English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 52 Lessons (7h 34m) | 1.89 GB
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.
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.
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.
Table of Contents
1 Part 1. Core pandas
2 Introducing pandas
3 Pandas vs. graphical spreadsheet applications
4 A tour of pandas
5 Counting values in a Series
6 The Series object
7 Customizing the Series index
8 Creating a Series from Python objects
9 Mathematical operations
10 Arithmetic operations
11 Passing the Series to Python’s built-in functions
12 Series methods
13 Sorting a Series
14 Counting values with the value counts method
15 Invoking a function on every Series value with the apply method
16 The DataFrame object
17 Shared and exclusive attributes of Series and DataFrames
18 Sorting a DataFrame
19 Selecting rows from a DataFrame
20 Extracting values from Series
21 Filtering a DataFrame
22 Filtering by a single condition
23 Filtering by condition
24 The drop duplicates method
25 Part 2. Applied pandas
26 Working with text data
27 String slicing
28 Splitting strings
29 MultiIndex DataFrames
30 MultiIndex DataFrames – Scaling up
31 Sorting a MultiIndex
32 Extracting one or more rows with loc
33 Manipulating the Index
34 Reshaping and pivoting
35 Additional options for pivot tables
36 Melting a data set
37 The GroupBy object
38 Attributes and methods of a GroupBy object
39 Grouping by multiple columns
40 Merging, joining, and concatenating
41 Missing values in concatenated DataFrames
42 Merging on index labels
43 Working with dates and times
44 Storing multiple timestamps in a DatetimeIndex
45 Date offsets
46 Coding challenge
47 Imports and exports
48 Exporting a DataFrame to a JSON file
49 Exporting Excel workbooks
50 Configuring pandas
51 Precision
52 Visualization
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