFrame using iloc
>> restaurant_inspections
inspection 0 1
Chapter 2 Basi C Data aCC ess an D Merging

14
data score date score date
restaurant location
Diner (4, 2) 90 02/18 100 05/18
Pandas (5, 4) 55 04/18 76 01/18
>> score_columns = (
restaurant_inspections.columns.get_level_values("data")
== "score")
>> score_columns
[True, False, True, False]
>> restaurant_inspections.iloc[:, score_columns]
inspection 0 1
data score score
restaurant location
Diner (4, 2) 90 100
Pandas (5, 4) 55 76
The loc method
loc is similar to iloc, but it allows you to index into a DataFrame via column
names or labels. Listing 2-7 shows the loc equivalents to Listing 2-4 .
Listing 2-7. Example of accessing rows and columns in a
DataFrame using loc
>> import pandas as pd
>> account_info = pd.DataFrame({
"name": ["Bob", "Mary", "Mita"],
"account": [123846, 123972, 347209],
"balance": [123, 3972, 7209],
})
>> account_info.loc[1, "balance"]
3972
Chapter 2 Basi C Data aCC ess an D Merging

15
>> account_info.loc[:, ["name", "balance"]]
name balance
0 Bob 123
1 Mary 3972
2 Mita 7209
loc can also be used on multi-indexed multi-level column DataFrames
and just like iloc supports Boolean arrays. Listing 2-8 demonstrates this.
Listing 2-8. Example of extracting a sub-DataFrame from a multi-
indexed multi-level column DataFrame using loc
>> import pandas as pd
>> account_info
account 0 1
account_info number balance number balance
name username
Bob smithb 123846 123 123847 450
Mary mj100 123972 3972 123973 222
Mita patelm 347209 7209
>> account_info.loc[
("Mary", "mj100"), pd.IndexSlice[:, "balance"]
]
0 balance 3972
1 balance 222
At the end of the dictionary syntax section, it was mentioned that the
loc method is preferred over the dictionary syntax for complex DataFrames.
Let’s look at what’s happening underneath when we use each syntax to
explain why that is. Listing 2-9 shows what each access method translates
into underneath when operating on a more complex DataFrame. Note in
the second half of Listing 2-9 where the dictionary syntax is used, the code
underneath uses the __getitem__ method and then calls __setitem__ on it.
Chapter 2 Basi C Data aCC ess an D Merging

16
This is in opposition to the loc method which calls __setitem__ directly. It is
the __getitem__ that cannot be trusted here and makes no guarantees about
whether it returns a copy or what’s called a view that points back to the
original DataFrame. In simple cases where there are not multiple levels of
columns, the code underneath in both these cases would look the same, but
in the more complex case seen here, the dictionary syntax results in chained
indexing and calls the unpredictable __getitem__.
Listing 2-9. A comparison of using loc vs. dictionary syntax to
extract a sub-DataFrame
"""
The code below is equivalent to:
account_info.__setitem__(
(slice(None), (0, 'balance')),
NEW_BALANCE,
)
"""
account_info.loc[:, (0, "balance")] = NEW_BALANCE
"""
The code below is equivalent to:
account_info.__getitem__(0).__setitem__('balance', NEW_BALANCE )
"""
account_info[0]["balance"] = NEW_BALANCE
Quite often you may have data from multiple sources that you need
to combine into a single DataFrame. Now that you know how to do some
basic data access, we’ll look at different methods for combining data from
different DataFrames together.
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17
Combining DataFrames using the merge
method
Merge works the same way as a relational database join and even has the
familiar options: outer, inner, left, and right. Merge right is essentially the
same as merge left, but the DataFrames are simply passed in in reverse
order so this chapter won’t provide an explicit example for merge right.
Inner merge is used when you want to find the intersection between
two pandas DataFrames (Figure  2-1 ). In Listing 2-10 , for example, we are
trying to find the data that is present in both data sets or in this case the
buildings that were standing in 1844 that are still standing today.
Figure 2-1. Venn diagram of inner merge
Listing 2-10. Finding 1844 buildings that are still standing in 2020
using an inner merge
>> import pandas as pd
>> building_records_1844
established
building
Grande Hotel 1830
Jone 's Farm 1842
Chapter 2 Basi C Data aCC ess an D Merging

18
Public Library 1836
Marietta House 1823
>> building_records_2020
established
building
Sam 's Bakery 1962
Grande Hotel 1830
Public Library 1836
Mayberry 's Factory 1924
>> cols = building_records_2020.columns.difference(
building_records_1844.columns
)
>> pd.merge(
building_records_1844,
building_records_2020[cols],
how='inner',
on=["building"],
)
established
building
Grande Hotel 1830
Public Library 1836
In Listing 2-11 , we are merging two data sets of gene samplings
together, meaning we want all the data from both in the same data
set without duplication. We can achieve this by doing an outer merge
(Figure  2-2 ).
Chapter 2 Basi C Data aCC ess an D Merging

19
Listing 2-11. Merging two gene samplings together without
duplicating the data in common using outer merge
>> import pandas as pd
>> gene_group1
FC1 P1
id
Myc 2 0.05
BRCA1 3 0.01
BRCA2 8 0.02
>> gene_group2
FC2 P2
id
Myc 2 0.05
BRCA1 3 0.01
Notch1 2 0.03
BRCA2 8 0.02
Figure 2-2. Venn diagram of outer merge
Chapter 2 Basi C Data aCC ess an D Merging

20
>> pd.merge(
gene_group1,
gene_group2,
how='outer',
on=["id"],
)
FC1 P2 FC2 P2
id
Myc 2 0.05 2 0.05
BRCA1 3 0.01 3 0.01
BRCA2 8 0.02 8 0.02
Notch1 NaN NaN 2 0.03
In Listing 2-12 , we are updating the modern building records with
more accurate historical data. The historical record contains the exact
established date that we would like to use to update the modern record
which contains only an estimate. First, we use a merge left to add the new
more accurate established date as a new column in the data set (Figure  2- 3).
The merge left is useful in this case since we only care about updating the
established date in the modern record for buildings that still exist. Note we
are also taking advantage of the suffixes parameter to provide names for
the column names. This is advantageous so we don’t have to rename the
column to be the same as the original column after we’re done performing
the operation. Once the merge is complete, then we need to merge the
data from the two established columns together. This is done by replacing
all the missing values (i.e., NaNs) in the old established column with the
values in the modern record. So, if the historical record has an established
date, then we use that ; otherwise, we fall back on the modern record’s
established date. Finally, the modern record’s original established column
is deleted in favor of the new column that contains the merged values from
the modern record and the historical record.
Chapter 2 Basi C Data aCC ess an D Merging

21
Listing 2-12. Updating modern records with more accurate
historical data using a merge left
>> import pandas as pd
>> building_records_1844
building established
Grande Hotel 1832
Jone 's Farm 1842
Public Library 1836
Marietta House 1823
>> building_records_2020
building established
Sam 's Bakery 1962
Grande Hotel 1830
Public Library 1836
Mayberry 's Factory 1924
Figure 2-3. Venn diagram of merge left
Chapter 2 Basi C Data aCC ess an D Merging

22
>> merged_records = pd.merge(
building_records_2020,
building_records_1844,
how='left',
right_on="building",
left_on="building",
suffixes=("_2000", ""),
)
>> merged_records
building established_2000 established
Sam’s Bakery 1962 NaN
Grande Hotel 1830 1832
Public Library 1835 1836
Mayberry’s Factory 1924 NaN
>> merged_records[" established "].fillna(
merged_records[" established_2000 "],
inplace=True,
)
>> del merged_records["established_2000"]
>> merged_records
building established
Sam 's Bakery 1962
Grande Hotel 1832
Public Library 1836
Mayberry 's Factory 1924
In Listing 2-13 , we are planning on running a third medical trial, and we
want to generate a list of participants that are eligible. Only participants who
have participated in the previous trial a or b, but not both, will be eligible for
the third trial. In order to generate a list of eligible patients, we need to use
an anti-join method when performing a merge of trial a and trial b patients
(Figure  2-4 ). pandas merge method provides a parameter called indicator that
Chapter 2 Basi C Data aCC ess an D Merging

23
adds an additional column called _merge into the resulting DataFrame that
reports whether the key is present in left_only, right_only, or both DataFrames.
This comes in handy in this particular case as we wish to do a somewhat
unconventional merge. Using the query method, we are able to select rows
where the _merge value is not both and then drop the _merge column. This
can be done all in one line as shown at the end of Listing 2-13 but is broken up
into two steps beforehand so you can see how it works underneath.
Listing 2-13. Eliminating patients who participated in both trials
using anti-join merge method
>> import pandas as pd
>> trial_a_records
name
patient
230858 John
237340 May
240932 Catherine
124093 Ahmed
Figure 2-4. Venn diagram of anti-join
Chapter 2 Basi C Data aCC ess an D Merging

24
>> trial_b_records
name
patient
210858 Abi
237340 May
240932 Catherine
154093 Julia
>> both_trials = pd.merge(
trial_a_records,
trial_b_records,
how='outer',
indicator=True,
right_index=True,
left_index=True,
on="name",
)
name _merge
patient
230858 John left_only
237340 May both
240932 Catherine both
124093 Ahmed left_only
210858 Abi right_only
154093 Julia right_only
>> both_trials.query('_merge != "both"').drop('_merge', 1)
name
patient
230858 John
124093 Ahmed
210858 Abi
154093 Julia
Chapter 2 Basi C Data aCC ess an D Merging

25
>> both_trials = pd.merge(
trial_a_records,
trial_b_records,
how='outer',
indicator=True,
right_index=True,
left_index=True,
on="name",
).query('_merge != "both"').drop('_merge', 1)
Combining DataFrames using the join
method
The pandas join method is just a wrapper around merge, and it provides
the same basic merging methods: left, right, outer, and inner. It allows you
to perform merge operations on multi-index DataFrames automatically
without needing to specify the indexes to merge on. When doing a left join,
it automatically uses the indexes from the left DataFrame to join on, and the
same is true for the right DataFrame. Since join is performed on a DataFrame as
opposed to merge where you pass in both DataFrames explicitly, join’s default is
to merge on the “right” DataFrame’s indexes. In this case, “right” is the passed in
DataFrame. This is in opposition to merge’s default which is an inner join.
Using merge, it is possible to not specify an explicit key to merge on.
In cases where you are merging two DataFrames with the same data and
do not wish to have duplicated columns for the left and right DataFrames
but simply merge the two data sets together, merge is preferable over
join. Because join calls merge underneath, it explicitly specifies the keys
to merge on, thus eliminating the possibility of not outputting duplicated
columns for DataFrames that share common column names. A basic rule
to follow here is use merge if you are not joining on the index.
Chapter 2 Basi C Data aCC ess an D Merging

26
Listing 2-14 plays off of a previous inner merge example in Listing 2-10 , but
unlike the previous example where the records of the buildings in common
matched, this time there are discrepancies. A join is desirable for a couple
reasons in this scenario. Firstly, the data has already been indexed according
to the unique building and join will automatically pick up the indexes and use
those to join the two sets of data. Secondly, there are discrepancies in the data,
and thus we wish to see columns from both DataFrames side by side in the
output DataFrame so we can compare them.
Listing 2-14. Highlighting discrepancies in established date of 1844
buildings that are still standing in 2020 using an inner join
>> import pandas as pd
>> building_records_1844
established
building location
Grande Hotel (4,5) 1831
Jone 's Farm (1,2) 1842
Public Library (6,4) 1836
Marietta House (1,7) 1823
>> building_records_2020
established
building location
Sam 's Bakery (5,1) 1962
Grande Hotel (4,5) 1830
Public Library (6,4) 1835
Mayberry 's Factory (3,2) 1924
>> building_records_1844.join(
building_records_2020,
how='inner',
rsuffix="_2000",
)
Chapter 2 Basi C Data aCC ess an D Merging

27
established established_2000
building location
Grande Hotel (4,5) 1831 1830
Public Library (6,4) 1836 1835
Combining DataFrames using the concat
method
Concatenate is a simple way of combining two DataFrames together.
Listing 2-15 demonstrates a simple concatenation of the same data from
multiple sources. Concatenate has many options including the option join
which specifies whether to use an outer or inner merge and axis which
specifies whether to merge across columns with axis=1 or rows with axis=0.
By default, concatenate performs an outer merge across rows. Note in
Listing 2-15 , location (6,4) is present in both county_a and county_b data,
and in the concatenated result, it is repeated in the index.
Listing 2-15. Joining two DataFrames together using concat
>> import pandas as pd
>> temp_county_a
temp
location
(4,5) 35.6
(1,2) 37.4
(6,4) 36.3
(1,7) 40.2
Chapter 2 Basi C Data aCC ess an D Merging

28
>> temp_county_b
temp
location
(6,4) 34.2
(0,4) 33.7
(3,8) 38.1
(1,5) 37.0
>> pd.concat([temp_county_a, temp_county_b])
temp
location
(4,5) 35.6
(1,2) 37.4
(6,4) 36.3
(1,7) 40.2
(6,4) 34.2
(0,4) 33.7
(3,8) 38.1
(1,5) 37.0
Concatenate can also be used in a more complicated manner to create
multi-level columns or indexes. Listing 2-16 demonstrates a concatenation
where each DataFrame being concatenated is a value in a multi-level
column. Note in Listing 2-16 , device_a and device_b data are temperature
measurements at the same locations. Here we specify axis=1 so that the
two DataFrames are outer merged across the columns. The key parameter
in Listing 2-16 tells concatenate to treat the two temperature columns
as different columns even though they are named the same and as a bi-
product creates a multi-level column. Performing an outer merge across the
columns and putting the temperatures of each device into separate columns
means the concatenated result has no repeated location index values. This is
in opposition to Listing 2-15 where the resulting index values were repeated
and the two DataFrames were simply stacked on top of each other.
Chapter 2 Basi C Data aCC ess an D Merging

29
Listing 2-16. Joining two DataFrames together using a multi- level
column concat
>> import pandas as pd
>> temp_device_a
temp
location
(4,5) 35.6
(1,2) 37.4
(6,4) 36.3
(1,7) 40.2
>> temp_device_b
temp
location
(4,5) 34.2
(1,2) 36.7
(6,4) 37.1
(1,7) 39.0
>> pd.concat(
[temp_device_a, temp_device_b],
keys=["device_a", "device_b"],
axis=1,
)
device_a device_b
temp temp
location
(4,5) 35.6 34.2
(1,2) 37.4 36.7
(6,4) 36.3 37.1
(1,7) 40.2 39.0
Chapter 2 Basi C Data aCC ess an D Merging

30
There are many different ways to combine DataFrames together
and extract a sub-DataFrame in pandas. Which method you use really
depends on your particular use case. The examples presented here are a
representative sample, but you should consult the documentation of each
method as there are some parameters that were not explicitly covered in
this chapter, such as sorting. 1
1 https://pandas.pydata.org/pandas-docs/version/0.25/user_guide/
merging.html
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31 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_3
CHAPTER 3
How pandas Works
Under the Hood
As with any program language, it’s important to understand what is
going on underneath because it helps you write more explicit, simpler,
performant, and correct code. The building blocks of a language (its data
structures and API) when used correctly can make an operation trivial
and when used incorrectly can make an operation overly complex if not
impossible. Python packages are no different.
A programming language is simply text that is easily readable and writable
by humans that can be translated into CPU instructions that are understood
by machines. As programming languages have become increasingly high
level (farther removed from the machine code that computers understand),
the necessity for developers to understand the translation has become less
essential. A byproduct of this, however, is that software can be written in a
non-performant and atypical way without developers being forced to address
the underlying issues. Non-performant solutions on modern computing
platforms typically are not visibly non- performant until they are scaled
to handle much more data. Big data software typically operates at a scale
where the performance impact is visible because it processes huge data sets,
often repeating a small quick operation so many times that its performance
becomes significant. When working at this scale, it’s important to understand
the data structures and performance optimizations available to you, in
order to get the most out of your machine with the least amount of effort.
This starts with understanding the performance of data structures in the
language you are using.

32
Python data structures
Python’s data structures are its building blocks. Choosing the right data
structure for the problem you are trying to solve is essential to writing
correct and performant code.
First, we’ll look at tuples. They are in many ways comparable to
a C array and in fact are an array underneath. They are an iterable,
meaning you can loop over them and look at each value, though they are
immutable, meaning the values cannot be changed once a tuple has been
created. They are great at storing static chunks of related information such
as metadata. When a small tuple is no longer referenced in a program and
its memory can be freed, Python keeps it around and adds it to the tuple
free list so that it can be used again. This saves time in the interpreter as
it does not have to re-allocate the memory for a new tuple. Underneath,
tuples translate into a fixed-size array, meaning an array of pointers
whose size cannot be changed. Listing 3-1 shows a code example of a
tuple, and Figure  3-1 illustrates its representation in memory. Each index
is represented as a memory addresses 0x0000 0FB0 8421 0000 through
0002 in Listing 3-1 . The value at each index is a memory address or pointer
to the actual value in memory. This is how Python is able to store non-like
types into the same underlying array object. Each address or index of the
tuple contains a pointer and only has to make room for the pointer rather
than the actual value.
Listing 3-1. Example tuple
person_info = ("Sara", 140, 5.7)
Chapter 3 how pandas works Under the  hood

33
A list, simply put, is a mutable tuple. A list is an array of a fixed size
underneath, but when the number of elements exceeds the size that Python
originally allocated, it creates a new fixed-size array with space for more
elements and copies the elements from the old array into the new array.
The allocated size is base 2, so if you initialize a list with five values,
underneath Python will allocate the fixed-size array to hold eight references.
If you then append four more values, on the fourth append, the fixed-size
array will be re-allocated to be double the size (16) and the previous value
references will be copied into the new array including the new fourth value
reference that didn’t fit in the previous size 8 array. Unlike tuples, they do not
have any behind-the-scenes performance optimizations for reusing freed
memory. This is due to the fact that lists are mutable—meaning their values
can be changed after creation and they are not of fixed size. Listing 3-2 shows
an example of a list, and Figure  3-2 illustrates its representation in memory.
Just like a tuple, the list contains references to the values rather than the
values themselves. Note since the list was initialized with three values, the
fixed-size array underneath is of length 4, so at index 3 (element 4), there is an
empty placeholder value of 0x0.
Listing 3-2. Example list
people = ["Sara", "Sam", "Joe"]
Figure 3-1. A representation of Listing 3-1 in memory
Chapter 3 how pandas works Under the  hood

34
A dictionary is a hash table. The keys are hashed to a memory address
or a particular index in an array. Depending on the number of keys in the
dictionary, a certain number of bits of the hash are used to determine
the index. Listing 3-3 shows an example dictionary and illustrates its
representation in memory. In the example in Listing 3-3 , 2 bits are used as
there are only two elements. The values in the hashed array are the indexes
into a second array that contains the complete hash, the key, and the value
of the key in the dictionary. Note each element in the hashed index array
only takes up 64 bytes (the size of a pointer) compared to the array of data
which takes up much more than that because it includes the hash, the key,
and the value. By keeping a separate array of indexes to the data array,
the dictionary implementation is able to save space in memory by using a
smaller hashed index array to be a placeholder for the unused keys rather
than the larger data array. It is also able to grow the hashed index array
as it needs to, similar to how a list grows as more elements are inserted,
as opposed to allocating a bunch of memory for hash indexes that don’t
exist. Note the hashed index array can be completely re-initialized when
the number of hashed bits increases, independent of the data array. Also
note because of this implementation, the dictionary keys are now sorted by
insertion time in the data array, and as of Python 3.7, dictionary keys are
now guaranteed to be in insertion order.
Figure 3-2. A representation of Listing 3-2 in memory
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35
Listing 3-3. Example dictionary and its representation in memory
word_alphabet = {"a": "apple", "b": "banana"}
Hash-Index Data
None hash("a"), "a", "apple"
0 hash("b"), "b", " banana"
1
None
Sets are basically the same implementation as dictionaries but without a
value. They are a data structure for tracking membership and perform almost
all the operations in mathematical set theory such as union and intersection.
Listing 3-4 shows an example set and how it is represented in memory.
Listing 3-4. Example set and its representation in memory
alphabet = {"a", "b"}
Hash-Index Data
None hash("a"), "a"
0 hash("b"), "b"
1
None
There are also many other data structures including integers, floats,
Booleans, and strings. These pretty much directly translate into their
c-type equivalents underneath and aren’t really worth going over here.
Something that is worth mentioning though is some of these have special
built-in caching in Python.
Python has a string and integer cache. Take, for example, str1 and
str2 in Listing 3-5 . They are both set to the value “foo” but underneath
they are pointing at the same memory location. This means that rather
than creating a new string that is an exact copy of str1 and duplicating the
memory, the new string will simply point to the existing string value. This
Chapter 3 how pandas works Under the  hood

36
is demonstrated here by the assertion line where the “is” property is used
to compare the references or pointers of the two strings for equality.
Listing 3-5. str1 and str2 are pointing to the same memory location
str1 = "foo"
str2 = "foo"
assert(str1 is str2)
The string cache, however, only works on strings containing letters,
numbers, and underscore. This is advantageous to know when working
with large data sets that may contain other letters. You can save a lot of
memory by eliminating characters from string values in the data set that
prevent string caching. See Listing 3-6 .
Listing 3-6. str1 and str2 are not pointing to the same memory
location
str1 = "foo bar"
str2 = "foo bar"
assert(str1 is not str2)
The integer cache works in a similar way; it only caches integers
between and including –5 and 256. See Listing 3-7 .
Listing 3-7. in t1 and int2 are pointing to the same memory location
but int3 and int4 are not
int1 = 22
int2 = 22
int3 = 257
int4 = 257
assert(int1 is int2)
assert(int3 is not int4)
Chapter 3 how pandas works Under the  hood

