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E-raamat: Pandas in Action

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  • Formaat: 440 pages
  • Ilmumisaeg: 12-Oct-2021
  • Kirjastus: Manning Publications
  • Keel: eng
  • ISBN-13: 9781638351047
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Pandas in ActionSuurem pilt
  • Formaat: 440 pages
  • Ilmumisaeg: 12-Oct-2021
  • Kirjastus: Manning Publications
  • Keel: eng
  • ISBN-13: 9781638351047
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Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.

In Pandas in Action you will learn how to:

    Import datasets, identify issues with their data structures, and optimize them for efficiency
    Sort, filter, pivot, and draw conclusions from a dataset and its subsets
    Identify trends from text-based and time-based data
    Organize, group, merge, and join separate datasets
    Use a GroupBy object to store multiple DataFrames

Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You&;ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Data analysis with Python doesn&;t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It&;s a perfect way to up your data game.

About the book
Pandas in Action introduces Python-based data analysis using the amazing pandas library. You&;ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical&;or impossible&;in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you&;ll find in the real world.

What's inside

    Organize, group, merge, split, and join datasets
    Find trends in text-based and time-based data
    Sort, filter, pivot, optimize, and draw conclusions
    Apply aggregate operations

About the reader
For readers experienced with spreadsheets and basic Python programming.

About the author
Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries.

Table of Contents
PART 1 CORE PANDAS
1 Introducing pandas
2 The Series object
3 Series methods
4 The DataFrame object
5 Filtering a DataFrame
PART 2 APPLIED PANDAS
6 Working with text data
7 MultiIndex DataFrames
8 Reshaping and pivoting
9 The GroupBy object
10 Merging, joining, and concatenating
11 Working with dates and times
12 Imports and exports
13 Configuring pandas
14 Visualization

Arvustused

An outstanding pandas reference for developers just starting to use pandas and/or Python for data analysis or data science.