37
Again, this can be advantageous to know as breaking a single column
of large numbers into two columns representing the number in scientific
notation, for example, may lead to memory savings.
The performance of the CPython interpreter,
Python, and NumPy
The Python interpreter that most developers typically install is called
CPython. It is the interpreter that is recommended for use with pandas as
pandas is highly dependent on C for performance optimizations. CPython
is implemented in C and translates Python code into what’s called bytecode,
an intermediate low-level format that is run on the Python Virtual Machine.
There are many different Python interpreters including Jython, IronPython,
and PyPy which are implemented in Java, C#, and RPython (a restricted
subset of Python), respectively. PyPy is a Just-In-Time or JIT compiler, which
means it compiles the Python code into machine code as it runs. This is
in opposition to CPython that runs the bytecode on the Python Virtual
Machine and calls into pre-compiled C extensions. PyPy is generally faster
than CPython because it runs low- level optimized machine code as opposed
to parsing the bytecode on the Python Virtual Machine. Unfortunately, PyPy
does not fully support pandas at this time.
Python is a high-level language, which means it’s easy to read and fast
to implement. This also means it is slow compared to some lower-level
languages because of all these self-managing niceties. These niceties include
the garbage collector, the global interpreter lock, and dynamic typing, but
they don’t come for free. The garbage collector is responsible for freeing
memory that is no longer in use so that it can be used again. The global
interpreter lock, also known as the GIL, protects objects from being accessed
by multiple threads at the same time. Dynamic typing allows the same
variable to hold different types of values. Because CPython is implemented
in C, it is C compatible and thus allows Python to call into more performant
Chapter 3 how pandas works Under the  hood

38
extensions written in C. But why is C so much more performant than
Python? There are a several reasons why Python is relatively slower than C; it
is interpreted rather than compiled, it has a global interpreter lock, it allows
dynamic typing, and it has a built-in garbage collector.
Interpreting Python into bytecode is like compiling C into object files
only bytecode is run on the Python Virtual Machine and machine code
is run on the CPU. The interpretation of the Python code at runtime adds
extra overhead that makes Python generally run slower than C. There are
several phases of interpretation: a tokenizer that converts Python code
to a token stream, a lexical analyzer that runs syntax analysis, a bytecode
generator that optimizes and converts the Python code to bytecode (.pyc
files), and the bytecode interpreter that interprets the bytecode stream and
maintains the state of the bytecode interpreter.
If you’ve ever edited a Python file and re-run your program only to notice
that it didn’t run with the change you just made, you’ll understand that the
Python interpreter caches the bytecode in .pyc files. Deleting the .pyc files
before re-running forces the interpreter to re-interpret the Python code into
bytecode—in essence, forcing the Python interpreter to clear its bytecode
cache. The bytecode cache decreases the overhead of runtime interpretation
by not re-interpreting the Python code into bytecode unless the Python code
has changed. On versions of Python that pre-dated 3.3, however, the method
used to obtain the timestamp on the .py file did not match the windows
operating system timestamp which resulted in a timestamp that was younger
than the .pyc file. This meant that the interpreter did not re-interpret the
.py file into bytecode. Similarly, if you remove a .py file but still import it in
your code, the imports may continue to work because the .pyc file is still
present on your system. While these two issues may sway you from wanting
to use the bytecode cache, the cache plays an important role in improving
interpretation performance and typically operates in the background without
developers being the wiser. The bytecode cache is particularly advantageous
for third-party libraries where the code is installed and not expected to
change or the library owners do not want to expose the Python source code.
Chapter 3 how pandas works Under the  hood

39
Python has a global interpreter lock or what’s known to most as the
GIL. In order to understand why the GIL exists, you really have to take a
step back in time and explore what was happening in computer science
at the time the GIL was invented. It began with the invention of threads.
Anticipating the future of computing, software introduced the concept of
multi-threading prior to multi-core CPUs. Threading enabled a program to
run processes in parallel that operated on the same memory space. It was a
fantastic way to improve performance of CPU-intensive computation.
For example, say we want to calculate how many times the name Tiffany
appears in a list. You could do this by counting how many times Tiffany
appears in the list all by yourself, or you could break up the list into sub-
lists and give one to each of your friends, and each time one of you sees
the word Tiffany, increase the running total on the whiteboard by one. In
this example, you and your friends are the threads and the count on the
whiteboard is the shared memory. Generally, breaking up the problem
into smaller chunks and using threads to parallelize the computation is
faster than computing the whole thing on one thread. The problem you
may encounter here is when one of your friends has erased the total on
the whiteboard and is updating it at the same time you wish to update it.
Fortunately, you are smart enough to realize this is happening and wait until
your friend has finish updating the total before you try to do so. Computers,
on the other hand, need to be told how to handle this or need to be what’s
called thread safe. If a piece of software encounters this same scenario, it’s
going to simply squash the value. This is what’s known as a race condition.
In Figure  3-3 , time is represented on the y axis as t, and there are two
threads that each wish to increment the total at nearly the same time.
Total in this example is located in shared memory, meaning both threads
can access that value. Here you can see that Thread 1 increments the
counter first, followed by Thread 2. However, Thread 2 effectively does
not increment the counter because at the time that it grabbed the total to
increment, Thread 1’s increment had not taken effect yet. This has an end
result of the total being one less than it should be (6 instead of 7).
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Similar to this running total example, the CPython interpreter uses a
reference garbage collector technique where it keeps track of the number
of places that refer to an object. The garbage collector is responsible for
keeping track of allocated memory and deallocating it when it is no longer
used. It does this by keeping a running total of all the places that reference
each object in the program. When there are no more references, meaning
the reference count is zero, then the memory is deallocated, meaning
it is freed and available to store something else. In Listing 3-8 , string
foo’s reference count would be two because there are two variables that
reference it.
Thread 1
total = 5
Thread 2 Shared Memory
t=0s
Thread 1
total = 6 total = 6
Thread 2 Shared Memory
t=3s
Thread 1
total = 5 total = total + 1
total = total + 1
Thread 2 Shared Memory
t=1s
Thread 1
total = 5 total = 6
Thread 2 Shared Memory
t=2s
Thread 1
total = 6
Thread 2 Shared Memory
t=4s
Figure 3-3. A demon stration of a race condition on increment of
total between Thread 1 and Thread 2
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Listing 3-8. Example of creating two references to the string foo
ref1 = "foo"
ref2 = "foo"
Recall that the string cache is at play here, and because of that, both
ref1 and ref2 point to the same value underneath.
When we delete ref2, string foo’s reference count is 1, and when we
delete ref1, string foo’s reference count is 0 and the memory can be freed.
This is demonstrated in Listing 3-9 .
Listing 3-9. Example of deleting references to foo that were created
in Listing 3-8
delete(ref2) # reference count = 1 after this line executes
delete(ref1) # reference count = 0 after this line executes
Not all objects are freed when their references reach 0 though because
some never reach 0. Take, for example, the scenario presented in Listing 3-10
which tends to happen quite often when working with classes and objects in
Python. In this scenario, exec_info is a tuple and the value at the third index is
the traceback object. The traceback object contains a reference to the frame,
but the frame also contains a reference to the exc_info variable. This is what’s
known as a circular reference, and since there is no way to delete one without
breaking the other, these two objects must be garbage collected. Periodically
the garbage collector will run, identify, and delete circular referenced objects
like this.
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Listing 3-10. Example of creating a circular reference
import sys
try:
raise Exception("Something went wrong.")
except Exception as e:
exc_info = sys.exc_info()
frame = exc_info[2].tb_frame # create a third reference
assert(sys.getrefcount(frame) == 3)
del(exc_info)
assert(sys.getrefcount(frame) == 3)
Keeping track of these references does not come for free. Each object has
an associated reference counter which takes up space, and each reference
made in the code takes up CPU cycles to compute the appropriate increment
or decrement of the object’s reference count. This is partially why, if you
compare the size of an object in Python to the size of an object in C, the sizes
are so much larger in Python and also why Python is slower to execute than C. 
Part of those extra bytes and extra CPU cycles are due to the reference count
tracking. While the garbage collector does have performance implications, it
also makes Python a simple language to program in. As a developer, you don’t
have to worry about keeping track of memory allocation and deallocation; the
Python garbage collector does that for you.
In a multi-threaded application, reference counts have the same
problem as the total has in Figure  3-3 . A thread may create a new reference
to an object in the shared memory space at the same time as another thread
and a race condition occurs where the reference count ends up only being
incremented once instead of twice. When this happens, it can ultimately
lead to the object being freed from memory before it should be (because
the race condition leads to the object’s reference count being incremented
by one instead of two). In other cases when there is a race condition on
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a decrement of the reference counter, this can lead to a memory leak
where the memory is never destroyed because the reference count is one
larger than it should be. So, as you can see here, running a multi-threaded
application can not only have consequences on the data that the program is
operating on but also within the Python interpreter itself.
Traditionally, race conditions are solved through locks. This is effectively
what you were doing subconsciously—that is, waiting for your friend to finish
updating the total before you updated it yourself. In software, this is done
through a shared memory lock. When a thread needs to update the total, it
acquires the lock, updates the total, and then releases the lock. Meanwhile,
the other thread waits until the lock is free, acquires the lock, and then also
updates the total. This interaction is demonstrated in Figure  3-4 .
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Thread 1
total = 5
Thread 2 Shared Memor y
t=0s
Thread 1
total = 6
Thread 2 Shared Memor y
t=5s
Thread 1
total = 6
Thread 2 Shared Memor y
t=6s
Thread 1
total = 5 total = total + 1 total = total + 1
Thread 2 Shared Memor y
t=2s
Thread 1
total = 6
Thread 2 Shared Memor y
t=7s
Thread 1
total = 5 total = 7
Thread 2 Shared Memor y
t=1s
Thread 1
total = 7
Thread 2 Shared Memor y
t=8s
Thread 1
total = 5 total = 6
Thread 2 Shared Memor y
t=3s
Thread 1
total = 6
Thread 2 Shared Memor y
t=4s
Figure 3-4. A demonstration of a lock on total shared between
Thread 1 and Thread 2
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This is great! We’ve solved the problem! Or have we? Consider the
scenario in Figure  3-5 where there are instead two locks and two totals.
Thread 1
total1 = 5
total2 = 4
Thread 2 Shared Memory
t=0s
Thread 1
total1 = 5
total2 = 4
Thread 2 Shared Memory
t=1s
Thread 1
total1 = 5
total2 = 4
Thread 2 Shared Memory
t=2s x
x
Thread 1
total1 = 5
total2 = 4
Thread 2 Shared Memory
t=3s x
x
Thread 1
total1 = 5
total2 = 4
Thread 2 Shared Memory
t=4s x
x
Figure 3-5. A demon stration of a deadlock between Thread 1 and Thread 2
Figure 3-5 is what’s known as deadlock. This happens when two threads
require multiple pieces of data to execute, but they request them in different
orders. In order to avoid these kinds of issues altogether, the author of
Python implemented a lock at the thread level which only allowed one
thread to run at any given time. This was a simple and elegant way to solve
this problem. At the time, since multi-core CPUs were quite uncommon,
it didn’t really impact performance since in the CPU, these threads’
instructions would be run serially anyway. However, as computers have
become more advanced and computations have become more intensive,
multi-core CPUs have become the standard in pretty much all modern
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computing platforms. When it comes to big data which involves running
large CPU-intensive computations, not taking advantage of multi- core CPUs
means in some cases not being able to run the computation at all (at least
within a reasonable amount of time). So how can we break out of the GIL
and truly run a multi-core big data computation?
C extensions, being written in C and not in Python, are not subject to
the GIL. pandas is built on NumPy which is a Python wrapper around C
extensions that do all the heavy lifting for the computation. This means
that all the intensive computation when using pandas is done in C, where
the computations are just generally faster due to the properties of the
language. This also means that pandas is able to break out of the GIL and
truly run multi-core computation on multiple cores simultaneously.
Since NumPy runs the computations in C, it must translate all the Python
objects into C-compatible types. According to the NumPy documentation, as
long as an array’s types are translatable to C types, the GIL is released prior to
the calculation. This means that when using NumPy, it’s important to operate
on types that are translatable to C types. If you operate on Python objects that
NumPy is unable to translate to C, the operation cannot compute the result in
C. It must instead stay in Python where the GIL cannot be released until the
computation has finished.
The Appendix shows a detailed mapping of the types in Python to the
types NumPy uses in its C extensions known as scalars. All of the NumPy
types are referred to as dtypes within the NumPy API and are available as
attributes of the NumPy library.
NumPy’s main data structure is an N-dimensional array or ndarray.
It is a special array structure that handles this Python-C boundary. It’s
important to note that ndarrays in NumPy are homogeneous, meaning all
the elements have the same type. This again has to do with Python vs. C. C
is a lower-level language where the developer must manage the memory
allocation and deallocation themselves, and it does not permit dynamic
typing. In the case of an array in C, all the elements must be of consistent
and known size or type in order to allocate the appropriate amount of
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memory for the input data arrays and the output data array in C. 
Listing 3-11 shows an example of how an array of floats is created in
C. Note the memory must be explicitly allocated using malloc, and it is of
fixed size 100—having only enough room for 100 floats.
Listing 3-11. Allocating memory for an array of 100 floats in C
float * array = (float *) malloc(100 * sizeof(float));
NumPy uses ndarray as shown in Listing 3-12 to build a fixed-size
array where dtype is used to specify the type of each element in the array
or how much memory each element in the array will take up. Recall from
the previous section that Python’s list implementation is a dynamic array
underneath. Python’s list type, in contrast to an ndarray, handles elements
of any size by allocating space for the pointer to that data as opposed to
the data itself. This leads to a Python list taking up more memory than an
ndarray because there is a layer of indirection due to the list holding the
pointers to the data rather than the data itself. In summary, ndarrays have a
fixed length and each element has the same type, whereas Python’s list type
has a dynamic length and elements can be many different types. This is an
important distinction and why each column in pandas is assigned a particular
dtype. This is also why it’s important to make sure pandas has the correct type
for a particular column and why it’s important to normalize your data so that
a type other than the all-encompassing object type is assigned. Figure  3-6
shows how the ndarray created in Listing 3-12 is represented in memory.
Figure 3-6. A representation of Listing 3-12  in memory. Compare this
to Figure  3-2
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Listing 3-12. ndarray of three unsigned 8-bit ints
import numpy as np
groups_waiting_for_a_table = np.ndarray(
(3,0),
buffer=np.array([4, 7, 21], dtype=np.uint8),
dtype=np.uint8,
)
The CPython interpreter provides a C-API that exposes the ability
to acquire and release the GIL. NumPy uses the macros NPY_BEGIN_
THREADS and NPY_END_THREADS to denote when C code is able to
run without the GIL. NumPy mathematical operations are instances of
universal functions, known as ufunc’s, implemented in C, and all of them
call these macros. See the Appendix for a list of common ufuncs.
Recall that running without the GIL means that the program can now
execute instructions on multiple cores simultaneously. This means that
intensive mathematical operations when using NumPy are able to break
up the example of counting how many times Tiffany appears in a list,
using multi-threading at the C level. While NumPy itself doesn’t typically
implement multi-threaded computations (simply running the computation
in C is enough of a performance improvement itself ), there are other
libraries which will do so when used in combination with NumPy.
When NumPy is compiled to use the Basic Linear Algebra Subroutines,
known as BLAS, or a Linear Algebra Package, known as LAPACK, it runs
operations according to the size of the memory cache and the number
of cores available on the system. By optimizing the calculation according
to the resources on the machine, NumPy is able to run the computations
much faster than it otherwise would. There are several different
implementations of BLAS/LAPACK including OpenBLAS, ATLAS, and Intel
MKL. We’ll explore how these libraries work to improve performance in
more detail in later chapters, but for now, just know they exist and for large
computations they can make a huge difference in performance.
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An introduction to pandas performance
pandas is a wrapper around NumPy and NumPy is a wrapper around
C; thus, pandas gets its performance from running things in C and not
in Python. This concept is fundamental to everything you do in pandas.
When you are in C, you are fast, and when you are in Python, you are slow. 1
The same requirements present for working with NumPy arrays hold
true when working with pandas DataFrames—namely, the Python code
must be translatable to C code; this includes the types that hold the data
and the operations performed on the data. Table  3-1 is a table of pandas
types to NumPy types. Note that datetimes and timedeltas don’t translate
into NumPy types. This is because C does not have a datetime data
structure, and so in cases where operations must be made on datetime
data, it is more performant to, instead, convert the datetimes to an integer
type of seconds since the epoch.
1 www.youtube.com/watch?v=ObUcgEO4N8w
Table 3-1. pandas to NumPy types
pandas type NumPy type
object string_, unicode_
int64 int_, int8, int16, int32, int64, uint8, uint16, uint32,
uint64
float64 float_, float16, float32, float64
bool bool_
datetime64 datetime64[ns]
timedelta[ns] na
category na
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Note that category is also not translatable into C. category is similar to a
tuple in that it is intended to hold a collection of categorical variables, meaning
metadata with a fixed unique set of values. Because it’s not translatable into C,
it should never be used to hold data that needs to be analyzed. Its advantage
mainly comes in its ability to sort things in a custom sort order efficiently and
simply. Underneath it looks like a data array of indexes where the indexes
correspond to a unique value in an array of categories. The documentation
claims that it can result in a huge memory savings when using string
categories. Of course, we know from the previous section that Python already
has a built-in string cache that does that for us automatically for certain strings
so this would really only make a difference if the strings contained characters
other than alphanumeric and underscore. Listing 3-13 shows an example
of a category and its representation in memory. Note that it uses integers to
represent the value and those integers map to an index in the category array.
This is a common method of conserving memory in pandas. We’ll run into
this again later when we look at multi-indexing.
Listing 3-13. pandas category example and its representation in
memory
import pandas as pd
produ ce = pd.Series(
["apple", "banana", "carrot", "apple"], dtype="category"
)
Data Categories
0 apple
1 banana
2 carrot
0
Operations must also be translatable into C in order to take advantage
of NumPy’s performance optimizations. This means custom functions
like the one in Listing 3-14 will not be performant because they will run
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51
in Python and not in C. We’ll dig more into this example and the apply
function specifically in Chapter 6.
Listing 3-14. pandas custom Python operation that isn’t
translatable to C
import pandas as pd
def grade(values):
if 70 <= values["score"] < 80:
values["score"] = "C"
elif 80 <= values["score"] < 90:
values["score"] = "B"
elif 90 <= values["score"]:
values["score"] = "A"
else:
values["score"] = "F"
return values
scores = pd.DataFrame(
{"score": [89, 70, 71, 65, 30, 93, 100, 75]}
)
scores.apply(grade, axis=1)
Since pandas is built on NumPy, it uses NumPy arrays as the building
blocks for a pandas DataFrame, which ultimately translate into ndarrays
deep down during computations.
Listing 3-15. pandas single-index DataFrame and its representation
in memory
import pandas as pd
restaurant_inspections = pd.DataFrame({
"restaurant": ["Diner","Diner","Pandas","Pandas"],
"location": [(4,2),(4,2),(5,4),(5,4)],
"date": ["02/18","05/18","04/18","01/18"],
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"score": [90,100,55,60]})
>> restaurant_inspections
restaurant location date score
Diner (4, 2) 02/18 90
Diner (4, 2) 05/18 100
Pandas (5, 4) 04/18 55
Pandas (5, 4) 01/18 76
Index Blocks
restaurant Diner Diner Pandas Pandas
location (4, 2) (4, 2) (5, 4) (5, 4)
date 02/18 05/18 04/18 01/18
score 90 100 55 76
Listing 3-15 is an example of the simplest form of a pandas DataFrame.
The data is restaurant health inspection data. It has four columns:
restaurant, location, date, and score. Each column has four rows worth
of data. Note that some of the data is repeated as there can be multiple
inspections of the same restaurant over time. Underneath, this DataFrame is
represented as a NumPy array called Index that contains the column names
and a two-dimensional NumPy array called Blocks that contains the data.
This same data could be represented in a more expressive way using a
multi-index DataFrame, where each index is a unique restaurant. This is
done in two parts. First, we create the index, then the index is attached to
the data as shown in Listing 3-16 . The data is represented the same as in the
previous example, but note there are only two data columns instead of four.
Listing 3-16. pandas multi-index DataFrame and its representation
in memory
import pandas as pd
restaurants = pd.MultiIndex.from_tuples(
(
("Diner", (4,2)),
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53
("Diner", (4,2)),
("Pandas", (5,4)),
("Pandas", (5,4)),
),
names = ["restaurant", "location"]
)
restaurant_inspections = pd.DataFrame(
{
"date": ["02/18", "05/18", "04/18", "01/18"],
"score": [90, 100, 55, 76],
},
index=restaurants,
)
>> restaurant_inspections
date score
restaurant location
Diner (4, 2) 02/18 90
05/18 100
Pandas (5, 4) 04/18 55
01/18 76
Levels Names Labels
restaurant Diner Pandas 0 0
location (4, 2) (5, 4) 0 0
1 1
1 1
Index Blocks
date 02/18 05/18 04/18 01/18
score 90 100 55 76
Something special happens when we create a multi-index. Underneath
the index doesn’t look the same as it did in the single-index example.
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There is still a NumPy array called Levels that holds the index names;
however, instead of a simple two-dimensional NumPy array of data, the
data undergoes a form of compression. The Names is a two-dimensional
NumPy array that keeps track of the unique values within the index, and
Labels is a two-dimensional NumPy array of integers whose values are the
indexes of the unique index values in the Names NumPy array. This is the
same memory saving technique used by the pandas category data type,
and in fact, since category came later, they probably copied this technique
from the pandas multi-index.
The DataFrame in Listing 3-16 ends up being about two-thirds the size
of the single-index DataFrame in Listing 3-15 due to the data compression
incurred by the use of the multi-index. pandas is able to save memory by
using an integer type instead of another larger type to keep track of and
represent index data. This of course is advantageous when there is a lot
of repeated data in the index and less advantageous when there is little to
no repeated data in the index. This is also why it is important to normalize
the data. If, for example, there were multiple representations for the same
restaurant name (DINER, Diner, diner), we would not be able to take
advantage of the compression as we have done here. We would also not be
able to take as large of an advantage of the Python string cache either.
Similar to multi-level indexes, pandas also permits multi-level columns.
The multi-level columns are implemented the same as the multi- level
indexes with the same data compression technique. Listing 3-17 shows an
example of how to create a multi-index multi-level column DataFrame.
Listing 3-17. pandas multi-index multi-level column DataFrame
import pandas as pd
restaurants = pd.MultiIndex.from_tuples(
(
("Diner", (4,2)),
("Pandas", (5,4)),
),
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55
names = ["restaurant", "location"]
)
inspections = pd.MultiIndex.from_tuples(
(
(0, "score"),
(0, "date"),
(1, "score"),
(1, "date"),
),
names=["inspection", None],
)
restaurant_inspections = pd.DataFrame(
[[90, "02/18", 100, "05/18"], [55, "04/18", 76, "01/18"]],
index=restaurants,
columns=inspections,
)
>> restaurant_inspections
inspection 0 1
score date score date
restaurant location
Diner (4, 2) 90 02/18 100 05/18
Pandas (5, 4) 55 04/18 76 01/18
Choosing the right DataFrame
Choosing the orientation of a pandas DataFrame is a decision that takes a
lot of consideration and planning. Considerations include
• What kind of data processing will you be doing with the
data?
• Do you need to run aggregated calculations over the
data or group it?
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56
• Ar e all the data types translatable to C types and what
can you do to make them so?
• Can you separate the data from the metadata?
• Is there a particular DataFrame orientation(s)
that would make the processing simpler and more
performant?
Consider the following example of health inspection data. Each
restaurant can have multiple inspections, and as part of the data
processing, we would like to count how many inspections each restaurant
has had.
The simplest form of a pandas DataFrame looks like the DataFrame
in Listing 3-18 . In order to calculate the number of inspections, the data
must be aggregated uniquely by restaurant and then the number of
inspections for each restaurant must be counted. This DataFrame takes up
approximately 1,120 bits underneath.
Listing 3-18. Storing and operating on restaurant health inspection
data in a single-index DataFrame
import pandas as pd
restaurant_inspections = pd.DataFrame({
"restaurant": ["Diner","Diner","Pandas","Pandas"],
"location": [(4,2),(4,2),(5,4),(5,4)],
"date": ["02/18","05/18","02/18","05/18"],
"score": [90,100,55,60]})
>> restaurant_inspections
restaurant location date score
Diner (4, 2) 02/18 90
Diner (4, 2) 05/18 100
Pandas (5, 4) 02/18 55
Pandas (5, 4) 05/18 76
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57
>> total_inspections = restaurant_inspections.groupby(
["restaurant", "location"], as_index=False,
)["score"].count()
>> to tal_inspections.rename(
columns={"score": "total"}, inplace=True
)
>> total_inspections
restaurant location total
Diner (4, 2) 2
Diner (4, 2) 2
>> restaurant_inspections = pd.merge(
restaurant_inspections,
total_inspections,
how="outer",
)
>> restaurant_inspections
restaurant location date score total
Diner (4, 2) 02/18 90 2
Diner (4, 2) 05/18 100 2
Pandas (5, 4) 02/18 55 2
Pandas (5, 4) 05/18 76 2
Using a single-index DataFrame is less than ideal for this type of
calculation for several reasons. First, we need to run an aggregated
calculation so we need to group the data by unique restaurant. This grouping
can be quite time-consuming if there are many groups. After running the
calculation on each group, you’ll notice the resulting total_inspections is not
the same dimensions as the original restaurant_inspections DataFrame. The
dimension mismatch requires us to do some finagling to get the new data
back into the original DataFrame. We end up using a merge to do it which
builds an entirely new DataFrame. This means we will be doubling our
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58
memory during the merge, and if the original DataFrame is very large, this
could cause a slowdown or even a memory crash if we are very close to our
max memory usage.
If instead we represent the data as a multi-index DataFrame as shown
in Listing 3-19 , the data is already grouped uniquely by restaurant. This
means the groupby will be faster since the data is already grouped in the
index. It also means the DataFrame will take up less memory since, as you
recall from the previous section, the data in the index is compressed. Most
significantly, however, we don’t have to do the kind of finagling that we
had to do when using a single-index DataFrame. We are able to run the
calculation and put it back into the original DataFrame without creating a
copy which is a huge time and memory saver. The code you’ll notice is also
simpler and easier to follow. This DataFrame takes up approximately 880
bits underneath. Recall that when we create a multi-index, the index data
is compressed, which is why this multi-index DataFrame is smaller than its
single-index counterpart.
Listing 3-19. Storing and operating on restaurant health inspection
data in a multi-index DataFrame
import pandas as pd
restaurants = pd.MultiIndex.from_tuples(
(
("Diner", (4,2)),
("Diner", (4,2)),
("Pandas", (5,4)),
("Pandas", (5,4)),
),
names = ["restaurant", "location"],
)
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59
restaurant_inspections = pd.DataFrame(
{
"date": ["02/18", "05/18", "02/18", "05/18"],
"score": [90, 100, 55, 76],
},
index=restaurants,
)
>> restaurant_inspections
date score
restaurant location
Diner (4, 2) 02/18 90
05/18 100
Pandas (5, 4) 02/18 55
05/18 76
>> restaurant_inspections["total"] = \
restaurant_inspections["score"].groupby(
["restaurant","location"],
).count()
>> restaurant_inspections.set_index(
["total"],
append=True,
inplace=True,
)
date score
restaurant location total
Diner (4, 2) 2 02/18 90
05/18 100
Pandas (5, 4) 2 02/18 55
05/18 76
What if we take this one step further? If we make the dates the column
names, then all the scores will be on the same row and the calculation
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60
becomes trivial. Here the unique restaurants are indexes, and the unique
inspection dates are columns. Note the score is now the only data. This
makes each row a unique restaurant, and thus the count can simply be
performed across each row. See Listing 3-20 .
Listing 3-20. Storing and operating on restaurant health inspection
data in a multi-index date column DataFrame
import pandas as pd
restaurants = pd.MultiIndex.from_tuples(
(
("Diner", (4,2)),
("Pandas", (5,4)),
),
names = ["restaurant", "location"],
)
restaurant_inspections = pd.DataFrame(
{
"02/18": [90, 55],
"05/18": [100, 76],
},
index=restaurants,
)
>> restaurant_inspections
date 02/18 05/18
restaurant location
Diner (4, 2) 90 100
Pandas (5, 4) 55 76
>> restaurant_inspections["total"] = \
restaurant_inspections.count(axis=1)
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61
>> restaurant_inspections.set_index(
["total"],
append=True,
inplace=True,
)
date 02/18 05/18
restaurant location total
Diner (4, 2) 2 90 100
Pandas (5, 4) 2 55 76
This DataFrame takes up approximately 660 bits underneath. Note this
takes up less memory because we no longer are tracking the date and score
column names and the date values are no longer being repeated. This
is pretty much as compressed as we can get with this data, and it allows
us to perform a very efficient aggregated calculation over each unique
restaurant. Let’s see if we can identify any holes in using this format on a
larger data set.
Currently each row is a unique restaurant, but what if there were
multiple restaurants with the same name at different locations? This would
still mean that there is a unique restaurant per row so no issues there.
What if the restaurants were not all inspected on the same days? In a
large city, it would be near impossible for an inspector to inspect all the
restaurants on the same day. This means then that there would be holes in
the data as shown in Listing 3-21 .
Listing 3-21. A r epresentation of Listing 3-20 if not all restaurants
were inspected on the same day
date 02/18 05/18 06/18 07/18
restaurant location total
Diner (4, 2) 2 90 100 NaN NaN
Pandas (5, 4) 2 NaN NaN 55 76
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62
These holes are potentially a big problem. Recall that the score data
was represented as an unsigned 8-bit integer, now because there are NaNs
in the data, the type must accommodate the NaN type size which forces
the type to be a 32-bit float. That’s four times more memory for each score.
Not only that, but now we have a bunch of gaps in our data that wasted
space and ultimately wasted memory. The fewer dates in common there
are between the restaurants, the worse this problem becomes. Multi-level
column index to the rescue! See Listing 3-22 .
Listing 3-22. Storing and operating on restaurant health inspection
data in a multi-index multi-level column DataFrame
import pandas as pd
restaurants = pd.MultiIndex.from_tuples(
(
("Diner", (4,2)),
("Pandas", (5,4)),
),
names = ["restaurant", "location"]
)
inspections = pd.MultiIndex.from_tuples(
(
(0, "score"),
(0, "date"),
(1, "score"),
(1, "date"),
),
names=["inspection", "data"],
)
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63
restaurant_inspections = pd.DataFrame(
[[90, "02/18", 100 "05/18",], [55, "04/18", 76 "01/18",]],
index=restaurants,
columns=inspections,
)
>> restaurant_inspections
inspection 0 1
score date score date
restaurant location
Diner (4, 2) 90 02/18 100 05/18
Pandas (5, 4) 55 04/18 76 01/18
>> total = \
restaurant_inspections.iloc[
:,
restaurant_inspections.columns.get_level_values("data") \
== "score"
].count()
>> new_index = pd.DataFrame(
total.values,
columns=["total"],
index=restaurant_inspections.index,
)
>> new_index.set_index("total", append=True, inplace=True)
>> restaurant_inspections.index = new_index.index
>> restaurant_inspections
inspection 0 1
score date score date
restaurant location total
Diner (4, 2) 2 90 02/18 100 05/18
Pandas (5, 4) 2 55 04/18 76 01/18
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64
This is probably the most optimal we can get with this DataFrame
format for this particular use case. We have compressed our data as much
as possible taking advantage of both multi-level indexes and multi-level
columns and organized the DataFrame in such a way as to achieve the
fastest calculation possible. Note the main disadvantage of this particular
format is it requires a bit of finagling to get the total back onto the index,
and for that reason, this solution is less readable. If this was the solution
you were going to go with, you might consider making two custom
functions: one that puts data onto the index and another that puts data
onto the columns. These functions would improve code readability by
hiding the finer details of appending level data to the DataFrame.
Once you have decided on a DataFrame format that makes sense,
you will likely need to load your raw data into pandas, normalize it,
and convert it to that particular DataFrame format. In Chapter 4, we’ll
dive into some common pandas data loading methods and discuss the
normalization options they provide in more detail.
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65 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_4
CHAPTER 4
Loading and 
Normalizing Data
Raw data comes in many forms: CSV, JSON, SQL, HTML, and so on.
pandas provides data input and output functions for loading data into a
pandas DataFrame and outputting data from a pandas DataFrame into
various common formats. In this chapter, we’ll deep dive into some of
these input functions and explore the various loading and normalization
options they provide.
The functions that load data into pandas provide a wide range
of normalization and optimization capabilities that can improve the
performance of a program, even to the point where it means the difference
between being able to load the data into pandas and running out of
memory. Each input function is different however, so it really depends
on the input/output format that you are working with and it’s always
worthwhile to check the documentation of the particular functions you are
using. Table  4-1 lists the various input and output functions that pandas
supports.

66
Chapter 3 mentioned several very good reasons for normalizing data;
it can save memory and optimize data analysis. Normalizing data can gain
you the benefit of utilizing Python’s string cache or run computations in
C rather than Python by choosing a C-compatible data type. Many of the
preceding input functions provide options for various ways of normalizing
data as part of the load process. Instead of loading the data with a large
memory foot print and then removing unnecessary columns or casting
columns to a smaller data type to reduce the memory foot print after
loading, many of the input functions allow you to remove and specify the
types of columns during load. This means you can load more data without
running out of memory, and the data load and normalization process is
faster since you are doing two things at once rather than consecutively
loading and then normalizing.
Operations that result in the creation and elimination of data can be
expensive because they require large chunks of memory to be allocated
and deallocated. Converting (more commonly referred to as casting) data
Table 4-1. IO p andas data functions
Input Output
readacsv toacsv
readaexcel toaexcel
readahdf toahdf
readasql toasql
readajson toaJSON
readahtml toahtml
readastata toastata
readaclipboard toaclipboard
readapickle toapickle
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from one type to another is also expensive as it requires large chunks of
memory to be allocated and deallocated. Working with large chunks of
memory often means cache misses and a lot of time is spent in IO moving
things from memory that is farther away from the CPU to memory that is
closer to the CPU (such as from main memory to the first level cache, for
example). Thus, while you may think that memory has nothing to do with
processing speed, it can actually have a huge impact on the runtime.
In these input functions, pandas typically infers the data type upon
loading the data. While this can be quite nice at first and seem like a
fantastic feature that you should surely always take advantage of, it also has
a large and often negative impact on performance. Often the data being
loaded has not yet been normalized, and numeric columns may contain
non-numeric values, for example, that force the inferred data type to be an
object, the largest data type it can be. Many of the data load functions allow
you to specify the type of the columns and convert place holder values to
NaNs which can prevent pandas from inferring the wrong data type.
pd.read_csv
The pandas CSV loader pd.read_csv is the most widely used of the loaders
and by far the most complete in terms of data normalization options.
Because the Python standard library has a built-in CSV loader and the
pandas loader has some fairly fancy Pythonic options, it has two different
parsing engines: the C engine and the Python engine. As you can probably
guess by now, the C engine is more performant than the Python engine,
but depending on what options you specify, you may have no choice but
to use the Python engine for parsing. Thus, it’s advisable to be careful
which options you use and the values you provide to those options so that
you guarantee you are using the C parsing engine and get the best load
performance possible. The CSV loader has an explicit engine parameter
that lets you force the parsing engine to be Python or C. Explicitly always
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specifying this parameter when loading is an easy way to guarantee the
CSV loader uses the C engine for parsing. If you specify another Python
parser–specific option while engine is explicitly set to ‘c’, the CSV loader
will throw an exception informing you that particular setting is not
compatible with the C parsing engine as shown in Listing 4-1 .
Listing 4-1. read_csv will raise a ValueError when engine is set to ‘c’
and other settings are not compatible
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,145
"""
)
>> df = pd.read_csv(data, sep=None, engine='c')
ValueError: the 'c' engine does not support sep=None
with delim_whitespace=False
Another reason to use the C parsing engine is that it supports a higher
precision of floating points via the float_precision parameter. Generally, the
Python engine uses double floating point precision, and the C engine uses
its own low-level string to decimal parser that is comparable to the Python
engine. Both can result in floating point rounding errors such as cases where
–15.361 and –15.3610 are not equal. However, the C parsing engine supports
additional options high precision and round- trip precision. If you want your
floats to be as accurate as possible, use the round-trip precision option. This
is a common problem with floating points, and so, an alternative approach,
often used when handling financial data, is to split a float into two integers:
one integer represents the number above the decimal and the other
represents the number below the decimal.
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The first parameter to the read_csv function is filepath_or_buffer. Typically,
the path to the CSV file is passed here, but note for unit testing purposes
and for example purposes in the rest of this chapter, a StringIO object can be
passed in its place. It also accepts a URL path if the CSV file is hosted by a third-
party application for example. The documentation officially reads 1
By file-like object, we refer to objects with a read() method, such as a file
handler (e.g. via builtin open function) or StringIO.
This file-like object is another Python-ism and is commonly known
as duck typing. This term is born of the idiom “If it walks like a duck and
quacks like a duck, it’s a duck.” In this case, if it has a read method, it is
a file-like object. This is why StringIO can also be substituted for a file
handler since it also has a read method. StringIO is a nice substitute in
unit tests since it allows you to pretend it’s a file without actually having to
include a test.csv for validating your loader works as expected.
read_csv provides a sep parameter which specifies the character(s) used
to delineate the data. The default is a comma. Note sep treats any values
longer than one character with the exception of \s+ as regular expressions.
The use of complex delimiters here can force the use of the Python parsing
engine instead of C, and for that reason, it is advisable to use single-character
delimiters when possible and not specify complex regular expressions.
The parameter delim_whitespace may also be set to True as an alternative
to setting sep= “\s+”, specifically to denote whitespace file delineation.
The sep parameter can also be set to None, in which case the Python
parsing engine will be used and it will automatically detect the delimiter.
The skipinitialspace parameter can be used to ignore spaces surrounding
the delimiter. By default, this is disabled, so if there are spaces between
the delimiters in your file, you will need to set this to True. Listing 4-2
demonstrates how you might use sep in combination with skipinitialspace to
configure loading of data that is not comma delimited.
1 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.
read_hdf.html
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Listing 4-2. Loading non-comma-delimited data
>> data = io.StringIO(
"""
id| age| height| weight
129237| 32| 5.4| 126
123083| 20| 6.1| 145
"""
)
>> pd.read_csv(data, sep="|", skipinitialspace=True)
idageheightweight
0129237325.4126
1123083206.1145
The parameter usecols narrows down the list of columns to load. It’s
possible to have columns within the CSV file that you don’t care about, and thus
this can be an efficient way of eliminating them upon load as opposed to loading
all the data and removing them after. Note that usecols can also be a function
where the column name is an input and the output is a Boolean indicating
whether to include that column or discard it upon loading. A function however
is less ideal as it requires calls between the C parsing engine and the custom
function which will slow the loader down. Listing 4-3 shows an example of using
use_cols to eliminate columns id and age during load.
Listing 4-3. Eliminating columns during load
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,145
"""
)
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>> pd.read_csv(data, usecols=["height", "age"])
heightweight
05.4126
16.1145
The skiprows parameter allows you to skip certain rows in the file.
In its simplest form, it can be used to skip the first n number of rows in a
file; however, it can also be used to skip particular rows by specifying a list
of indexes to skip. It can also be a function that accepts a row index and
returns True if that row should be skipped. Note if a function is passed
here, it will have the unfortunate consequence of jumping between the
C parsing engine and the skiprows Python function which may lead to a
substantial slowdown when parsing large data sets. For this reason, it’s
recommended to keep the skiprows a simple integer or list value.
The skipfooter parameter lets you specify the number of lines at the
end of the file to skip. The documentation notes that this is unsupported
with the C parsing engine. Since the Python engine uses the Python CSV
parser, the CSV parser runs and then the last lines of the file are dropped.
This makes sense if you think about this problem a little more deeply:
How would the parser know which lines to skip without knowing how
many lines there are in the file first (which would require first parsing the
file)? This behavior can be somewhat surprising for some users when,
for example, they are actively trying to avoid lines in the file because they
break the parser and find that the parser is still trying to parse those lines
they configured the parser to skip. If you run into this situation in your own
program, nrows is a nice alternative. Listing 4-4 demonstrates an example
of running into a parsing error even though the loader was configured to
skip that line.
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Listing 4-4. Encountering a parsing error when using skipfooter
import pandas as pd
data = io.StringIO(
"""
student_id, grade
1045,"a"
2391,"b"
8723,"c"
1092,"a"
"""
)
try:
grades = pd.read_csv(
data,
skipfooter=1,
)
except pd.errors.ParserError as e:
pass
The parameter comment lets you specify a character that denotes a
comment, and the rest of the line is ignored after the character. This can
be a good strategy for manually filtering out certain lines prior to parsing.
If you comment out the line, then it will not be included in the data set.
You may also consider setting error_bad_lines to False. Note by default
pandas will still raise a warning on each bad line, so if you wish to disable
the warning as well, you may set warn_bad_lines to False.
By default, the header or column names are inferred by read_csv, and
the first row of the data is treated as the header. Using the parameter header,
you can specify which row numbers are to be treated as columns if the data
contains multi-level columns. Similarly, using index_col, you can specify
which columns via column index are to be treated as part of the multi-index.
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In Listing 4-5 , a multi-index multi-level column DataFrame was dumped to
a CSV file using the pandas to_csv function. The data contains information
on family and species of nightshades. The indexes contain the id of the
family and species, while the columns contain the actual names. Here, the
first two rows contain multi-level column data so header is set to [0,1], and
the first two columns are the multi-index so index_col is set to [0,1].
Listing 4-5. Example of loading a multi-index multi-level column
DataFrame
>> data = io.StringIO(
"""
family,,nightshade,nightshade,nightshade
species,,tomatoe,deadly-nightshade,potato
family_id,species_id,,,
61248,129237,1,0,0
61248,123083,0,1,0
61248,123729,0,0,1
"""
)
>> df = pd.read_csv(data, header=[0,1], index_col=[0,1])
family nightshade
species tomatoes deadly-nightshade potato
family_id species_id
61248 12937 1 0 0
61248 123083 0 1 0
61248 123729 0 0 1
If the squeeze parameter is enabled, read_csv returns a Series instead of
a DataFrame if there is only one column in the CSV file. This can be useful
when you need to load data from multiple sources and combine it into a
single DataFrame. If the data is loaded into a series, you can simply add it to
an existing DataFrame as a new column as demonstrated in Listing 4-6 .
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74
Listing 4-6. Example of using squeeze
import pandas as pd
site_data = pd.read_csv('site1.csv')
site_data['site2'] = pd.read_csv('site2.csv', squeeze=True)
The dtype parameter allows you to specify a type for each column
in the data. If this is not specified, read_csv will attempt to infer the data
type which typically results in the inferred type being an object which is
the largest size that a data type can be. Specifying the dtype during load
can be a huge performance improvement, but that also means you have
to have some knowledge at load time about the columns in the data set. If
you don’t know exactly what to expect until you look at the data, you might
consider loading the header of the data first or the first couple rows using
nrows, identifying the column types, and then loading the whole data file
with the appropriate types specified.
Listing 4-7. Example of not specifying the types of the columns
when loading
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,145
"""
)
>> df = pd.read_csv(data, index_col=[0])
age height weight
id
129237 32 5.398438 126
123083 20 6.101562 145
>> df.memory_usage(deep=True)
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75
Index 16
age 16
height 16
weight 16
>> df.dtypes
age int64
height float64
weight int64
>> df.index.dtype
dtype('int64')
Listing 4-7 shows an example of loading the data without specifying
the type of each column. Note that all the types take up 8 bytes and are
defaulted to the largest int and float possible, whereas in Listing 4-8 , they
take up much less memory. This data only has two rows worth of data,
but just in those two rows, we’ve decreased the memory footprint of the
DataFrame by more than half just by specifying the types of the columns.
Listing 4-8. Example of specifying the types of the columns when
loading
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,145
"""
)
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'age': np.int8,
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76
'height': np.float16,
'weight': np.int16},
index_col=[0],
)
age height weight
id
129237 32 5.398438 126
123083 20 6.101562 145
>> df.memory_usage(deep=True)
Index 16
age 2
height 4
weight 4
>> df.dtypes
age int8
height float16
weight int16
>> df.index.dtype
dtype('int64')
The converters parameter allows you to specify a function to
convert values in a particular column such as in Listing 4-9 . This is a
nice normalization feature if, for example, there are multiple values that
represent the same value in a column and you wish to normalize it to a
single value. This, however, comes at a cost. Because these functions are
written in Python, the C engine must make calls between C and Python
to convert each of the values which can be very time-consuming when
working with large data sets. So, while the data is being normalized at
load time, it is also going to load more slowly because it will be jumping
between C and Python for each value to convert in each column. In this
scenario, it would be more performant to convert the column values after
using an Apply-Cython implementation so that the conversion happens
quickly all in C and avoids this jumping back and forth. See Chapter 6 for
how to implement an apply in Cython.
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Listing 4-9. Standardizing values at load time with converters
import pandas as pd
MEDICATIONS_MAPPER = {"atg": "atg", "aftg": "atg", "bta": "bta"}
def medication_converter(value):
return MEDICATIONS_MAPPER[value.lower()]
data = io.StringIO(
"""
id,age,height,weight,med
129237,32,5.4,126,bta
123083,20,6.1,145,aftg
"""
)
>> treatments = pd.read_csv(
data,
converters={'med': medication_converter},
)
id age height weight med
129237 32 5.4 126 bta
123083 20 6.1 145 atg
The nrows parameter allows you to specify the number of rows to read
from the file. Something that may be unintuitive here is that nrows doesn’t
actually skip reading the rows when using the Python parsing engine. This
is because the Python parsing engine reads the whole file first. This means
that if there are lines after the number of rows you intended to read from
the file that result in parsing errors, when running with the Python parsing
engine, you will not be able to avoid them by using nrows. Since the Python
parsing engine reads the whole file first, it will still throw a parsing error on
those lines, even though you told the CSV loader not to read those rows. So,
this is yet another reason to avoid the Python parsing engine, particularly
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78
when using this setting. Note that skipfooter, on the other hand, even in the
C parsing engine does in fact read the footer row. This is simply because in
order to identify it as the footer of the file, it has to read it and reach the end
of the file to identify it as the footer. Listing 4-10 shows an example of how to
avoid lines that would otherwise cause parsing errors using nrows and the C
parsing engine.
Listing 4-10. Avoiding a parsing error by using nrows
import pandas as pd
data = io.StringIO(
"""
student_id, grade
1045,"a"
2391,"b"
8723,"c"
1092,"a"
"""
)
grades = pd.read_csv(
data,
nrows=3,
)
The nrows parameter in combination with skiprows and header can also
be useful for reading a file into memory in pieces, processing it, and then
reading the next chunk. This is particularly useful with huge sets of data
that you may otherwise be unable to read all at once due to memory
constraints. Listing 4-9 shows an example of this. Note process is a function
that is wrapping the read_csv function. It takes the loaded data from
read_csv and does some processing on it to reduce the memory footprint
and/or normalize it beyond the capabilities of read_csv and returns it to be
concatenated with the rest of the data. In Listing 4-11 , we load the first 1000
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79
rows, process them, and use those first 1000 rows to initialize data. Then we
continue reading in rows, processing 1000 at a time until we read in less than
1000 rows. Once we read in less than 1000 rows, we know we’ve read the
entire file and exit the loop.
Listing 4-11. Reading a file and processing it nrows at a time to
reduce memory overhead
import pandas as pd
ROWS_PER_CHUNK = 1000
data = process(pd.read_csv(
'data.csv',
nrows=ROWS_PER_CHUNK,
))
read_rows = len(data)
chunk = 1
while chunk * ROWS_PER_CHUNK == read_rows:
chunk_data = process(pd.read_csv(
'data.csv',
skiprows=chunk * ROWS_PER_CHUNK,
nrows=ROWS_PER_CHUNK,
header=None,
names=data.columns,
))
read_rows += len(chunk_data)
data = data.append(process(chunk_data), ignore_index=True)
The parameter iterator used in combination with chunksize also lets
you read the data in chunks similar to Listing 4-11 . Again, this may be
necessary for performance reasons. Maybe the data you are reading
cannot be read in with a smaller memory footprint using read_csv, and
some normalization must take place after loading that results in a smaller
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80
memory footprint, meaning while you aren’t able to read all the data into
memory all at once using read_csv because the resulting DataFrame would
be too large, you are able to read it in a chunk at a time and reduce the
memory footprint on each chunk such that the resulting DataFrame will fit
in memory. Note using iterator and chunksize is a better alternative if you
are reading the whole file chunks at a time than using nrows and skiprows
as it keeps the file open at the correct location instead of constantly
re-opening it and scrolling to the next position. Listing 4-12 shows an
example of this.
Listing 4-12. Reading a file in chunks to reduce memory overhead
import pandas as pd
ROWS_PER_CHUNK = 1000
data = pd.DataFrame({})
reader = pd.read_csv(
'data.csv',
chunksize=ROWS_PER_CHUNK,
iterator=True
)
for data_chunk in reader:
processed_data_chunk = process(data_chunk)
data = data.append(processed_data_chunk)
The parameter low_memory which defaults to True actually processes
the file in chunks already when using the C parsing engine in order to save
memory. However, it is limited in the processing it can do of each chunk
by the options of read_csv, and thus custom processing and iterating over
the chunks manually may be better in certain scenarios.
pandas read_csv function also provides an option called memory_map.
When set to True and if a filepath is provided, it will map the file directly into
virtual memory and access the data directly from there. Using this option
can improve performance because there is no longer any IO overhead
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waiting for the next chunk of the file to be loaded into memory. Generally,
accessing memory mapped files is faster because the memory is local to the
program and the memory mapped is already in the page cache so there is
no need to load it on the fly. In practice, memory mapping the file generally
doesn’t provide much of a performance advantage in the typical use case
of loading a file serially from beginning to end. If you are experiencing a
lot of cache misses, meaning the file data that would normally be loaded
into cache (memory closer to the CPU) is not present and must be loaded
from main memory, this may hold a performance improvement. Cache
misses may happen if other programs are running concurrently which add
their memory into the cache and consequently knock your file data out of
the cache. See Chapter 8 for a more detailed explanation of the memory
hierarchy and cache misses. This might also hold a performance advantage
if you are reading this file many times over the course of your program or
your program runs periodically and you don’t want to keep having to load
the same file into memory each time it runs. So, while this feature sounds
like it can provide you with a substantial speedup, the reality is unless you
are working outside of the standard read a file from start to finish workflow,
it’s unlikely to do so.
The na_values parameter allows you to specify values to interpret as
Not a Number, also known as NaNs. This type comes from NumPy which,
if you recall from Chapter 2, is a dependency of pandas. It’s commonly
used in NumPy as a placeholder for a value resulting from a computation
that is invalid such as divide by 0. Note by default pandas interprets any
string Nan or nan as a NaN type automatically. This automatic conversion
may be problematic if you are working with data where Nan or nan may
actually be a valid name, for example. This is where keep_default_na
comes in handy. Setting the parameter keep_default_na to False turns off
pandas automatic interpretation of certain values to NaNs. For a complete
list of values that pandas automatically converts to the NaN type, see the
Appendix.
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The parameter na_filter when set to False disables checking for NaNs
altogether, and the documentation notes this can lead to a performance
improvement when you know for certain there are no NaNs in the data.
The parameter na_values, on the other hand, lets you specify additional values
other than the default set that you would also like to be converted to NaNs.
The parameter verbose outputs the number of NaN values in each
column that contains NaNs when the Python parsing engine is used
and parsing performance metrics when the C parsing engine is used.
The pandas documentation states it outputs NaN values explicitly for
non-numeric columns. This can be somewhat deceiving however, as
the non-numeric determination is made at the time the parsing engine
runs and not based on the final type of the column in the resulting
DataFrame. Any column with a NaN in it at parsing time is considered a
non-numeric column, even if the type of that column ultimately ends up
being a numeric type (such as a float64 in the following example). The
Python parser must parse all the values in the column and convert them
appropriately to NaNs before assigning the final type. This means the NaN
values are counted during parsing before the final type of the column has
been assigned. Listing 4-13 demonstrates this behavior.
Listing 4-13. Unexpectedly counting NaNs in numeric columns
>> import pandas as pd
>> data = io.StringIO(
"""
student_id,grade
1045,"a"
2391,"b"
,"c"
1092,"a"
"""
)
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>> grades = pd.read_csv(
data,
verbose=True,
index_col="student_id",
engine='python',
)
Filled 1 NA values in column student_id
>> grades
grade
student_id
1045 a
2391 b
NaN c
1092 a
>> grades.index.dtype
dtype('float64')
A limitation of how pandas read_csv handles placeholder types is that
you cannot specify a converter to convert a NaN to a 0, for example, and also
cast a column to a particular dtype. Listing 4-14 illustrates a case where you
might wish to do this. The weight column in the data set does not always have
a value, nor is it consistent in the way a non-value is entered. Sometimes it
is left as empty; other times, it is “unknown”. If we do not specify a type for
this column and let pandas infer the type, pandas stores the column values
as objects, meaning some of them are integers, some of them are NaNs, and
some of them are strings. Note that an object in this example takes up 32 bytes
per element with some additional overhead. This is much more than the
desired type of an int16 which takes up 2 bytes per element. Not only does the
resulting DataFrame take up much more memory, but it is also unusable in its
state to run computations over. Since some of the values are objects, summing
all the weights in the column, for example, might result in string addition
rather than integer addition. Thus, leaving pandas to infer the data type in this
scenario is less than ideal.
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Listing 4-14. Example of how pandas handles NaNs in the data by
default
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,
123087,25,4.5,unknown
"""
)
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'age': np.int8,
'height': np.float16},
index_col=[0],
)
age height weight
id
129237 32 5.398438 126
123083 20 6.101562 NaN
123083 20 6.101562 unknown
>> df.memory_usage(deep=True)
Index 24
age 3
height 6
weight 155
>> df.dtypes
age int8
height float16
weight object
>> df.index.dtype
dtype('int64')
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Instead of letting pandas infer the data type, let’s convert all the
placeholder values to NaNs using na_values. Although ideally we would like
them to be int16s, float16s take up the same amount of memory, and pandas
supports NaNs being stored as floats whereas it does not support them being
stored as integers during loading, so we set the dtype of the weight column
to be float16. Note if we do not specify the dtype, it will be a float64. If we
really need them to be integers, we can replace the NaNs with zeros using
fillna and convert them using astype after loading as shown in Listing 4-15 .
Listing 4-15. Example of using na_values and dtype to convert
placeholder values to float16 NaNs during load
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,
123087,25,4.5,unknown
"""
)
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'age': np.int8,
'height': np.float16,
'weight': np.float16},
na_values={"unknown"},
index_col=[0],
)
age height weight
id
129237 32 5.398438 126
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123083 20 6.101562 NaN
123083 20 6.101562 NaN
>> df.memory_usage(deep=True)
Index 16
age 3
height 6
weight 6
>> df.dtypes
age int8
height float16
weight float16
>> df.index.dtype
dtype('int64')
>> df["weight"].fillna(0, inplace=True)
>> df["weight"] = df["weight"].astype(np.int16)
>> df
age height weight
id
129237 32 5.398438 126
123083 20 6.101562 0
123083 20 6.101562 0
>> df.memory_usage(deep=True)
Index 16
age 3
height 6
weight 6
>> df.dtypes
age int8
height float16
weight int16
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The parsing performance metrics output in verbose mode when using
the C parsing engine can be useful in determining where the parsing engine
is spending its time. Listing 4-16 shows an example output. Tokenization
is the parser breaking up the data into individual values, Type conversion
is converting each column to a particular type whether that is inferred by
pandas or explicitly specified, and Parser memory cleanup is the time it took
to free all the no longer needed memory after the data was read. Depending
on these values, they may point to areas for improvement, for example, if
Tokenization is taking a long time, you may be able to speed up performance
by specifying additional options like how to interpret quotes, spaces, bad
lines, and so on. If a lot of time is spent in type conversion, it may indicate
you have some custom converters that are slowing down the process or you
may need to specify the dtypes rather than letting pandas infer them. If you
find a lot of time is spent in memory cleanup, you may need to not parse the
whole file at once or you may be doing too many conversions of the data that
lead to a lot of memory duplication and thus a lot of memory cleanup.
Listing 4-16. Example output when running in verbose mode with
C parsing engine
>> grades = pd.read_csv(verbose=True, engine='c')
Tokenization took: 0.01 ms
Type conversion took: 0.45 ms
Parser memory cleanup took: 0.01 ms
The parse_dates parameter if set to True will attempt to automatically
detect and convert columns with date formatted strings to datetime objects.
Rather than just setting it to True however, it’s far better to explicitly list which
columns should be converted to datetime objects. This parameter lets you
explicitly specify which columns to convert in the form of a list and even
combine multiple columns into a single datetime object when it is specified as
a list of lists of columns. Listing 4-17 shows an example of explicitly specifying
which columns to convert. Note each datetime object takes up 8 bytes, but this
is still far less memory than if we didn’t convert it at all.
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Listing 4-17. Explicitly converting certain columns to datetime
objects
>> data = io.StringIO(
"""
id,birth,height,weight
129237,04/10/1999,5.4,126
123083,07/03/2000,6.1,150
123087,11/23/1989,4.5,111
"""
)
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'height': np.float16,
'weight': np.int16},
parse_dates = ["birth"],
index_col=[0],
)
birth height weight
id
129237 1999-04-10 5.398438 26
123083 2000-07-03 6.101562 150
123083 1989-11-23 6.101562 111
>> df.memory_usage(deep=True)
Index 24
birth 24
height 6
weight 6
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>> df.dtypes
age int8
height float16
weight datetime64[ns]
>> df.index.dtype
dtype('int64')
Note Listing 4-17 assumes that there are no NaNs or placeholder values
in the column. If there are, like in Listing 4-18 , na_values must be specified
to convert all the placeholder values to NaNs ; otherwise, the column will
be an object rather than a datetime because the placeholder values will be
left as strings.
Listing 4-18. Explicitly converting certain columns to datetime
objects and handling NaNs
>> data = io.StringIO(
"""
id,birth,height,weight
129237,04/10/1999,5.4,126
123083,unknown,6.1,150
123087,11/23/1989,4.5,111
"""
)
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'height': np.float16,
'weight': np.int16},
parse_dates=["birth"],
na_values=["unknown"],
index_col=[0],
)
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birth height weight
id
129237 1999-04-10 5.398438 26
123083 NaT 6.101562 150
123083 1989-11-23 6.101562 111
>> df.memory_usage(deep=True)
Index 24
birth 24
height 6
weight 6
>> df.dtypes
age int8
height float16
weight datetime64[ns]
>> df.index.dtype
dtype('int64')
Generally, parse_dates while it’s convenient is counterproductive from
a performance perspective. It takes time to convert the dates, and once
they are converted, the data type is not translatable to C. For this reason,
it’s recommended to convert datetimes to time since the epoch or some
simple numeric C-translatable value if possible. If you need to work with
particular days, months, and years, it might even make sense to store those
in separate columns.
There are many other date-specific parameters included in read_csv.
The parameter infer_datetime_format is enabled by default, so whenever
possible, pandas attempts to infer the format of any datetime values
automatically. The documentation says that in some cases this can
increase the parsing speed by five to ten times when it’s able to detect and
use a particular date parsing format. 2 When set to True, the parameter
2 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.
read_csv.html
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keep_date_col keeps both the combined column and the original separate
date columns if parse_dates specified that multiple date columns should
be combined together. The date_parser parameter lets you specify a date
parsing function. The documentation notes this function may be called in
several different ways ranging from calling it once on each row or passing
in all rows and columns at once. Since this function is jumping between
C and Python, it’s advantageous to call it the least amount of times as
possible. This means it’s best to implement this function to operate on all
datetime rows and columns and output an array of datetime instances.
This function could be an existing parser (the default is dateutil.parser.
parser), or it could be a custom function. This might be useful if you need
to do some special timezone handling or the data is stored in a special
datetime format. Not all countries specify the day before the month so
pandas provides a dayfirst parameter so you can specify whether the day
comes first in the dates you are parsing. The parameter cache_dates which
is enabled by default keeps a cache lookup of the converted dates, so that
if the same date appears multiple times in the data set, it does not have to
run the conversion again and can just use the cached value.
The parameter escapechar lets you escape certain characters. For
example, in most programing languages, a commonly used escape
character is a backslash (\) so it may be desirable to escape certain quote
characters inside of a quote with a \” or element delimiter characters with \,.
Listing 4-19 illustrates this use case. If the temperature recordings were
recorded by a country that uses commas as a decimal point delimiter and
also uses commas as a CSV element delimiter, read_csv will not be able to
parse this file with its default configuration and will raise a parsing error,
“pandas.errors.ParserError: Error tokenizing data. C error: Expected 2
fields in line 5, saw 3”. If, instead, the backslash character is used to escape
all the commas delimiting decimal places (\,), then read_csv can be
configured in such a way to correctly parse the data.
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Listing 4-19. Using commas as a delimiter and a decimal point
>> import pandas as pd
>> data = io.StringIO(
"""
temp, location
35,234unf923
32,2340inf012
33,2340inf351
33\,1,2340abe045
"""
)
>> grades = pd.read_csv(
data,
decimal=",",
escapechar="\\",
index_col="location",
)
temp
location
234unf923 35.000000
2340inf012 32.000000
2340inf351 33.000000
2340abe045 33.100000
pd.read_json
The read_json loader parses entirely in C unlike read_csv which may use
the Python parser under certain conditions.
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The parameter orient defines how the JSON format will be converted
into a pandas DataFrame. There are six different options: split, records,
index, columns, values, and table. If the JSON is formatted such that there
are columns, data, and an index already defined as keys, as is the case in
Listing 4-20 , the split option should be used. It’s also worth noting that the
JSON parser is particularly picky about spacing including whitespace.
Listing 4-20. Using orient split
>> data = io.StringIO(
"""
{
"columns": ["temp"],
"index": ["234unf923", "340inf351", "234abe045"],
"data": [[35.2],[32.5],[33.1]],
}
"""
)
>> temperatures = pd.read_json(
data,
orient="split",
)
temp
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
If the JSON is formatted such that each value is a row in the data with
the column names as keys, as is the case in Listing 4-21 , the records option
should be used.
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Listing 4-21. Using orient records
>> data = io.StringIO(
"""
[
{"location": "234unf923", "temp": 35.2},
{"location": "340inf351", "temp": 32.5},
{"location": "234abe045", "temp": 33.1},
]
"""
)
>> temperatures = pd.read_json(
data,
orient="records",
)
location temp
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
If the JSON is formatted such that each key is the index value and the
value of each key is a dictionary of the columns and values for the row, as
is the case in Listing 4-22 , the index option should be used.
Listing 4-22. Using orient index
>> data = io.StringIO(
"""
{
"234unf923": {"temp": 35.2},
"340inf351": {"temp": 32.5},
"234abe045": {"temp": 33.1},
}
"""
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)
>> temperatures = pd.read_json(
data,
orient="index",
)
temp
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
If the JSON is formatted such that each key is the column and each
value is a dictionary where the key is the index and the value is the column
value, as is the case in Listing 4-23 , the columns option should be used.
Listing 4-23. Using orient columns
>> data = io.StringIO(
"""
{
"temp": {
"234unf923": 35.2,
"340inf351": 32.5,
"234abe045": 33.1,
},
}
"""
)
>> temperatures = pd.read_json(
data,
orient="columns",
)
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temp
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
If the JSON is formatted such that each row is simply represented as
a list of values, as is the case in Listing 4-24 , the values option should be
used.
Listing 4-24. Using orient values
>> data = io.StringIO(
"""
[
["234unf923", 35.2],
["340inf351", 32.5],
["234abe045", 33.1],
]
"""
)
>> temperatures = pd.read_json(
data,
orient="values",
)
0 1
0 234unf923 35.200000
1 340inf351 32.500000
2 234abe045 33.100000
If the JSON is formatted such that it provides a detailed data schema,
as is the case in Listing 4-25 , the table option should be used.
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Listing 4-25. Using orient table
>> data = io.StringIO(
"""
{
"schema": {
"fields": [
{"name": "location", "type": "string"},
{"name": "temp", "type": "string"},
],
"primaryKey": "location",
},
"data": [
{"location": "234unf923", "temp": 35.2},
{"location": "340inf351", "temp": 32.5},
{"location": "234abe045", "temp": 33.1},
]
}
"""
)
>> temperatures = pd.read_json(
data,
orient="table",
)
temp
location
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
Similar to read_csv, read_json has a chunksize that lets you read the
files in chunks at a time. This only is permitted however if the lines option
is also set to True, meaning the JSON format is oriented as records without
the list brackets. Listing 4-26 demonstrates this.
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Listing 4-26. Loading the file in chunks
>> data = io.StringIO(
"""
{"location": "234unf923", "temp": 35.2}
{"location": "340inf351", "temp": 32.5}
{"location": "234abe045", "temp": 33.1}
"""
)
>> temperatures = pd.DataFrame({})
>> reader = pd.read_json(
data,
lines=True,
chunksize=2,
)
>> for chunk in reader:
temperatures = temperatures.append(process(chunk))
>> temperatures
location temp
234unf923 35.200000
340inf351 32.500000
234abe045 33.100000
By default, the JSON loader determines whether certain columns are
date-like based on the column name unlike other readers that look at the
values. It accepts a convert_dates parameter which can be a list of column
names or a Boolean. If it’s set to True, it converts columns that end
with _at or _time, begin with timestamp, or are named modified or date
into datetimes. To disable automatic detection of date columns, you can
set keep_default_dates to False.
By default, the JSON loader will try to infer the type of each column and
axis, unless orient is set to table, in which case the type is provided as part of
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the JSON schema. Just like reading a CSV file, if the types are not specified
in the JSON file, it saves a lot of memory to provide them. Listing 4-27 shows
what the memory footprint might be of a JSON file being loaded without
types specified vs. Listing 4-28 which shows the memory footprint of the
same JSON file with types specified. Note in Listing 4-28 where types are
explicitly specified, the memory of the resulting DataFrame decreased by
about 40%.
Listing 4-27. Loading a JSON with pandas type inference
>> data = io.StringIO(
"""
{
"birth": {
"129237": "04/10/1999",
"123083": "05/18/1989",
},
"height": {
"129237": 5.4,
"123083": 6.1,
},
"weight": {
"129237": 126,
"123083": 130,
},
}
"""
)
>> patient_info = pd.read_json(
data,
orient="columns",
)
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birth height weight
129237 04/10/1999 5.4 126
123083 05/18/1989 6.1 130
>> df.dtypes
birth object
height float64
weight int64
>> df.index.dtype
dtype('int64')
>> df.memory_usage()
Index 16
birth 16
height 16
weight 16
Listing 4-28. Explicitly specifying the type when loading a JSON
>> data = io.StringIO(
"""
{
"birth": {
"129237": "04/10/1999",
"123083": "05/18/1989",
},
"height": {
"129237": 5.4,
"123083": 6.1,
},
"weight": {
"129237": 126,
"123083": 130,
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101
},
}
"""
)
>> patient_info = pd.read_json(
data,
orient="columns",
convert_dates=["birth"],
dtype={"height": np.float16, "weight": np.int16},
)
birth height weight
129237 1999-04-10 5.4 126
123083 1989-05-18 6.1 130
>> df.dtypes
birth datetime64[ns]
height float16
weight int16
>> df.index.dtype
dtype('int64')
>> df.memory_usage()
Index 16
birth 16
height 4
weight 4
pd.read_sql, pd.read_sql_table, and 
pd.read_sql_query
The p andas read_sql loader is a wrapper around read_sql_table and read_
sql_query. Depending on the parameters passed to it, it calls one of those
two functions underneath. It also has built-in support for SQLAlchemy.
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SQLAlchemy is a very popular Object Relational Mapper library, also
known as an ORM. The pandas SQL reader also supports talking directly to
DBAPI which is a lower-level database library that SQLAlchemy depends
on. While DBAPI is limited within pandas to SQLite3, SQLAlchemy can
talk to all kinds of relational databases so there’s no need to rewrite all
your SQL queries when switching databases. ORMs are quite popular
in the application development space as they allow you to map your
database tables to objects or classes in Python. This is nice because you
can track your database table definitions inside your codebase. You can
define your database tables as classes and then with a simple command
add them to your database. You can also modify existing database tables
via migration scripts using migration libraries like Alembic, for example,
which let you roll forward and roll back database changes with little risk
of unrecoverable production database mishaps. When building out table
definitions, you can also specify things like the conversion of column types
between Python and the database. SQLAlchemy is also nice because it
abstracts the SQL query into more human-readable query language with
easily parameterized expressions, like in Listing 4-29 .
Listing 4-29. Using a raw SQL string query vs. the SQLAlchemy
ORM to generate a query
cur.execute(
"""
SELECT * FROM temperature_readings
WHERE temperature_readings.temp > 45
"""
)
session.query( TemperatureReadings ).filter(
temp > 45
)
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Listing 4-30 shows an example of how you might build a database and
insert data into it. In this example, we are creating a user table with two
columns, id and name, and inserting a new user with an id of zero and
name Eric into that table. Note two different URLs are defined in the code,
the one in use connects to sqlite and the other connects to a local Postgres
database instance.
Listing 4-30. Cr eating tables in a postgres database and adding data
to it using SQLAlchemy
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
SQLITE_URL = "sqlite://"
POSTGRES_URL = "postgresql://postgres@localhost:5432"
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String(50))
engine = create_engine(SQLITE_URL)
Session = sessionmaker(bind=engine)
def create_tables():
Base.metadata.create_all(engine)
def add_user():
session = Session()
user = User(id=0, name="Eric")
session.add(user)
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session.commit()
session.close()
>> create_tables()
>> add_user()
Listing 4-31 provides a simple docker-compose.yml file which will spin up
a local Postgres database on your machine using the command in Listing 4-32 .
Note this database is configured to write the data to disk so you can kill the
docker container at any time and the data will persist in the postgres-data
directory and be there the next time you spin up the docker container.
Listing 4-31. The contents of the docker-compose.yml that will
create a local Postgres database
version: '3'
services:
postgres:
image: postgres:9.4-alpine
ports:
- '127.0.0.1:5432:5432'
volumes:
- ./postgres-data:/var/lib/postgresql/data
Listing 4-32. Starting the Postgres database
>> docker-compose up -d
The SQL loaders generally can either load the whole table or load parts of
the table based on a query. Although the SQL loaders accept a SQLAlchemy
engine, they only accept a select statement rather than a query object. This
means while you can use SQLAlchemy’s query API, you must convert it to a
selectable before passing it into the loader as shown in Listing 4-34 . A selectable
is essentially the raw SQL query string. Listing 4-33 shows an example of how
you would load all the user data from the database into a DataFrame, while
Listing 4-34 shows how you might load the user with id=0 into a DataFrame.
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Listing 4-33. Loading all the users into a DataFrame using read_sql
>> pd.read_sql(
sql=User.__tablename__,
con=engine,
columns=["id", "name"],
)
id name
0 0 Eric
Listing 4-34. Loading the user with id=0 into a DataFrame using
read_sql
>> se lect_user0 = session.query(Patient).filter_by(id=0).
selectable
>> pd.read_sql(
sql=select_user0,
con=engine,
columns=["id", "name"],
)
id name
0 0 Eric
The SQL loaders have similar options as the other loaders we’ve looked
at, for example, loading the data chunks at a time or datetime conversion.
However, there are differences as well. Unlike some of the other loaders
we’ve looked at, the SQL loaders do not have an option for data type
specification. This often poses a problem for pandas users working with
databases as they may store a normalized version of the data in a database
and then wish to load it back out only to find the data types are not the
same. If you run into this situation, SQLAlchemy and some custom loading
code can help. SQLAlchemy provides a custom types option which lets
you convert between the database type and the Python type. As we’ve seen
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with other loaders where the types are not explicitly specified, pandas will
store the id column as an int64. Listing 4-35 shows an example of how we
might specify the Python type for the integer id column as an int32 instead
of a more generic and larger integer type. Using this table definition, now
when we add a user, the id will be stored as an integer inside the database,
but when we read it out, it will be a NumPy int32 type.
Listing 4-35. Using SQLAlchemy TypeDecorator to specify a data
type for pandas
from sqlalchemy import Column, String
from sqlalchemy.ext.declarative import declarative_base
import sqlalchemy.types as types
import numpy as np
Base = declarative_base()
class Int32(types.TypeDecorator):
impl = types.Integer
def process_bind_param(self, value, dialect):
return value
def process_result_value(self, value, dialect):
return np.int32(value)
class User(Base):
__tablename__ = 'user'
id = Column(Int32, primary_key=True)
name = Column(String(50))
Listing 4-36 shows a code snippet of the internal implementation of
read_sql where self.pd_sql is the SQLAlchemy engine object, sql_select
is the selectable passed in as the SQL parameter, and self.frame is the
resulting DataFrame that is returned. Here you can see exactly how pandas
is loading the data from the database and converting it into a DataFrame.
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The fetchall function returns the data as a list of tuples, for example, [(0, ‘Eric’)].
This implementation is relevant for the next step in how we will get pandas to
use the correct data types we defined in Listing 4-35 .
Listing 4-36. Par t of the pandas implementation of read_sql
result = self.pd_sql.execute(sql_select)
column_names = result.keys()
data = result.fetchall()
self.frame = DataFrame.from_records(
data, columns=column_names, coerce_float=coerce_float
)
Instead of relying on the pandas read_sql implementation, we are
going to write our own custom SQL loading code that will maintain the
data types we defined in the SQLAlchemy user table when creating the
DataFrame. The custom SQL loading code is shown in Listing 4-37 and is
faster and consumes less memory than using astype to convert the types
after loading.
Listing 4-37. Custom SQL loader code that maintains the data types
defined on the SQLAlchemy table in Listing 4-35
>> sql = session.query(User).selectable
>> results = engine.execute(sql).fetchall()
>> data = {
columns[col]: np.array(
[row[col] for row in results],
dtype=type(results[0][col]))
for col, v in enumerate(results[0])}
>> df = pd.DataFrame(data)
>> df.dtypes
0 int32
1 object
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We’ve covered several of the most popular loaders and their options
in this chapter, but there are still many more. Be sure to read the
documentation for the particular loader you are using and see what kinds
of normalization during load features are at your disposal, and if not, you
may have to write some custom code yourself. Keep in mind performance
savings can come from reducing memory overhead and reducing steps
during the load and normalization process. pandas provides many ways
of improving normalization and load performance depending on the
bottlenecks you have in your particular situation. In Chapter 5, we’ll
explore how to reshape the data into the desired DataFrame format once
it’s loaded and normalized.
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CHAPTER 5
Basic Data
Transformation
in pandas
The pandas library has a huge API that provides many ways of
transforming data. In this chapter, we’ll cover some of the most powerful
and most popular ways to transform data in pandas.
Pivot and pivot table
Pivot and pivot table are very popular and particularly attractive to
beginners because they are so powerful. However, their powerfulness
comes at a performance cost. While pivot is a great tool for initially
transforming the DataFrame as part of a data normalization step, it should
not be used frequently throughout the data analysis phase. Listing 5-1
shows an example of using pivot table to convert the raw inspection data
into an aggregated format of restaurants and their average inspection
score. Note in Listing 5-1 the aggregation function is explicitly specified as
np.mean though this is unnecessary since np.mean is the default. Pivot is
essentially doing a groupby, applying the aggregation function as needed,
and reorganizing the results into a new table format.