Preface xiii
Acknowledgments xv
About This Book xvii
About The Author xx
About The Cover Illustration xxi
Part 1 Core Pandas
1 Introducing pandas
3(19)
1.1 Data in the 21st century
4(1)
1.2 Introducing pandas
4(5)
Pandas vs. graphical spreadsheet applications
6(2)
Pandas vs. its competitors
8(1)
1.3 A tour of pandas
9(13)
Importing a data set
9(2)
Manipulating a DataFrame
11(3)
Counting values in a Series
14(1)
Filtering a column by one or more criteria
15(3)
Grouping data
18(4)
2 The Series object
22(32)
2.1 Overview of a Series
23(7)
Classes and instances
24(1)
Populating the Series with values
24(2)
Customizing the Series index
26(3)
Creating a Series with missing values
29(1)
2.2 Creating a Series from Python objects
30(2)
2.3 Series attributes
32(2)
2.4 Retrieving the first and last rows
34(2)
2.5 Mathematical operations
36(12)
Statistical operations
36(7)
Arithmetic operations
43(2)
Broadcasting
45(3)
2.6 Passing the Series to Python's built-in functions
48(2)
2.7 Coding challenge
50(1)
Problems
50(1)
Solutions
50(4)
3 Series methods
54(25)
3.1 Importing a data set with the read_csv function
55(5)
3.2 Sorting a Series
60(5)
Sorting by values with the sort values method
60(2)
Sorting by index with the sort index method
62(2)
Retrieving the smallest and largest values with the nsmallest and nlargest methods
64(1)
3.3 Overwriting a Series with the inplace parameter
65(1)
3.4 Counting values with the value_counts method
66(6)
3.5 Invoking a function on every Series value with the apply method
72(3)
3.6 Coding challenge
75(1)
Problems
75(1)
Solutions
76(3)
4 The DataFrame object
79(34)
4.1 Overview of a DataFrame
80(3)
Creating a DataFrame from a dictionary
80(1)
Creating a DataFrame from a NumPy ndarray
81(2)
4.2 Similarities between Series and DataFrames
83(7)
Importing a DataFrame with the read_csv function
83(1)
Shared and exclusive attributes of Series and DataFrames
84(3)
Shared methods of Series and DataFrames
87(3)
4.3 Sorting a DataFrame
90(4)
Sorting by a single column
90(2)
Sorting by multiple columns
92(2)
4.4 Sorting by index
94(1)
Sorting by row index
94(1)
Sorting by column index
95(1)
4.5 Setting a new index
95(1)
4.6 Selecting columns and rows from a DataFrame
96(3)
Selecting a single column from a DataFrame
96(1)
Selecting multiple columns from a DataFrame
97(2)
4.7 Selecting rows from a DataFrame
99(7)
Extracting rows by index label
99(2)
Extracting rows by index position
101(2)
Extracting values from specific columns
103(3)
4.8 Extracting values from Series
106(1)
4.9 Renaming columns or rows
106(2)
4.10 Resetting an index
108(1)
4.11 Coding challenge
109(1)
Problems
109(1)
Solutions
109(4)
5 Filtering a DataFrame
113(32)
5.1 Optimizing a data set for memory use
114(6)
Converting data types with the astype method
116(4)
5.2 Filtering by a single condition
120(4)
5.3 Filtering by multiple conditions
124(3)
The AND condition
124(1)
The OR condition
125(1)
Inversion with
126(1)
Methods for Boolean
127(1)
5.4 Filtering by condition
127(7)
The isin method
127(1)
The between method
128(2)
The isnull and notnull methods
130(2)
Dealing with null values
132(2)
5.5 Dealing with duplicates
134(5)
The duplicated method
134(2)
The drop_duplicates method
136(3)
5.6 Coding challenge
139(1)
Problems
139(1)
Solutions
140(5)
Part 2 Applied Pandas 145(184)
6 Working with text data
147(18)
6.1 Letter casing and whitespace
148(3)
6.2 String slicing
151(2)
6.3 String slicing and character replacement
153(2)
6.4 Boolean methods
155(2)
6.5 Splitting strings
157(5)
6.6 Coding challenge
162(1)
Problems
162(1)
Solutions
162(1)
6.7 A note on regular expressions
163(2)
7 MultiIndex DataFrames
165(33)
7.1 The MultiIndex object
166(4)
7.2 MultiIndex DataFrames
170(5)
7.3 Sorting a MultiIndex
175(4)
7.4 Selecting with a MultiIndex
179(9)
Extracting one or more columns
179(3)
Extracting one or more rows with loc
182(4)
Extracting one or more rows with iloc
186(2)
7.5 Cross-sections
188(1)
7.6 Manipulating the Index
189(5)
Resetting the index
189(4)
Setting the index
193(1)
7.7 Coding challenge
194(1)
Problems
194(1)
Solutions
195(3)
8 Reshaping and pivoting
198(22)
8.1 Wide vs. narrow data
199(1)
8.2 Creating a pivot table from a DataFrame
200(7)
The pivot_table method
201(4)
Additional options for pivot tables
205(2)
8.3 Stacking and unstacking index levels
207(2)
8.4 Melting a data set
209(4)
8.5 Exploding a list of values
213(1)
8.6 Coding challenge
214(1)
Problems
214(1)
Solutions
215(5)
9 The GroupBy object
220(19)
9.1 Creating a GroupBy object from scratch
221(1)
9.2 Creating a GroupBy object from a data set
222(3)
9.3 Attributes and methods of a GroupBy object
225(4)
9.4 Aggregate operations
229(3)
9.5 Applying a custom operation to all groups
232(1)
9.6 Grouping by multiple columns
233(2)
9.7 Coding challenge
235(1)
Problems
235(1)
Solutions
235(4)
10 Merging, joining, and concatenating
239(21)
10.1 Introducing the data sets
241(2)
10.2 Concatenating data sets
243(2)
10.3 Missing values in concatenated DataFrames
245(2)
10.4 Left joins
247(2)
10.5 Inner joins
249(2)
10.6 Outer joins
251(2)
10.7 Merging on index labels
253(1)
10.8 Coding challenge
254(2)
Problems
256(1)
Solutions
256(4)
11 Working with dates and times
260(29)
11.1 Introducing the Timestamp object
261(5)
How Python works with datetimes
261(3)
How pandas works with datetimes
264(2)
11.2 Storing multiple timestamps in a DatetimeIndex
266(2)
11.3 Converting column or index values to datetimes
268(1)
11.4 Using the DatetimeProperties object
269(4)
11.5 Adding and subtracting durations of time
273(2)
11.6 Date offsets
275(2)
11.7 The Timedelta object
277(5)
11.8 Coding challenge
282(1)
Problems
282(1)
Solutions
283(6)
12 Imports and exports
289(21)
12.1 Reading from and writing to JSON files
290(9)
Loading a ISON file Into a DataFrame
292(6)
Exporting a DataFrame to a JSON file
298(1)
12.2 Reading from and writing to CSV files
299(2)
12.3 Reading from and writing to Excel workbooks
301(5)
Installing the xlrd and openpyxl libraries in an Anaconda environment
301(1)
Importing Excel workbooks
302(3)
Exporting Excel workbooks
305(1)
12.4 Coding challenge
306(1)
Problems
307(1)
Solutions
307(3)
13 Configuring pandas
310(9)
13.1 Getting and setting pandas options
311(4)
13.2 Precision
315(1)
13.3 Maximum column width
316(1)
13.4 Chop threshold
316(1)
13.5 Option context
317(2)
14 Visualization
319(10)
14.1 Installing matplotlib
320(1)
14.2 Line charts
320(5)
14.3 Bar graphs
325(2)
14.4 Pie charts
327(2)
Appendix A Installation and setup 329(18)
Appendix B Python crash course 347(36)
Appendix C NumPy crash course 383(8)
Appendix D Generating fake data with Faker 391(6)
Appendix E Regular expressions 397(12)
Index 409
Boris Paskhaver is a software engineer, Agile consultant, and educator. His six programming courses on Udemy have amassed 236,000 students, with an average course rating of 4.59 out of 5. He first used Python and the pandas library to derive a variety of business insights at the worlds #1 jobs site, Indeed.com.
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