110
Listing 5-1. Calculating the average inspection score per restaurant
with pivot_table
>> df
restaurant location date score
Diner (4, 2) 02/18 90
Pandas (5, 4) 04/18 55
Diner (4, 2) 05/18 100
Pandas (5, 4) 01/18 76
>> df = df.pivot_table(
values=['score'],
index=["restaurant","location"],
aggfunc=np.mean
)
>> df
score
restaurant location
Diner (4, 2) 95
Pandas (5, 4) 66
There are a couple performance issues in Listing 5-1 . Pivot table does
not have an option to limit memory duplication so it creates an entirely new
DataFrame each time it is used. If your DataFrame is quite large, this can be a
big performance hit to your program. Internally, pivot table is grouping the data
by unique restaurant and location combinations which takes time, particularly
with a large amount of combinations. If this was being used as part of a data
normalization step, it would be far better than if it was used many times
throughout a program as part of data analysis. This is because the performance
hit of uniquely grouping and copying all that memory would happen only once
compared to it happening many times throughout the program. It is far better
to normalize and orient a DataFrame once in such a way that it optimizes all
the analysis you plan to perform on it than leave it in a somewhat unoptimized
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raw form and have to re-orient it at each analysis step. Note, if instead the
DataFrame was already uniquely grouped, we could run a groupby to calculate
the average score like in Listing 5-2 which would be twice as fast. We’ll discuss
the performance of groupby in depth in Chapter 7. It is very likely that other
analysis needs the data grouped by unique restaurant, so the grouping in this
example at the very least should be part of the normalization step, in which
case it becomes unnecessary to use pivot table at all.
Listing 5-2. Calculating the average inspection score per restaurant
with groupby
>> df
date score
restaurant location
Diner (4, 2) 02/18 90
04/18 55
Diner (4, 2) 05/18 100
01/18 76
>> df = df[["score"]].groupby(["restaurant","location"]).mean()
>> df
restaurant location score
Diner (4, 2) 95
Pandas (5, 4) 66
Pivot does the same thing as pivot table, but it does not allow you to
aggregate data. Any columns and index value combinations that result
in multiple values must be aggregated together when using pivot table.
Pivot, on the other hand, simply throws a ValueError if it runs into such a
scenario. Note in Listing 5-3 no combination of drug and date results in
multiple values; however, in Listing 5-4 , there are or would be multiple
rows for the same drug and date; thus, Listing 5-4 throws a ValueError.
So, a regrettable limitation of both pivot and pivot table is they do not
output data where there are multiple values for an index and column
combination. Pivot table forces you to aggregate the multiple values
together or select one and pivot simply throws a ValueError.
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Listing 5-3. Re-orienting a DataFrame using pivot
>> df
date tumor_size drug dose
02/18 90 01384 10
02/25 80 01384 10
03/07 65 01384 10
03/21 60 01384 10
02/18 30 01389 7
02/25 20 01389 7
03/07 25 01384 10
03/21 25 01389 7
>> df.pivot(
index="drug",
columns="date",
values="tumor_size"
)
date 02/18 02/25 03/07 03/21
drug
01384 90 80 65 60
01389 30 20 25 25
Listing 5-4. Pivot throws a ValueError when there are multiple
values for the same column-index combination
>> df
date tumor_size drug dose
02/18 90 01384 10
02/25 80 01384 10
03/07 65 01384 10
03/21 60 01384 10
02/18 30 01389 7
02/25 20 01389 7
03/07 25 01389 7
03/21 25 01389 7
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>> df.pivot(
index="drug",
columns="date",
values="tumor_size",
)
ValueError: Index contains duplicate entries, cannot reshape
Pivot is more performant than pivot table because it does not allow
specification and generation of multi-level columns and multi-indexes.
Thus, it does not have the overhead of generating and handling this
more complex DataFrame format. Regardless of whether the resulting
DataFrame is a multi-index or multi-level column DataFrame, pivot table
still runs the various computations as if it is multi-level which adds a
fair amount of overhead, up to six times more than pivot in some cases.
While pivot will allow you to specify multiple values and create a multi-
level column for them, it does not allow you to provide an explicit list of
columns to generate multi-level columns or provide a list of indexes to
generate multi-level indexes. Pivot table, on the other hand, supports this
type of multi-level DataFrame. It also has some other nicety options like
adding a subtotal of all rows and columns and dropping columns with
NaNs. In summary, if you can get away with using pivot, you should, as it’s
more performant than using pivot table.
Stack and unstack
Stack and unstack reshape a DataFrame’s column level into an innermost
index and vice versa. An example of this is shown in Listing 5-5 where each
column is a restaurant health inspection, the value is the health inspection
score, and the index represents the restaurant that was inspected. Stack
is used to reshape the data so that the health inspection scores for each
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restaurant occur across each row rather than each column. Note stack
converts the column names across the top into column values which then
are ultimately dropped from the DataFrame.
Listing 5-5. Reshaping a DataFrame so that each row represents an
inspection using stack
>> df
score
inspection 0 1
restaurant location
Diner (4, 2) 90 100
Pandas (5, 4) 55 76
>> df = df.stack().reset_index()
>> df
restaurant location inspection score
Diner (4, 2) 0 90
1 100
Pandas (5, 4) 0 55
1 76
>> df.drop(column=["inspection"], inplace=True)
>> df.set_index(["restaurant", "location"], inplace=True)
>> df
score
restaurant location
Diner (4, 2) 90
100
Pandas (5, 4) 55
76
You might recognize the shape of the original DataFrame in Listing 5-5
from Listing 3-22 . The shape of the DataFrame before it’s reshaped in
Listing 5-5 is the orientation that was deemed the most optimal in the
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“Choosing the right DataFrame” section at the end of Chapter 3. Listing 5-5
shows how to convert from that optimal shape to the original non-optimal
shape. Now let’s look at how we might take the original non-optimal
shape and turn it into the optimal shape. Listing 5-6 adds a new column
called inspection to the DataFrame whose values become the column
names for the new DataFrame. We also are making use of a handy groupby
aggregation function called cumcount that creates a row number for each
row in each group.
Listing 5-6. Reshaping a DataFrame so that each column is an
inspection using unstack
>> df
score
restaurant location
Diner (4, 2) 90
100
Pandas (5, 4) 55
76
>> df["inspection"] = df.groupby(
["restaurant", "location"]).cumcount()
>> df
inspection score
restaurant location
Diner (4, 2) 0 90
1 100
Pandas (5, 4) 0 55
1 76
>> df.set_index("inspection", append=True, inplace=True)
>> df
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score
restaurant location inspection
Diner (4, 2) 0 90
1 100
Pandas (5, 4) 0 55
1 76
>> df = df.unstack()
>> df
score
inspection 0 1
restaurant location
Diner (4, 2) 90 100
Pandas (5, 4) 55 76
So how performant are stack and unstack? They both duplicate memory
as they are not inplace operations which can be costly and thus should really
only be used in data normalization. They are, however, very unique in the
way they can transform the data, so it is difficult to find a more performant
alternative other than melt which is what we’ll explore in the next section.
Melt
An example of using melt is shown in Listing 5-7 . Note that this is very
similar to the stack example. We are essentially doing what we did in
approximately four lines with stack in one line in this example. While melt
does the same thing as stack and a bit more, it does it in a slightly more
performant way. This is mainly due to the slight overhead advantage it has in
not calling into all the various data transformations at a high level, meaning
rather than calling stack underneath, melt performs the lower- level data
manipulations underneath stack directly, thus avoiding the middle code
layers. If you compare a raw stack to melt, stack is about four times faster.
The drawback of using stack is that it often requires other manipulation such
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as setting an index, renaming columns, converting it back to a DataFrame,
and so on. This means in some cases it’s more performant to just use melt.
Listing 5-7. Reshaping a DataFrame so that each row represents an
inspection using melt
>> df
restaurant location 0 1
Diner (4, 2) 90 100
Pandas (5, 4) 55 76
>> df = df.melt(
id_vars=["restaurant","location"],
value_vars=[0,1],
value_name="score").drop(columns="variable")
>> df
restaurant location score
Diner (4, 2) 90
100
Pandas (5, 4) 55
76
Transpose
Transpose is a useful trick. It simply turns the columns into rows and the
rows into columns. In Listing 5-8 , there is a list of patients who need to be
treated for a certain disease and a table that provides a list of drugs used
to treat the disease based on blood type. We need to add the list of drugs
that can be used to treat the given patient into the patient table based on
the patient’s blood type. The first step is to index both the patient list and
the drug table by blood type, and then we can do a simple join to add the
drug data into the patient list. Because the drug table is oriented such that
the blood types are across the columns instead of the rows, we first do a
transpose. Note when we do this, the index which is provided by default
when creating the DataFrame turns into the columns and the columns
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turn directly into the index. This means in Listing 5-8 we don’t explicitly
have to set the index as the transpose already sets the index to the blood
type for us.
Listing 5-8. Using a transpose to reshape a DataFrame
>> patient_list
id history
blood_type
0+ 02394 hbp
B+ 02312 NaN
0- 23409 lbp
>> drug_table
index 0+ 0- A+ A- B+ B-
0 ADF ADF ACB DCB ACE BAB
1 GCB RAB DF EFR HEF
2 RAB
>> drug_table = drug_table.transpose(copy=False)
>> drug_table
blood_type 0 1 2
0+ ADF GCB RAB
0- ADF RAB
A+ ACB DF
A- DCB EFR
B+ ACE
B- BAB HEF
>> patient_list.join(drug_table)
id history 0 1 2
blood_type
0+ 02394 hbp ADF GCB RAB
B+ 02312 NaN ACE
0- 23409 lbp ADF RAB
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Transpose is one of the few DataFrame reshaping functions that has
an option of not duplicating data if possible named copy. That being
said, copy=False doesn’t necessarily mean the data is not duplicated, as
we’ll explain in more detail in Chapter 9. Whether the data is duplicated
or not depends on a multitude of factors which ultimately boil down to
whether the new shape of the data can reuse the underlying NumPy arrays
as is or whether new NumPy arrays must be created. Recall that NumPy
arrays must all be the same type. This means if the DataFrame you are
transposing has the same types for the rows and the columns, then it will
likely be able to reuse the existing NumPy arrays. If not, it will have to
duplicate memory and re-build them. This means transpose should really
be used only when it’s absolutely necessary and ideally as part of a data
normalization step.
The takeaway from looking at all these data transformation functions is
data transformation is quite costly and in an ideal program should happen
only during the normalization phase. It should be used sparingly during
data analysis. Chances are that if you find yourself having to do a lot of
transformations at each of the data analysis steps, you should re-think the
orientation of your normalized DataFrame. In the next chapter, we’ll look
at the apply method and explore when it should and should not be used as
well as more performant alternatives.
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CHAPTER 6
The apply Method
apply is one of the most incorrectly used functions in pandas. Chances are
if you are using it, you shouldn’t be. This is because apply “applies” the
function to each row or each column in the data set effectively breaking
one of the cardinal rules of using pandas: do not iterate over the data set.
In this chapter, we’ll explore when apply is the right choice and present
alternative solutions for when it’s not.
When not to use apply
For those comfortable with basic programing features, iteration is a familiar
way to manipulate data. We think to ourselves: I would like to run this
operation on every row or every column, and thus apply looks very friendly.
However, that way of organizing a problem is completely wrong in pandas.
Much of the same principles used in working with relational databases can
also be used when working in pandas. When you perform an operation
on data in a database, you don’t do it one row at a time but rather define a
range; the same is true in pandas. When operating on a data set, you define
all the elements you wish to operate on and then provide the operation. In
the simplest form, this might look like df[“col 1”] + df[“col 2”], and in a more
complex case, this might look like df.where(100 > df >= 90, “A”).
pandas has many built-in functions for performing data computational
operations. A comprehensive list is provided in the Appendix. These
computations often directly translate to a NumPy function, operating in C,
which makes these much more performant than their apply equivalents.
They are accessible directly off the pandas DataFrame and also the pandas
Series object (a column or row of a pandas DataFrame).

122
A simple example of apply is illustrated in Listing 6-1 . We pass it the
sum function, specify the axis we want to apply the function to, and we get
the sum of each row.
Listing 6-1. Example of using apply
>> df = pd.DataFrame([[4, 9],[6, 7]], columns=['A', 'B'])
>> df
A B
0 4 9
1 6 7
>> df.apply(np.sum, axis=1)
0 13
1 13
While the example in Listing 6-1 is simple and illustrates how to use
apply , the use case in which it is used is very wrong. It’s a textbook example
of when to not use apply as the np.sum function is a built-in off the
DataFrame itself and thus the built-in should be used as it’s much more
performant. But why is it so much more performant? Let’s explore that in
more detail.
The answer to the question of why the built-in pandas sum is so much
more performant than applying the NumPy sum to each row lies in where
the iteration over the rows takes place. The following loop in Listing 6-2 is
the underlying implementation of the pandas apply method.
Listing 6-2. M ain loop in the pandas apply implementation
for i, v in enumerate(series_gen):
results[i] = self.f(v)
keys.append(v.name)
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123
As you can see in Listing 6-2 , the looping over the rows takes place in
Python. Here you can see the series_gen which is either the columns or the
rows that the function to be applied (held in self.f ) will be applied to. This
is in opposition to the built-in pandas sum function that simply passes an
ndarray to be operated on to the NumPy sum function, which then iterates
and sums the data in C and returns the resulting ndarray back to Python.
This process of running the operation on the data in C instead of Python is
known as vectorization. Essentially, vectorization is able to achieve a huge
speedup over the alternative of running the operation in Python. For all
the reasons covered in Chapter 3, looping and performing operations in
C is much more performant than Python. However, the speedup doesn’t
always come from just looping in C.
Vectorized operations allow you to apply a mathematical operation to
a sequence of numbers. For example, if you want to add 4 to each element
in an ndarray, you specify that using the syntax arr + 4. In the case of
NumPy ufuncs (see the Appendix for a comprehensive list), they actually
make use of specialized vector registers in the CPU itself. Vector registers
are registers that can contain a series of values, and when an operation is
performed on them, it is performed on each value in the register at once.
So, what would have been a loop over an array of eight values and eight
consecutive add instructions in the CPU becomes one add instruction
operating on eight values in the CPU. As you can imagine, this leads
to a huge speedup. Vectorization will also pad arrays of mismatched
dimensions in order to make the dimensions match such that an operation
can run. This process is known as broadcasting. When you add a new
column in pandas via df[“new_col”] = 4, 4 is broadcast to have the same
number of rows as all the other columns in the DataFrame. Similarly,
aggregation functions like sum operate over a sequence of numbers using
vectorization. What all of this boils down to is apply is not a vectorized
operation—it loops in Python and should be avoided whenever possible.
It becomes effectively the same thing as iterating over the rows and
applying the function yourself as illustrated in Listing 6-3 .
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124
Listing 6-3. Equivalent of apply implemented manually
results = [0] *len(df)
for i, v in df.itterrows():
results[i] = v.sum()
df["sum"] = results
In fact, the performance of this custom implementation of apply
illustrated in Listing 6-3 vs. Listing 6-1 where apply is used directly yields
slightly better performance simply because there is less overhead to
get to the guts of the operation when implementing this simple custom
alternative.
How much slower is apply vs. using a built-in pandas operation though?
Let’s look at some concrete examples and compare the performance.
Comparing the performance of the apply example in Listing 6-1 to the
alternative method in Listing 6-4 when performed on 100,000 rows, the
apply function averages about 8.5 seconds compared to running the sum
function directly off the pandas DataFrame which averages about 0.4
milliseconds.
Listing 6-4. Alt ernative implementation of Listing 6-1
df.sum(axis=1)
Let’s look at another example. Say you have a data set with one column
named A but that column has incomplete data and you wish to replace
the values that are missing with the max of columns B and C. This could
be implemented using apply as demonstrated in Listing 6-5 , or this could
be implemented in a much more performant way using the where method
demonstrated in Listing 6-6 .
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125
Listing 6-5. Replacing missing data using apply
def replace_missing(series):
if np.isnan(series["A"]):
series["A"] = max(series["B"], series["C"])
return series
df = df.apply(replace_missing, axis=1)
Listing 6-6. Replacing missing data using the where method
df["A"].where(
~df["A"].isna(),
df[["B", "C"]].max(axis=1),
inplace=True,
)
The where method replaces falsey values with the value provided
in the second parameter. This means, in Listing 6-6 , all the NaN values
are being replaced with the max of columns B and C. Note we are also
specifying inplace=True so that this replace happens on the current
DataFrame as opposed to creating a new DataFrame that would result in
duplicated memory.
Let’s look at a trickier example in Listings 6-7 and 6-8 . Suppose you
have a DataFrame with two columns, fruit and order, and you want to drop
all the data where the fruit is not present in the order for each row. pandas
does have string operations including Series.str.find that will return True if
a substring is present in a string for each value in a Series. However, it will
only allow you to pass in a constant. In other words, you cannot specify a
Series of substrings but only a single string value, so find will not work in
this case. There is also no “in” check built into pandas that operates on
two series objects, so although this is exactly what we want, pandas does
not support it. This means we must implement some kind of customized
solution ourselves, so let’s explore the performance of various options.
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126
Listing 6-7. Dropping rows whose order column does not contain
the substring in the fruit column using apply
def test_fruit_in_order(series):
if (series["fruit"].lower() in
series["order"].lower()
):
return series
return np.nan
>> data = pd.DataFrame({
"fruit": ["orange", "lemon", "mango"],
"order": [
"I'd like an orange",
"Mango please.",
"May I have a mango?",
],
})
fruit order
0 orange I'd like an orange
1 lemon Mango please.
2 mango May I have a mango?
>> data.apply(
test_fruit_in_order,
axis=1,
result_type="reduce",
).dropna()
fruit order
0 orange I'd like an orange
2 mango May I have a mango?
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127
Listing 6-8. Solving Listing 6-7 using a list comprehension
mask = [fruit.lower() in order.lower()
for (fruit, order) in data[["fruit", "order"]].values]
data = data[mask]
Using apply to solve this problem as in Listing 6-7 takes about 14 seconds
on 100,000 rows, whereas using a list comprehension as in Listing 6-8 takes
about 100 milliseconds. But why is a list comprehension so much faster than
apply ? Don’t they both loop in Python? List comprehensions are specially
optimized loops within the Python interpreter. The bytecode that they
translate into more closely resembles a loop written in C as they do not load a
bunch of specialized Python list attributes. What follows is the bytecode for a
for loop (Listing 6-9 ) vs. a list comprehension (Listing 6-10 ). Notice how much
simpler and smaller the bytecode is for a list comprehension than for a for
loop even though they are doing the same thing.
Listing 6-9. Bytecode translation of a simple for loop
def for_loop():
l = []
for x in range(5):
l.append( x % 2 )
0 0 BUILD_LIST 0
2 STORE_FAST 0 (l)
1 4 SETUP_LOOP 30 (to 36)
6 LOAD_GLOBAL 0 (range)
8 LOAD_CONST 1 (5)
10 CALL_FUNCTION 1
12 GET_ITER
>> 14 FOR_ITER 18 (to 34)
16 STORE_FAST 1 (x)
2 18 LOAD_FAST 0 (l)
20 LOAD_METHOD 1 (append)
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128
22 LOAD_FAST 1 (x)
24 LOAD_CONST 2 (2)
26 BINARY_MODULO
28 CALL_METHOD 1
30 POP_TOP
32 JUMP_ABSOLUTE 14
>> 34 POP_BLOCK
>> 36 LOAD_CONST 0 (None)
38 RETURN_VALUE None
Listing 6-10. Bytecode translation of a list comprehension
def list_comprehension():
l = [x % 2 for x in range(5)]
0 0 LOAD_CONST 1
2 LOAD_CONST 2
4 MAKE_FUNCTION 0
6 LOAD_GLOBAL 0 (range)
8 LOAD_CONST 3 (5)
10 CALL_FUNCTION 1
12 GET_ITER
14 CALL_FUNCTION 1
16 STORE_FAST 0 (l)
18 LOAD_CONST 0 (None)
20 RETURN_VALUE None
When to use apply
So far, we’ve looked at some examples that don’t warrant the use of apply .
Let’s take a look at one that does. Often the reality of working with data
in the wild results in some much more complex scenarios. Say you want
to calculate the percentile of score for each element in a DataFrame, the
implementation of which is provided in Listing 6-11 .
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129
Listing 6-11. Im plementation of scipy.stats.percentileofscore
def percentileofscore(a, score):
"""
Three-quarters of the given values lie below a given score:
>>> stats.percentileofscore([1, 2, 3, 4], 3)
75.0
With multiple matches, note how the scores of the two
matches, 0.6 and 0.8 respectively, are averaged:
>>> stats.percentileofscore([1, 2, 3, 3, 4], 3)
70.0
"""
n = len(a)
left = np.count_nonzero(a < score)
right = np.count_nonzero(a <= score)
pct = (right + left + (1 if right > left else 0)) * 50.0/n
return pct
This means if we had the following input DataFrame, we would see the
following output DataFrame after applying scipy.stats.percentileofscore
using the pandas apply function (Listing 6-12 ).
Listing 6-12. Applying scipy.percentileofscore to each element in a
DataFrame
>> from scipy import stats
>> data = pd.DataFrame(np.arange(20).reshape(4,5))
0 1 2 3 4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
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130
>> def apply_percentileofscore(series):
return series.apply(
lambda x:stats.percentileofscore(series,x)
)
>> data.apply(apply_percentileofscore, axis=1)
0 1 2 3 4
0 20.0 40.0 60.0 80.0 100.0
1 20.0 40.0 60.0 80.0 100.0
2 20.0 40.0 60.0 80.0 100.0
3 20.0 40.0 60.0 80.0 100.0
This is a fairly complicated use case and a very non-performant
implementation since it calls the apply method twice for each row.
Unfortunately, there is no built-in pandas function that matches the
behavior of SciPy’s percentileofscore function being applied to each
element like we need to do in this example. While we could do this
calculation individually on the DataFrame one column at a time and
piece the results back together, that would be a very cumbersome
implementation. Listing 6-13 demonstrates this approach.
Listing 6-13. A mor e performant implementation of Listing 6-12
def percentileofscore(df):
res_df = pd.DataFrame({})
for col in df.columns:
score = pd.DataFrame([df[col]] *5, index=df.columns).T
left = df[df < score].count(axis=1)
right = df[df <= score].count(axis=1)
right_is_greater = (
df[df <= score].count(axis=1)
> df[df < score].count(axis=1)
).astype(int)
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res_df[f'res{col}'] = (
left + right + right_is_greater
) * 50.0 / len(df.columns)
return res_df
percentileofscore(data)
Listing 6-13 ’s implementation results in better performance since we
have eliminated one of the loops in Python (the loop over the rows) by
doing pandas operations on all the rows at once. However, we were also
forced to create a duplicate DataFrame where all columns are populated
with the score in order to achieve these row-wise operations which
results in undesirable memory overhead. Note we are also not reusing
the implementation from SciPy but have re-implemented it using pandas
operations, which is less than ideal for readability and increases the
complexity and possibly the fragility of implementation. Fortunately, there
is yet another way to implement this as we’ll discuss in the next section.
Improving performance of apply using
Cython
Taking a lesson from the previous example of a simple summation, if the
pandas developers had provided this function for us off the DataFrame,
it would have been implemented in C. So why don’t we just implement
it in C ourselves? You might be saying to yourself, “I don’t know C—that
sounds hard.” But, in fact, the Cython library makes it quite easy, and
you don’t need to know C syntax to do it! Cython lets you write Python
and compile it into C to be used as a C extension. First, we need to write
our percentileofscore function that will operate on the entire DataFrame
as shown in Listing 6-14 . Then, compile it as shown in Listing 6-15 , and
finally we can use it as shown in Listing 6-16 .
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Listing 6-14. A mor e performant implementation of Listing 6-12
using Cython
from scipy.stats import percentileofscore as pctofscore
from copy import deepcopy
def percentileofscore(values):
percentiles = [0] *len(values[0])
num_rows = len(values)
for row_index in range(num_rows):
row_vals = values[row_index]
for col_index, col_val in enumerate(row_vals):
percentiles[col_index] = \
pctofscore(row_vals, col_val)
values[row_index] = percentiles
Listing 6-15. setup.py for compiling Cython in Listing 6-14
import pyximport; pyximport.install(language_level=3)
from distutils.core import setup
from Cython.Build import cythonize
setup(
ext_modules = cythonize("percentileofscore.pyx")
)
>> python setup.py build_ext --inplace
Listing 6-16. Using the compiled Cython function implemented in
Listing 6-14
from percentileofscore import percentileofscore
percentileofscore(data.values)
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Note that the Cython function accepts values and not the full
pandas DataFrame; this is because values is a two-dimensional array
and something that is easily translatable into C, whereas the pandas
DataFrame is a Python object and is not. Also note that the function
modifies the data in place as opposed to returning a whole new two-
dimens ional array. This is a performance benefit as we do not have to
allocate new memory for the new array, and once the data has been
converted, we no longer need the original data set (at least in this
particular case).
So how different is the performance of these approaches when
run over 100,000 rows? Using apply in Listing 6-12 averages around
58 seconds. Using pandas operations to effectively re-implement the
SciPy equivalent as in Listing 6-13 averages around 24 seconds. The
third approach of building a custom Cython function averages around
4 seconds. There are also other advantages of going with the Cython
approach other than performance. The SciPy function could be used
as is and did not have to be re-implemented, so from an effort of
implementation and readability perspective, it looks very appealing as
well.
In conclusion, only when all other options have been exhausted
should apply be used. It is equivalent in performance to iterrows and
itercolumns and should be treated with the same level of precaution. In
cases where apply needs to be used over a large data set and is causing a
second or more slowdown, a customized Cython apply equivalent should
be implemented instead so as to not degrade data analysis performance.
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135 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_7
CHAPTER 7
Groupby
Chances are at some point when working with data in pandas, you will
need to do some sort of grouping and aggregation of data. This is what
Groupby is for. It allows you to cluster your data into groups and run
aggregated calculations on those groups.
Using groupby correctly
When starting out, you may be inclined to do something like Listing 7-1
where you cluster your data into groups, then loop over each group, and
run some aggregate. This however results in terrible performance because
just as we saw in Chapter 6, we are looping in Python and not in C. If
instead you call the aggregate function directly off the groupby as in
Listing 7-2 , the groups will be passed into the aggregate function and the
looping will occur in C.
Listing 7-1. Calculating total number of arrivals to each destination
per year by looping over groups
>> arrivals_by_destination
number
date place
2015 LON 10
2015 BER 20
2015 LON 5

136
2016 LON 10
2016 BER 15
2016 BER 10
>> groups = arrivals_by_destination.groupby(["date","place"])
>> for idx, grp in groups:
arrivals_by_destination.loc[idx, "total"] = \
grp["number"].sum()
>> arrivals_by_destination
number total
date place
2015 LON 10 15
2015 BER 20 20
2015 LON 5 15
2016 LON 10 10
2016 BER 15 25
2016 BER 10 25
Listing 7-2. Calculating total number of arrivals to each destination
per year using groupby
>> arrivals_by_destination
number
date place
2015 LON 10
2015 BER 20
2015 LON 5
2016 LON 10
2016 BER 15
2016 BER 10
>> arrivals_by_destination["total"] = \
arrivals_by_destination.groupby(["date","place"]).sum()
>> arrivals_by_destination
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137
number total
date place
2015 LON 10 15
2015 BER 20 20
2015 LON 5 15
2016 LON 10 10
2016 BER 15 25
2016 BER 10 25
The difference in performance between Listing 7-1 and Listing 7-2 is
proportional to the number of groups. With just 8 groups, the performance
of Listing 7-1 is twice as slow as Listing 7-2 , and with 16 groups, it’s
four times as slow. Note in both these examples, we are starting with a
pre-indexed DataFrame. This means the groups have already been pre-
com puted so the groupby does not have to calculate all the groups again
but just reuse the existing groups in the index. This is a huge timesaver,
particularly if you are going to be doing a lot of groupby operations over
the columns in the index.
You may encounter a groupby scenario where you need a custom
function that isn’t built-in off the groupby object. This, however, is not the
time to give in to looping. What you are implementing when you loop over
the groups is the same performance as if you called apply on the groupby
object itself and passed in your custom function. If you find yourself in a
situation like this, consult Chapter 6 on apply and implement your custom
function in Cython.
Indexing
Working with a sorted index provides a substantial speedup when
there are many different values in each index. You may encounter the
warning “PerformanceWarning: indexing past lexsort depth may impact
performance.” This is referring to the number of levels in an index that are
sorted lexically or alphabetically.
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When accessing an unsorted index, pandas has O(n) performance
since it has to search the entire index for the index value as is demonstrated
in Figure  7-1 . When accessing a sorted index, pandas has O(log(n))
performance as it uses binary search to find the index value such as in
Figure  7-2 . When the index is unique, it uses a hash lookup which has
O(1) performance as in Figure  7-3 . This can make a huge difference when
n, the number of values in the index, is very large which is why unique
indexes result in the fastest performance. It’s worth noting that just as is the
case in Listing 7-2 , a unique index cannot always be achieved, so the best
performance we can get in those scenarios is with a sorted multi-index.
Figure 7-1. Unsorted index access O(n)
Figure 7-2. Sorted index access O(log(n))
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139
Avoiding groupby
So far, we’ve explored how to get the best performance when running a
groupby operation. Sometimes, however, the most performant option is to
not use a groupby at all. If you find yourself having to do a lot of groupby
operations on your DataFrame, you may consider re-orienting your
DataFrame so that you don’t need to use groupby. Since groupby groups
the data and then runs an aggregate function on each group of data, it is
essentially doing a loop over the number of groups. Even though in the
most performant case the groups are already pre-computed, the indexes
are fast to access, and the looping is run at the C level, all of that still takes
time. It’s much more performant in pandas to run simple row-wise or
column-wise operations.
Let’s take a look at how we can reformat the DataFrame in Listing 7-3
so that we can avoid using groupby. If we keep the index columns where
they are but instead break out the multiple values for each index across the
row, we can do two things to optimize this sum by groups operation. The
first thing this does is eliminate the groupby sum operation and turn it into
a simple sum across the columns. The second thing this does is make the
indexes unique. Note we are taking on some additional memory overhead
by doing this as the gaps in the data will be filled with zeros. Integers,
however, take up little space even in a very large DataFrame so the overall
performance speedup is worth the additional memory usage.
Figure 7-3. Unique index access O(1)
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140
Listing 7-3. Calculating total number of arrivals to each destination
without using groupby
>> arrivals_by_destination
number 0 1
date place
2015 BER 20 0
2015 LON 10 5
2016 BER 15 10
2016 LON 10 0
>> arrivals_by_destination["total"] = \
arrivals_by_destination.sum()
Listing 7-2 is approximately 8 times slower than Listing 7-3 , whereas
Listing 7-1 is 25 times slower. This is why it’s so important to carefully
select the DataFrame format that best suits the operations you plan to
perform on it. It can literally save you minutes of execution time.
We’ve looked at how to use groupby most efficiently, with pre-indexing
and using an aggregate function directly off the groupby object instead
of looping. We’ve also covered how to reformat your DataFrame so that
you don’t need to use a groupby and can use a more performant row- or
column-wise operation. Unfortunately, there isn’t one easy catch-all
DataFrame format or groupby method that can be applied to all use cases.
But you should now have an understanding and a repertoire of methods to
help you solve your particular groupby problem in the most efficient and
simplest way possible.
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141 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2_8
CHAPTER 8
Performance
Improvements Beyond
pandas
You may have heard another pandas user mention using eval and query to
speed up evaluation of expressions in pandas. While use of these functions
can speed up evaluation of expressions, it cannot do it without the help
of a very important library: NumExpr. Use of these functions without
installing NumExpr can actually cause a performance hit. In order to
understand how NumExpr is able to speed up calculations however, we
need to take a deep dive into the architecture of a computer.
Computer architecture
CPUs are broken up into multiple cores where each core has a dedicated
cache. Each core evaluates one instruction at a time. These instructions are
very basic compared to what you might see in a Python program. One line
of Python is often broken up into many CPU instructions. Some examples
include loading data such as storing an array value into a temporary
variable when looping, jumping to a new instruction location such as
when calling a function, and an evaluation expression such as adding two
values together.

142
In modern cores, the evaluation is split up into many phases. These
multiple phases are called a pipeline, where each instruction evaluation is
piped through a series of phases until the evaluation is complete. Modern Intel
CPUs are typically broken up into about 15 pipeline phases. Figure  8- 1 shows
an example of a simple five-phase pipeline processor. First, the instruction is
fetched typically from a dedicated instruction cache, or if not present, it must
be fetched from a farther cache or main memory. Then the instruction is
decoded. In the decoding phase, each instruction has a particular numerical
code that decodes to a certain type of instruction and results in certain
behavior so the decode phase is responsible for decoding the instruction and
gathering the data from the registers (you can think of these as a very small
dedicated memory cache) to pass to the execution phase. In the execution
phase, the instruction is actually run; this may mean two values are added
together, or if it’s a load instruction, simply a memory address is passed to
the next phase. Since an instruction may be a jump to a different memory
location rather than a sequential location, part of the evaluation phase is also
determining the next instruction to send through the pipeline. In the memory
access phase, any data that needs to be loaded from memory into a register
Figure 8-1. Five-phase pipeline architecture
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for an upcoming instruction is fetched and then loaded into a register in the
writeback phase. This means that if you wish to add two values together, those
values must first be loaded with a load instruction into two different registers
before an add instruction can be run. So, the line of Python code in Listing 8-1
consists of three instructions inside the CPU.
Listing 8-1. Converting a line of Python code into pseudo-code
CPU instructions
a = b + c load b
load c
add a, b, c
While the CPU instructions in Listing 8-1 may look similar to Python
bytecode, it’s important to note that they are not the same. Remember
that bytecode is run on the Python Virtual Machine, whereas CPU
instructions are run on the CPU. While you can use the dis module (dis
standing for disassembly) to output the bytecode and it may give you
some idea of what the machine code might look like, it is not machine
code. The Python Virtual Machine contains a giant switch statement
that translates a bytecode instruction into a function call which then
executes CPU instructions. So, while we may think of Python as being an
interpreted language that runs bytecode instructions in a software virtual
machine, the fact is at some point that add instruction makes its way to
the CPU. Eventually that add becomes a series of CPU instructions that are
shown in Listing 8-1 .
It’s very common for the memory access phase of the instruction pipeline
to take longer than all the other pipeline phases. Rather than making all the
other pipeline phases as slow as the memory access phase or inserting NOPs
(commonly called no ops or no operations), other instructions not dependent
on the data being loaded are used to fill the time. This enables the processor
to keep evaluating instructions even though one phase of the instruction may
take hundreds of cycles to complete. Compilers also play a part in keeping the
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processor busy during long instruction loads by re-ordering instructions so that
instructions not dependent on a memory load occur between a memory load
instruction and the next instruction that is dependent on that memory having
loaded. In modern Intel processors, re-ordering can occur inside the CPU as
well. Of course, sometimes there are no instructions to fill the gap and so NOPs
are used as a last resort. This ensures that the core’s instruction throughput is still
as close to one instruction per clock cycle (phase) as possible so that you don’t
have to wait hundreds of cycles for a memory load to complete.
The takeaway here is in order to make efficient use of your CPU and get
the highest instruction throughput possible you must make sure that your
data is loaded before you use it and that you give it enough computations
to do in between the data you are operating on and the next data you need.
So far, we’ve covered how the CPU operates on a low level to evaluate
instructions and how it works to achieve the best performance possible;
now let’s take a deeper look at the memory access phase and why it’s often a
bottleneck. Figure  8-2 shows a typical cache architecture of a modern Intel
CPU. Each core has a dedicated level 1 data, level 1 instruction, and a level
2 cache. All cores share the level 3 cache and main memory. Each of these
caches is placed farther and farther from the core on the board itself. While
core speeds are closely correlated to transistor size and speed, memory
speeds are correlated to how physically close they are to the core on the board.
Figure 8-2. Cache architecture
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When working with large data sets as is the expected use case in pandas,
all of that data cannot be stored inside the cache. It typically takes about three
clock cycles or instruction phases to access level 1 cache and at each level
exponentially increases in latency. To access the level 3 cache, it takes about
21 clock cycles, and if the data we wish to load is not in any of the caches and
it has to go all the way to main memory to load it, it takes anywhere from 150
to 400 clock cycles. At around 21 clock cycles, the performance hit incurred
by accessing the level 3 cache will likely exceed the number of pipelines in
our core. If we have to delay the instructions in our pipeline until the data
is retrieved without re-ordering to pad the delay, that could stall our entire
program for 21 clock cycles. 21 clock cycles of delay on a 4 GHz processor
is about 5.25 ns. This might seem insignificant and it is if we only incur this
delay a couple times in our program. However, keep in mind we are typically
operating on megabytes of data in pandas, and since not all of that is going to
fit in the caches, we will likely incur many performance hits like this. In fact,
we’re even more likely to incur larger performance hits all the way out to main
memory if running an operation over the entire data set.
Caches are generally designed for the best performance of the
average case. In software this means things like looping over arrays which
are sequential data structures. Because of this, when they have to load
something into the cache, they load sequential blocks of memory at a
time called cache lines. This helps to offset the performance hit of loading
something into the cache. The idea is typically programs operate on
sequential memory, so by loading the memory that follows the memory
the core needs right now, it will save needing to load that memory later.
In order to make the most effective use of caching, data that is located
sequentially or close together in memory should be repeatedly referred to in
a short time span. Sequential data will result in less cache loads. Repeatedly
referring to the same data in a short time span will prevent new data from
bumping the older data out of the cache and causing a cache miss that will
require the same data to be loaded into the cache again. Arrays, as we learned
in Chapter 3, are sequential data types, meaning the first element occurs at
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146
address A and the last element occurs at A plus the length of the array. When
you create a bunch of objects in memory with many attributes that point to
other objects and reference those attributes, each object has an address that
is not sequential, and thus you will not be able to utilize your cache as you will
be loading a bunch of different cache lines from a bunch of different memory
locations. Figure  8-3 demonstrates these two types of memory accesses.
Figure 8-3. Sequential memory access vs. object attribute access
over time
How NumExpr improves performance
NumExpr improves performance of pandas by running calculations on
subsets of the pandas DataFrame that are the size of the cache. Take the
example ( A + B ) ∗ 3 where A and B are pandas DataFrames. Without
NumExpr, each row of A + B would be added together, stored into a
temporary structure, and then multiplied by 3. With NumExpr, the first n
number of rows that fit inside the cache are added together and multiplied
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by 3 before moving to the next n rows. In this way, NumExpr is able to
reduce the number of memory loads and stores which we learned in the
“Computer architecture” section were the bottlenecks of the CPU and thus
a computer program. Listing 8-2 demonstrates this.
Note the cache in Listing 8-2 is three cache lines (enough to hold 64 rows
of A, B, and C data). While this is much smaller than a real-world level 1
cache which can generally hold around 128 cache lines, it simplifies the
example. This means that after computing the result of A + B for 64 rows, the
result must be stored back out to memory in order to make room for the next
64 rows of A and B and their result. Note that by using NumExpr’s method
of running the computation over cache-sized chunks, we have reduced
the number of loads and the number of stores. Also note the example in
Listing 8-2 is written as in-order pseudo-code CPU instructions (i.e., these
are not the actual instructions that would execute inside the core, and
they would most likely be re-ordered in the real world in order to offset the
memory load delay as discussed in the “Computer architecture” section).
Listing 8-2. CP U pseudo-code instructions during evaluation of a
pandas expression with and without NumExpr
# NumExpr is not installed. # NumExpr is installed.
C = ( A + B ) * 3 C = pd.eval("( A + B ) * 3")
load A[0:64] load A[0:64]
load B[0:64] load B[0:64]
add C[0], A[0], B[0] add C[0], A[0], B[0]
mult C[0], C[0], 3
add C[1], A[1], B[1] add C[1], A[1], B[1]
mult C[1], C[1], 3
.
.
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add C[63], A[63], B[63] add C[63], A[63], B[63]
mult C[63], C[63], 3
store C[0:64] store C[0:64]
load A[64:128] load A[64:128]
load B[64:128] load B[64:128]
add C[64], A[64], B[64] add C[64], A[64], B[64]
mult C[64], C[64], 3
add C[65], A[65], B[65] add C[65], A[65], B[65]
mult C[65], C[65], 3
.
.
add C[127], A[127], B[127] add C[127], A[127], B[127]
mult C[127], C[127], 3
store C[64:128] store C[64:128]
load C[0:64]
mult C[0], C[0], 3
mult C[1], C[1], 3
.
.
mult C[3], C[63], 3
store C[0:64]
load C[64:128]
mult C[64], C[64], 3
.
.
mult [127], C[127], 3
store C[64:128]
Note that in order to run an evaluation like this all at once on chunks
of the pandas DataFrame(s), we must communicate to NumExpr the
whole expression prior to computation. (A + B) ∗ 3 must be specified in
such a way that NumExpr knows it can be combined together.
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This is where query and eval come in.eval allows you to specify a complex
expression as a string to signal NumExpr that it can be run on a chunk of
the DataFrame(s) at a time. query is effectively another form of eval as it
calls eval underneath.
Depending on the computation, the shape and size of the data, the
operating system, and the hardware you are using, you may find that
using NumExpr and eval actually results in a significant performance
degradation. It’s always good to run a performance comparison before
blindly combining computations into an eval or query. NumExpr really
only works well for computations that exceed the size of the level 3 cache.
Typically, this is greater than 256,000 array elements. As we’ve seen with
other pandas functions, it also requires the data type and computation
be easily translatable into C. So, for example, datetimes will not yield a
performance improvement as they cannot be evaluated in NumExpr.
It’s also worth noting that using NumExpr directly can be much more
performant than using eval or query in pandas. Listing 8-3 demonstrates
such an example.
Listing 8-3. An exam ple where eval is slower than the typical
pandas syntax with NumExpr
import pandas as pd
import numpy as np
import numexpr as ne
nrows, ncols = 1000000, 1
df1, df2, df3, df4 = [pd.DataFrame(
np.random.randn(nrows, ncols)) for _ in range(4)]
# Calculate the sum using normal syntax.
df_sum1 = df1 + df2 + df3 + df4
# Calculate the sum using eval so that NumExpr optimizes it.
df_sum2 = pd.eval("df1 + df2 + df3 + df4")
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# Calculate the sum using NumExpr directly.
a1, a2, a3, a4 = (
df1.to_numpy(), df2.to_numpy(),
df3.to_numpy(), df4.to_numpy()
)
df_sum3 = ne.evaluate("a1 + a2 + a3 + a4")
The calculation of df_sum1 is twice as fast as df_sum2. This is generally
the opposite of what we would expect as df_sum2 is being calculated using
NumExpr. However, if we use NumExpr directly instead of going through
pandas eval, we find that df_sum3 is about four times faster than df_sum1.
This is due to the slowdown incurred inside the pandas eval function itself.
Inside eval, it wraps up the environment into a dictionary of local and
global variables and makes them accessible to NumExpr. This includes
converting the DataFrames to NumPy arrays. All of this takes a significant
amount of overhead. So much so that it actually ends up being slower than
not using eval and NumExpr. Very often, as is the case here, it’s much faster
to convert the DataFrames explicitly to NumPy arrays and call NumExpr
explicitly on those converted DataFrames.
Now that we’ve looked at how NumExpr improves pandas
performance of combined computations at the hardware level, let’s look
at how another one of NumPy and NumExpr’s dependencies, BLAS, takes
advantage of the hardware to optimize its computations.
BLAS and LAPACK
NumPy uses Basic Linear Algebra Subprograms (BLAS) underneath to
implement very performant linear algebra operations such as matrix
multiplication and vector addition. These subprograms are typically
written in assembly—a very low-level and performant language that
closely resembles CPU instructions. The Linear Algebra Package (LAPACK)
provides routines for solving linear equations. It is typically written in
Fortran and, just like NumPy, calls into BLAS underneath. There are many
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implementations of BLAS and LAPACK available such as the Netlib BLAS
and LAPACK, OpenBLAS, Intel MKL, Atlas, BLIS, and so on, each with their
own pros and cons. But, let’s explore more deeply how BLAS improves the
performance of pandas operations.
BLAS optimizes matrix operations by using Single Instruction Multiple
Data (SIMD) instructions that make use of vector registers in the hardware.
All CPUs have registers that hold the data that a CPU instruction needs to
operate on. Vector registers are just a special type of those registers. They
allow storage of multiple pieces of data in a single register, and when an
operation is run on the data, it is run on each piece of data in the register
at once. The advantage of SIMD instructions is that you can load a bunch
of data into a register and run the same operation on it concurrently rather
than having to run the same operation on each element consecutively. By
using a SIMD instruction, we reduce the number of CPU clock cycles that
it takes to complete the computation. For example, if a vector register is
able to hold four data elements, then we have reduced the number of clock
cycles from four to one. This means if you have a dot product of y and x as
in Listing 8-4 , then you can specify it as a series of SIMD instructions as in
Listing 8-5 . Note the y and x data is first loaded into the vector registers r1
and r2, and then the dot product is computed and stored in register r1.
Listing 8-4. Dot product

Listing 8-5. Dot product as SIMD pseudo-instruction
load vr2, Y1, Y2, Y3, Y4
load vr1, X1, X2, X3, X4
dot r1, vr2, vr1
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We’ve left out one important detail here which is that typically data
can only be loaded into vector registers if it is sequential in memory. This
poses a slight problem for most complex vector operations which typically
happen on rows of one matrix and columns of another matrix or vice
versa. BLAS is the opposite of Python in that its arrays are column majored
instead of row majored. BLAS also does not have two-dimensional
arrays—they are stored as a single-dimensional array. Listing 8-6 shows an
example of a Python array and how it would be stored in BLAS.
Listing 8-6. Comparison of a matrix representation in Python vs.
BLAS
Python BLAS
y = [[1, 2], [3, 4]] y = [1, 3, 2, 4]
y[row][col] y[col * num_cols + row]
So, going back to the dot product example in Listing 8-5 , because
these arrays are both represented as a single-dimensional array, despite
one being a bunch of rows and the other being a bunch of columns, they
both have contiguous memory addresses and so they both can be loaded
into the vector registers. This issue of consecutive memory addresses only
comes into play when working with more complex matrices and more
complex operations, so let’s look at a more complex example.
There are many ways to perform a matrix multiply. One way, using dot
product is shown in Figure  8-4 . Taking the dot product of the first matrix’s
row with the second matrix’s column will yield the value of a single
element in the result of the matrix multiply.
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In Figure  8-4 , we now run into a situation where loading a row of
the first matrix into a vector register is not possible as the memory is not
contiguous. Note, however, if we transpose the matrix so that the rows
become the columns, then the memory is contiguous and we can load the
row of the first matrix into a vector register.
But what happens if the matrix is very large? If we transposed a 1000
by 1000 matrix, it would not all fit in the cache and it would result in huge
delays when it had to go out to main memory to write and read the data.
BLAS optimizes for this by breaking up the matrix data into blocks just like
NumExpr. An example is shown in Figure  8-5 . By doing this, BLAS is able to
keep the transposed matrix all in the cache and also reuse that placeholder
transpose buffer on each block. This is advantageous not just for keeping
the transposed buffer in the cache but also because it doesn’t have to keep
re-allocating a new buffer for each block. It simply overwrites the buffer of
the previous block with the new transposed data for the current block.
Figure 8-4. Matrix multiplication using dot product
Figure 8-5. Breaking up a large matrix into blocks
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By breaking up the problem into blocks, BLAS is also able to take
advantage of multiple cores. It can run each of the blocks on a different
core which also saves time.
Another technique BLAS uses to speed up the computation is loop
unrolling. This is when you convert a loop into a series of repeated
instructions. Loop unrolling removes the need to predict branches and
potentially incur a branch prediction penalty for mispredicting a branch.
Recall that in the data pipeline instructions are loaded and processed
potentially before the result of a conditional instruction check occurs. So,
the hardware tries its best to correctly predict the result of that conditional
and which branch will be taken before it knows for certain. Loop unrolling
also avoids having to jump the instruction pointer to a new location in
instruction memory which potentially avoids cache misses and having to
go out to main memory to load instructions that aren’t in the instruction
cache. It also avoids the conditional instruction check at the beginning of
each iteration which saves CPU cycles. By unrolling a loop, you may also be
able to re-order computations such that the computations that use the same
memory are placed close together, thus reducing register load instructions.
In summary, BLAS uses a lot of techniques to improve performance
of linear algebra operations: SIMD instructions, blocking, loop unrolling,
threading, and so on. There are also many implementations of BLAS
available, and choosing a more performant option for certain types of
pandas programs may have a huge impact.
If you find yourself doing a lot of linear algebra operations with pandas,
you may consider switching to a more performant BLAS implementation.
np.config_show() will show you what BLAS implementation NumPy is
currently using. The Netlib BLAS implementation does not fully support
multiple cores and tends to be much less performant than the alternatives.
Other implementations like OpenBLAS fully support multiple cores and
are open source and freely available. As of Anaconda 2.5 or later, Intel MKL
is the default BLAS library and, though proprietary and large, is highly
optimized and available for free.
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By ensuring NumPy is using optimized dependencies NumExpr
and BLAS, you can significantly improve performance of certain pandas
operations. These libraries optimize operations to the particular hardware
you are running on to ensure you are getting the best performance
possible. But be mindful of when they will and will not boost performance.
In the final chapter, we’ll look at the future of pandas 1.0 and beyond.
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CHAPTER 9
The Future of pandas
There are an increasing number of packages that are built on top of or
compatible with pandas. Some of these like sklearn-pandas integrate
with other packages like scikit-learn to utilize DataFrames for machine
learning. Others, like Plotly, provide interactive plotting capabilities and
online collaboration. pandas has recently made the push in the last couple
years to branch out into other languages. There is now a pandas.js package
and a ruby wrapper that enables ruby users to call into the Python pandas
API. There is also a push to optimize data analysis at a more global scale
using an up-and-coming LLVM called Weld. It takes an approach similar
to NumExpr but on an even larger scale. The idea is to combine all the
data analysis operations together lazily and only run them when an actual
result is needed. This allows the operations to be optimized for parallel
compute and loading of memory on a much grander scale.
pandas 1.0
The pandas community has been feverishly working on pandas 1.0, the
first big upgrade since the initial release of pandas. It addresses a lot of the
shortcomings in previous versions.
pandas 1.0 adds a new pandas specific NA type. This new type will
make null values consistent across all types of columns. As you may recall
from Chapter 4, NaNs in pandas 0.25 must be stored as floats; they cannot
be Booleans, integers, or strings. Previously, it was not possible to load
a column with NaNs in it as an integer type—you had to convert it to an

158
integer after it was loaded. Now with pandas 1.0, it’s possible to load a
column with NaNs as an integer type. Listing 9-1 is the same example
presented earlier in the text in Listing 4-15 ; only now it’s making use of the
new nullable integer type available in pandas 1.0. Note the memory usage
of this new type takes up one more byte than is indicated by the data type.
So, while the type is set to an Int16Dtype, each element actually takes up
three bytes instead of two. The extra byte corresponds to a Boolean mask
in the IntegerArray implementation which marks which values are NA.
Listing 9-1. Example of how pandas handles NaNs in the data in 1.0
>> data = io.StringIO(
"""
id,age,height,weight
129237,32,5.4,126
123083,20,6.1,
123087,25,4.5,unknown
"""
)
>> df = pd.read_csv(
data.
dtype={
'id': np.int32,
'age': np.int8,
'height': np.float16,
'weight': pd.Int16Dtype()},
na_values=["unknown"],
index_col=[0],
)
age height weight
id
129237 32 5.4 126
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123083 20 6.1
123087 25 4.5
>> df.memory_usage(deep=True)
Index 24
age 3
height 6
weight 9
>> df.dtypes
age int8
height float16
weight Int16
>> df.index.dtype
dtype('int64')
With the introduction of pd.NA and the addition of nullable Boolean
arrays and a dedicated string data type, the null values can be consistently
represented using the same type across all data types. This seemingly
simple change also improves subtle inconsistencies across the API due to
having an inconsistent null type, for example, Categorical.min now returns
the expected minimum value instead of NaN as shown in Listing 9-2 .
Listing 9-2. Behavior change of pandas 1.0 when computing the
minimum of a Categorical
>> pd.Categorical([1, 3, 5, np.nan], ordered=True).min()
NaN
>> pd.Categorical([1, 3, 5, pd.NA], ordered=True).min() # 1.0
1
The introduction of a dedicated string data type (StringDtype) is also huge
in itself. In pandas 0.25, strings were stored as objects which take up a lot of
space and do not guarantee data consistency because an object can hold any
type. With the new explicit StringDtype, it will take up less space, guarantee
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consistency within the column, and also identify as a text type rather than
lumping text values in with all values that are of the generic object container
type. Listing 9-3 demonstrates how much less memory the new pandas string
type uses. When using the new string type, each value takes up only 8 bytes,
which is a huge decrease in memory compared to previous versions where
each object value took up about 60 bytes.
Listing 9-3. M emory usage of the pandas 1.0 string type compared
to using object in previous versions
>> data = io.StringIO(
"""
id,name
129237,Mary
123083,Lacey
123087,Bob
"""
)
>> # Load the data with pandas 0.25.3.
>> df = pd.read_csv(
data,
dtype={'id': np.int32},
index_col=[0],
)
name
id
129237 Mary
123083 Lacy
123083 Bob
>> df.memory_usage(deep=True)
Index 24
name 197
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>> df.dtypes
name object
>> # Load the data with pandas 1.0.
>> df = pd.read_csv(
data,
dtype={
'id': np.int32,
'name': pd.StringDtype()},
index_col=[0],
)
name
id
129237 Mary
123083 Lacy
123083 Bob
>> df.memory_usage(deep=True)
Index 24
name 24
>> df.dtypes
name string
Nullable Booleans are also a huge win for pandas users. Previously,
Boolean columns could not have a nullable state; only True and False were
allowed. This meant users had to use an integer representation or an object
to represent a Boolean with a third NaN state, but now they can use the
pandas BooleanArray type.
The introduction of the new types in pandas, namely, the nullable
Boolean, pandas NA type, and dedicated string type, yields marked
improvements to the pandas type casting in pandas 1.0. Now, integers,
Booleans, and strings will be recognized and stored as smaller data types
even when they contain null values. This is a huge win for performance
and saving memory on load. Note that while these new types exist and are
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inferred when creating pandas arrays, they are not inferred when creating
DataFrames. You must explicitly specify the types for pandas to use them
when creating pandas DataFrames. This is why in Listings 9-1 through 9-3
the new pandas types were explicitly specified when loading data using
read_csv. If the types were not explicitly specified, they would be inferred
to be the same types as in previous versions of pandas.
Rolling apply methods also now support an engine argument that gives
the option of using Numba instead of Cython. Numba converts the custom
apply function into optimized compiled machine code similar to Cython,
but for data sets with millions of rows and custom functions that operate
on NumPy ndarrays, the pandas team found Numba to produce more
optimized code than Cython. It only makes sense to use Numba, of course,
when you are running the calculation a lot of times over and over again
since Numba has the overhead of compiling the first time it is used.
There has been a lot of work to clean up the Categorical data type in
pandas 1.0. As you may recall from Chapter 2, the Categorical data type
is used to hold metadata with a unique set of values. Deprecations within
the API have been removed, previous operations on the data type that did
not return back a Categorical now do, and there is improved handling of
null values. There are also performance improvements, for example, now,
all the values passed into searchsorted are converted to the same data
type before running a comparison. Listing 9-4 shows an example of using
searchsorted on a Categorical. This operation in pandas 1.0 is about 24
times faster than in previous versions.
Listing 9-4. Using searchsorted on Categorical
import pandas as pd
metadata = pd.Categorical(
['Mary'] * 100000 + ['Boby'] * 100000 + ['Joe'] * 100000
)
metadata.searchsorted(['Mary', 'Joe'])
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There have also been a lot of refactors and bug fixes made to groupby.
This used to be a complex bit of code to look at with a fair number of
bugs, but there have been many improvements in pandas 1.0 including
improved handling of null values, offering a selection by column name
when axis is one, allowing multiple custom aggregate functions for the
same column to match series groupby behavior, and many more.
The support of load and dump options for reading CSV data in pandas
far exceeds options for other loaders. While supporting so many options
leads to complicated code for developers, it is very nice for users. Some of
the loaders have a nice balance of options, but some fall short in load and
dump capabilities that could lead to performance speedup for users. As we
saw in Chapter 3, read_sql is missing the ability to specify data types during
load which can be fairly critical for performance. The CSV loader on the
other hand has so many options, some of which can result in a performance
slowdown if you aren’t careful. A lot of work has been done to address this
and standardize the options for input and output data methods in pandas 1.0.
For example, both read_json and read_csv are now able to parse and
interpret Infinity, –Infinity, +Infinity, and NaNs as expected. In previous
versions, read_json didn’t handle NaNs or Infinity strings, and read_csv
didn’t cast Infinity strings as floats. The usecols parameter in read_excel has
also been standardized to behave more like read_csv’s usecols parameter.
Previously, usecols was allowed to be a single integer value, whereas now it’s
a list of integer values just like read_csv.
There have been a lot of other subtle performance improvements to
pandas 1.0 as well. We’ll look at a couple of them here just to give you some
idea of what methods are being used to improve performance.
A regression in performance of the infer_type method was fixed in
pandas 1.0. An if statement was moved down in the implementation to
avoid a performance slowdown introduced by converting data types to
objects when running an isnaobj comparison prematurely as shown in
Figure  9-1 .
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Another performance fix was made to the replace method which is used
to replace values with a different value. Here, some additional code was
inserted above the original to take advantage of some early exit conditions.
If the list of values to replace is empty, simply return the original values or a
copy of the original values if inplace is False. If there is only one valid value,
replace that single value with the new value. The values were also converted
to a list of valid values as opposed to being left as a list of values that may or
may not even be legal for the given column. Note while it is not explicitly
shown in Figure  9-2 , the new to_replace list was also used in the final replace
call. By doing so, this reduced the number of replaces that were needed
and improved the overall performance over large data sets where several
columns did not contain any of the values that were to be replaced.
Figure 9-1. A d iff of the performance fix for infer_type
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The performance of comparing indexes for equality was also
improved. This is another performance improvement that was made by
adding the early exit condition shown in Figure  9-3 . If the dimensions are
not equivalent, then the indexes are determined to be not equivalent and
the MultiIndex equality check exits early.
Figure 9-2. Additional code inserted above original replace logic to
take advantage of early exit conditions
Figure 9-3. Additional code inserted into is MultiIndex check to take
advantage of early exit conditions
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Improvements were also made to the is_monotonic check on an index.
Previously, the result relied on the generation of cached values, but when
the levels of an index are already individually sorted, is_lexsorted of codes
can be used to determine monotonicity instead. Recall from Chapter 3
that levels are the unique list of values within the index and codes hold the
position of those values within the index. codes represent each value as
an integer, and this integer is the index location of the value in the list of
values. So, putting that all together, is_lexsorted is an O(n) algorithm which
is operating on integers that represent the values, whereas the previous
implementation was always operating on the index values directly in
an O(nlog(n)) check, first sorting them using NumPy’s lexsort and then
determining based on the sort result whether any of them were not in
monotonic order. By using the already sorted integer representations of
the values, we are able to more quickly determine monotonicity. Figure  9-4
shows the change in bold.
Figure 9-4. Additional code inserted to improve performance of
is_monotonic check
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There is talk from the pandas team of removing the inplace option from
all pandas methods, and for that reason, they have generally recommended
to not use it. The inplace option, contrary to what its name suggests, does
not always operate inplace without duplicating memory. This typically
happens as a result of pandas type inference where the operation results in
a data type change, and thus the data has to be reconstructed with the new
type. Listing 9-5 illustrates this example. When the NaN value is replaced
with 0.0, the type is still a float and the value can be directly replaced in the
NumPy array without having to create a new one and copy memory. When
0.0 is replaced with the string null, the float64 type cannot hold a string
and so the NumPy array must be rebuilt and the memory must be copied
into a new array of type object. Both these operations were specified with
inplace=True, yet the latter resulted in a memory copy because the type of
the underlying data structure had to change.
Listing 9-5. An exam ple of inplace=True copying rather than
modifying the data
>> data = pd.DataFrame({"size":[np.nan,1.0,3.5]})
>> data.dtypes
size float64
>> id(data._data.blocks[0].values)
4757583472
>> data.fillna(0.0, inplace=True)
>> data.dtypes
size float64
>> id(data._data.blocks[0].values)
4757583472
>> data.replace(0.0, "null", inplace=True)
>> data.dtypes
size object
>> id(data._data.blocks[0].values)
4757572464
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While attempting to not duplicate data is arguably better performance
than always duplicating it, in an effort to shift users with as little pain as
possible over to a world where inplace does not exist, the pandas team has
recommended to not use inplace=True.
In the last couple years, the original author of pandas, Wes McKinney,
has started working on a new project called Apache Arrow 1 which he
hopes will one day be the back end of pandas. It aims to fix a lot of the core
issues of pandas including reducing the memory overhead and enabling
lazy evaluations.
Conclusion
Because of pandas’ diverse user base, it supports many different options
and many different methods for doing the same thing. pandas’ API has
a large and ever-expanding set of features and options which can be
incredibly overwhelming and often lead users to implement things in
a suboptimal way. It’s a difficult decision to make: limit the number of
features and options such that users can’t do the wrong thing or provide
a set of features so that users can find a way to do whatever they want.
pandas has certainly erred on the side of the latter which makes it a very
powerful tool and applicable to many different types of big data problems.
And for those users who don’t care if their program takes a minute or an
hour, it’s not an issue that they have used a suboptimal implementation.
However, for users that do, it can be difficult to reason about and
understand. Hopefully, this book has left you with a better understanding
of how pandas works underneath and an intuition for which method to
use during certain scenarios.
1 https://arrow.apache.org/
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In general, we’ve covered a few basic rules-to-code-by throughout
this book which you should keep in mind as you implement your future
pandas projects:
• Normalize your data at the same time as you load it if
possible
• Explicitly specify your data types
• Avoid looping in Python
• Carefully select a DataFrame orientation that optimizes
analysis
• Avoid operations that duplicate data
• Take advantage of Cython or faster custom
implementations as needed
By following these basic rules-to-code-by and now with an intuition
for how a given pandas operation will perform underneath, you should
be able to select the most optimal implementation for your next pandas
project.
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171 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2
APPENDIX
Useful Reference
Tables
Table A-1. Conversion between NumPy and C types 1
NumPy type C type Description
np.bool Bool Boolean (True or False) stored as a byte
np.byte signed char Platform-defined
np.ubyte unsigned char Platform-defined
np.short Short Platform-defined
np.ushort unsigned short Platform-defined
np.intc Int Platform-defined
np.uintc unsigned int Platform-defined
np.int_ Long Platform-defined
np.uint unsigned long Platform-defined
np.longlong long long Platform-defined
np.ulonglong unsigned long long Platform-defined
(continued )
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172
Table A-1. (continued )
NumPy type C type Description
np.half /
np.float16
N/A Half-precision float: sign bit, 5 bits exponent,
10 bits mantissa
np.single Float Platform-defined single-precision float:
typically sign bit, 8 bits exponent, 23 bits
mantissa
np.double Double Platform-defined double-precision float:
typically sign bit, 11 bits exponent, 52 bits
mantissa
np.
longdouble
long double Platform-defined extended-precision float
np.csingle float complex Complex number, represented by two
single-precision floats (real and imaginary
components)
np.cdouble double complex Complex number, represented by two
double-precision floats (real and imaginary
components)
np.clong
double
long double
complex
Complex number, represented by two
extended-precision floats (real and imaginary
components)
np.int8 int8at Byte (–128 to 127)
np.int16 int16at Integer (–32768 to 32767)
np.int32 int32at Integer (–2147483648 to 2147483647)
np.int64 int64at Integer (–9223372036854775808 to
9223372036854775807)
np.uint8 uint8at Unsigned integer (0 to 255)
(continued )
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NumPy type C type Description
np.uint16 uint16at Unsigned integer (0 to 65535)
np.uint32 uint32at Unsigned integer (0 to 4294967295)
np.uint64 uint64at Unsigned integer (0 to
18446744073709551615)
np.intp intptrat Integer used for indexing, typically the same
as ssizeat
np.uintp uintptrat Integer large enough to hold a pointer
np.float32 Float Platform-defined single-precision float: typically
sign bit, 8 bits exponent, 23 bits mantissa
np.float64 /
np.float_
Double Note that this matches the precision of the
built-in Python float
np.complex64 float complex Complex number, represented by two 32-bit
floats (real and imaginary components)
np.complex128
/ np.complex_
double complex Note that this matches the precision of the
built-in Python complex
Table A-1. (continued )
Table A-2. Common ufuncs for NumPy 2
Ufuncs Description
add(x1, x2, /], out, where, casting, order, …_) Add arguments element-wise
subtract(x1, x2, /], out, where, casting, …_) Subtract arguments, element-wise
multiply(x1, x2, /], out, where, casting, …_) Multiply arguments element-wise
(continued )
2 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.
read_csv.html
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Ufuncs Description
divide(x1, x2, /], out, where, casting, …_) Return a true division of the inputs,
element-wise
logaddexp(x1, x2, /], out, where, casting, …_) Logarithm of the sum of
exponentiations of the inputs
logaddexp2(x1, x2, /], out, where, casting, …_) Logarithm of the sum of
exponentiations of the inputs in
base 2
trueadivide(x1, x2, /], out, where, …_) Return a true division of the inputs,
element-wise
flooradivide(x1, x2, /], out, where, …_) Return the largest integer smaller
or equal to the division of the inputs
negative(x, /], out, where, casting, order, …_) Numerical negative, element-wise
positive(x, /], out, where, casting, order, …_) Numerical positive, element-wise
power(x1, x2, /], out, where, casting, …_) First array elements raised
to powers from second array,
element-wise
remainder(x1, x2, /], out, where, casting, …_) Return element-wise remainder of
division
mod(x1, x2, /], out, where, casting, order, …_) Return element-wise remainder of
division
fmod(x1, x2, /], out, where, casting, …_) Return element-wise remainder of
division
(continued )
Table A-2. (continued )
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Ufuncs Description
divmod(x1, x2], out1, out2_, / ]], out, …_) Return element-wise quotient and
remainder simultaneously
absolute(x, /], out, where, casting, order, …_) Calculate the absolute value
element-wise
fabs(x, /], out, where, casting, order, …_) Compute the absolute value
element-wise
rint(x, /], out, where, casting, order, …_) Round elements of the array to the
nearest integer
sign(x, /], out, where, casting, order, …_) Return an element-wise indication
of the sign of a number
heaviside(x1, x2, /], out, where, casting, …_) Compute the Heaviside step
function
conj(x, /], out, where, casting, order, …_) Return the complex conjugate,
element-wise
conjugate(x, /], out, where, casting, …_) Return the complex conjugate,
element-wise
exp(x, /], out, where, casting, order, …_) Calculate the exponential of all
elements in the input array
exp2(x, /], out, where, casting, order, …_) Calculate 2 ∗∗ p for all p in the input
array
log(x, /], out, where, casting, order, …_) Natural logarithm, element-wise
log2(x, /], out, where, casting, order, …_) Base 2 logarithm of x
Table A-2. (continued )
(continued )
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Ufuncs Description
log10(x, /], out, where, casting, order, …_) Return the base 10 logarithm of the
input array, element-wise
expm1(x, /], out, where, casting, order, …_) Calculate exp(x) - 1 for all elements
in the array
log1p(x, /], out, where, casting, order, …_) Return the natural logarithm of one
plus the input array, element-wise
sqrt(x, /], out, where, casting, order, …_) Return the non-negative square
root of an array, element-wise
square(x, /], out, where, casting, order, …_) Return the element-wise square of
the input
cbrt(x, /], out, where, casting, order, …_) Return the cube root of an array,
element-wise
reciprocal(x, /], out, where, casting, …_) Return the reciprocal of the
argument, element-wise
gcd(x1, x2, /], out, where, casting, order, …_) Return the greatest common divisor
of |x1| and |x2|
lcm(x1, x2, /], out, where, casting, order, …_) Return the lowest common multiple
of |x1| and |x2|
Table A-2. (continued )
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3 https://pandas.pydata.org/pandas-docs/stable/reference/frame.
html#computations-descriptive-stats
Table A-3. Values that are
automatically converted to
NaNs by read_csv 3
‘’
NULL
-1.#IND
NaN
-NaN
#N/A
NA
#N/A N/A
n/a
#NA
1.#QNan
-1.#QNan
NaN
-NaN
Null
N/A
1.#IND
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178
Table A-4. Built-in DataFrame computation methods
Computation Description
DataFrame.abs(self) Return a Series/DataFrame with absolute
numeric value of each element
DataFrame.all(self], axis,
boolaonly, …_)
Return whether all elements are True,
potentially over an axis
DataFrame.any(self], axis,
boolaonly, …_)
Return whether any element is True,
potentially over an axis
DataFrame.clip(self], lower, upper, axis_) Trim values at input threshold(s)
DataFrame.corr(self], method,
minaperiods_)
Compute pairwise correlation of columns,
excluding NA/null values
DataFrame.corrwith(self, other],
axis, …_)
Compute pairwise correlation
DataFrame.count(self], axis, level, …_) Count non-NA cells for each column or row
DataFrame.cov(self], minaperiods_) Compute pairwise covariance of columns,
excluding NA/null values
DataFrame.cummax(self], axis, skipna_) Return cumulative maximum over a
DataFrame or Series axis
DataFrame.cummin(self], axis, skipna_) Return cumulative minimum over a
DataFrame or Series axis
DataFrame.cumprod(self], axis, skipna_) Return cumulative product over a
DataFrame or Series axis
DataFrame.cumsum(self], axis, skipna_) Return cumulative sum over a DataFrame
or Series axis
DataFrame.describe(self],
percentiles, …_)
Generate descriptive statistics
(continued )
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179
Computation Description
DataFrame.diff(self], periods, axis_) First discrete difference of element
DataFrame.eval(self, expr], inplace_) evaluate a string describing operations on
DataFrame columns
DataFrame.kurt(self], axis, skipna,
level, …_)
Return unbiased kurtosis over requested
axis
DataFrame.kurtosis(self], axis,
skipna, …_)
Return unbiased kurtosis over requested
axis
DataFrame.mad(self], axis, skipna,
level_)
Return the mean absolute deviation of the
values for the requested axis
DataFrame.max(self], axis, skipna,
level, …_)
Return the maximum of the values for the
requested axis
DataFrame.mean(self], axis, skipna,
level, …_)
Return the mean of the values for the
requested axis
DataFrame.median(self], axis,
skipna, …_)
Return the median of the values for the
requested axis
DataFrame.min(self], axis, skipna,
level, …_)
Return the minimum of the values for the
requested axis
DataFrame.mode(self], axis, numerica
only, …_)
Get the mode(s) of each element along the
selected axis
DataFrame.pctachange(self],
periods, …_)
Percentage change between the current
and a prior element
DataFrame.prod(self], axis, skipna,
level, …_)
Return the product of the values for the
requested axis
Table A-4. (continued )
(continued )
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180
Computation Description
DataFrame.product(self], axis,
skipna, …_)
Return the product of the values for the
requested axis
DataFrame.quantile(self], q, axis, …_) Return values at the given quantile over
requested axis
DataFrame.rank(self], axis_) Compute numerical data ranks (1 through
n) along axis
DataFrame.round(self], decimals_) Round a DataFrame to a variable number
of decimal places
DataFrame.sem(self], axis, skipna,
level, …_)
Return unbiased standard error of the
mean over requested axis
DataFrame.skew(self], axis, skipna,
level, …_)
Return unbiased skew over requested axis
DataFrame.sum(self], axis, skipna,
level, …_)
Return the sum of the values for the
requested axis
DataFrame.std(self], axis, skipna,
level, …_)
Return sample standard deviation over
requested axis
DataFrame.var(self], axis, skipna,
level, …_)
Return unbiased variance over requested
axis
DataFrame.nunique(self], axis, dropna_) Count distinct observations over requested
axis
Table A-4. (continued )
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181 © Hannah Stepanek 2020 H. Stepanek, Thinking in Pandas , https://doi.org/10.1007/978-1-4842-5839-2
Index
A
Anti-join method, 22
Apply method
Cython library, 131, 133
DataFrame
element, 129
implementation, 128, 130
definition, 121
B
Basic Linear Algebra Subprograms
(BLAS), 150
dot product, 151, 153
library, 154
loop unrolling, 154
matrix comparison, 152
NumPy, 155
SIMD instruction, 151
techniques, 154
transposed matrix, 153
Big Data software, 31
Blackhole, image
GitHub, 5
image prediction
algorithms, 5
telescope, 4, 5
Bytecode, 37
cache, 38
interpretation, 38
tokenizer, 38
C
Categorical variables, 50
Computer architecture
arrays, 145
bytecode instructions, 143
cache, 144, 145
clock cycles, 145
compilers, 143
evaluation phase, 142
execution phase, 142
five-stage pipeline, 142
memory access
phase, 142
memory load phase/inserting
NOPs, 143
modern Intel CPU, 144
pseudo-code CPU
instructions, 143
Python program, 141
sequential memory access vs.
object attribute, 146

182
temporary variable, 141
writeback phase, 143
CPython
BLAS, 48
bytecode, 37, 38
C extensions, 37
circular reference, 41, 42
deadlock, 45
deleting references, 41
dynamic typing, 38
garbage collector, 37
GIL, 37, 39
interpreters, 37
LAPACK, 48
lexical analyzer, 38
libraries, 48
lock, demonstration, 44
memory allocation, 47
multi-core computation, 46
multi-core CPUs, 45, 46
multi-threaded
application, 42
ndarray, 47, 48
NumPy, 46
object’s reference count, 42
.pyc files, 38
race condition, 39, 40, 43
reference garbage collector
technique, 40
self-managing niceties, 37
shared memory lock, 43
threading, 39
tokenizer, 38
traceback object, 41
variables, 40
cumcount function, 115
Cython library, 131
D
DataFrames
calculation, 61
computation methods, 178–180
concat method, 27, 28, 30
consideration/planning, 55
creation and access, 9–11
custom functions, 64
data processing, 56
dimension mismatch, 57
format, 64
join method, 25, 27
multi-index date
column, 58, 60
multi-index multi-level
column, 62
single-index, 56
unsigned 8-bit integer, 62
Data transformation, pandas
melt, 116, 117
pivot/pivot table, 109, 111, 113
stack/unstack, 113, 115, 116
transpose, 117, 119
Deadlock, 45
Dictionary, 34
Discoverability, 6
Duck typing, 69
Dynamic typing, 38
Computer architecture ( cont .)
INDEX

183
E
exc_info variable, 41
F
Fetchall function, 107
Financial investment decisions, 6
G, H
Garbage collector, 37
Global interpreter lock (GIL), 37
Groupby
aggregate function, 135, 136
operation, 139, 140
pre-indexed DataFrame, 137
I
iloc method, 11–14
Image prediction algorithms, 5
“is” property, 36
J, K
Just-In-Time (JIT), 37
L
Linear Algebra Package
(LAPACK), 48, 150
Loading data
creation/elimination, 66
IO pandas, 66
non-numeric values, 67
normalization/optimization
capabilities, 65
pd.read_csv ( see pd.read_csv
loader)
read_json ( see read_json loader)
read_sql ( see read_sql_query
loader)
loc method, 14–16
M
memory_map, 80
Merge method
anti-join, 23, 24
historical record, 20, 21
inner merge, 17
_merge column, 23
outer merge, 18, 19
Mutable tuple, 33
N
na_values parameter, 81
Non-performant solutions, 31
Normalizing data, 66
Not a Number (Nan) type, 2, 81
np.sum function, 122
nrows parameter, 77
NumExpr
cache, 147
CPU pseudo-code
instructions, 147
df_sum1, 150
eval, 149
pandas DataFrames, 146
INDEX

184
NumPy
C extensions, 3
C types, conversion, 171–176
definition, 2
sum function, 121, 123
O
Object Relational
Mapper (ORM), 102
P, Q
Pandas
built-in string cache, 50
category, 50
DataFrames ( see DataFrames)
datetime data structure, 49
integer type, 54
levels, 54
multi-index DataFrame, 52
multi-index multi-level column
DataFrame, 54
names, 54
NumPy, 49
operations, 50, 51
rules-to-code-by, 169
single-index DataFrame, 51
suboptimal implementation, 168
Pandas 1.0
Boolean arrays, 159, 161
Categorical.min, 159
comparing indexes, 165
DataFrames, 162
exit conditions, 165
groupby, 163
infer_type method, 163, 164
inplace=True, 167, 168
inplace option, 167
is_monotonic check, 166
memory usage, 160
NaNs, 157, 158
NA type, 157
nullable Booleans, 161
nullable integer type, 158
Numba, 162
NumPy’s lexsort, 166
read_sql, 163
rolling apply methods, 162
searchsorted, 162
string data type, 159
usecols parameter, 163
values, 164
pandas merge method, 22
Panel data, 2
Parsing performance metrics, 87
pd.read_csv loader
columns types, 75
converters parameter, 76
C parsing engine, 67, 68, 87
data by default, 84
date_parser parameter, 91
date-specific parameters, 90
datetime objects/handling
Nans, 89
dayfirst parameter, 91
delimiter, 92
delim_whitespace parameter, 69
INDEX

185
dtype parameter, 74
duck typing, 69
eliminating columns, 70
escapechar parameter, 91
filepath_or_buffer, 69
float_precision parameter, 68
header/column names, 72
iterator parameter, 79, 80
memory mapping, 81
multi-index multi-level column
DataFrame, 73
na_filter parameter, 82
Nans, 82
na_values, 81, 89
na_values/dtype, 85
non-comma-delimited data, 70
nrows parameter, 77–79
NumPy, 81
parse_dates parameter, 87
placeholder values, 85
Python parser–specific option, 68
Python standard library, 67
round-trip precision option, 68
sep parameter, 69
skipfooter parameter, 71, 72
skipinitialspace parameter, 69
skiprows parameter, 71
squeeze parameter, 73, 74
StringIO object, 69
tokenization, 87
unknown, 83
usecols parameter, 70
ValueError, 68
verbose parameter, 82
Programming language, 31
Python data structures
array of pointers, 32
built-in caching, 35
data array, 34
dictionary, 34, 35
hashed index array, 34
integer cache, 36
list, 33
memory addresses, 32, 33
metadata, 32
performance optimizations, 33
references/pointers, 36
scientific notation, 37
sets, 35
string cache, 36
string/integer cache, 35
tuples, 32
R
read_csv function, 69, 177
read_json loader
chunks, 98
columns option, 95
convert_dates parameter, 98
index option, 94
JSON, 100
pandas type inference, 99
parameter orient, 93
records, 94
split, 93
table option, 97
values option, 96
INDEX

186
read() method, 69
read_sql_query loader, 101
read_sql_table loader, 101
S
Single Instruction Multiple
Data (SIMD), 151
skipfooter parameter, 71
Skiprows parameter, 71
Sorted index, 138
SQLAlchemy
custom loading code, 105
database table, 102
DataFrame, 105
datetime conversion, 105
docker-compose.yml
file, 104
fetchall function, 107
loader code, 107
normalization process, 108
ORM, 102
pandas implementation, 107
parameterized
expressions, 102
Postgres database, 103
query API, 104
vs. SQL string query, 102
TypeDecorator, 106
T
Threading, 39
Thread safe, 39
Tokenization, 87
U, V, W, X, Y, Z
ufuncs, 48, 173–176
Unique index, 139
Unsorted index, 138
usecols parameter, 70
INDEX

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