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首页Pandas for Everyone Python Data Analysis 无水印pdf
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Contents
Chapter 1. Pandas Dataframe basics
1.1 Introduction
1.2 Concept map
1.3 Objectives
1.4 Loading your first data set
1.5 Looking at columns, rows, and cells
1.6 Grouped and aggregated calculations
1.7 Basic plot
1.8 Conclusion
Chapter 2. Pandas data structures
2.1 Introduction
2.2 Concept map
2.3 Objectives
2.4 Creating your own data
2.5 The Series
2.6 The DataFrame
2.7 Making changes to Series and DataFrames
2.8 Exporting and importing data
2.9 Conclusion
Chapter 3. Introduction to Plotting
3.4 matplotlib
Chapter 4. Data Assembly
4.1 Introduction
4.2 Concept map
4.3 Objectives
4.4 Concatenation
4.6 Summary
Chapter 5. Missing Data
5.1 Introduction
Concept map
Objectives
5.2 What is a NaN value
5.3 Where do missing values come from?
5.3.3 User input values
5.4 Working with missing data
Summary
Chapter 6. Tidy Data by Reshaping
6.1 Introduction
Chapter 1. Pandas Dataframe basics
1.1 Introduction
Pandas is an open source Python library for data analysis. It gives Python the
ability to work with spreadsheet-like data for fast data loading, manipulating,
aligning, merging, etc. To give Python these enhanced features, Pandas
introduces two new data types to Python: Series and DataFrame. The
DataFrame will represent your entire spreadsheet or rectangular data, whereas
the Series is a single column of the DataFrame. A Pandas DataFrame can also
be thought of as a dictionary or collection of Series.
Why should you use a programming language like Python and a tool like
Pandas to work with data? It boils down to automation and reproducibility. If
there is a articular set of analysis that needs to be performed on multiple
datasets, a programming language has the ability to automate the analysis on the
datasets. Although many spreadsheet programs have its own macro
programming language, many users do not use them. Furthermore, not all
spreadsheet programs are available on all operating systems. Performing data
takes using a programming language forces the user to have a running record of
all steps performed on the data. I, like many people, have accidentally hit a key
while viewing data in a spreadsheet program, only to find out that my results
do not make any sense anymore due to bad data. This is not to say spreadsheet
programs are bad or do not have their place in the data workflow, they do, but
there are better and more reliable tools out there.
1.2 Concept map
1. Prior knowledge needed (appendix)
(a) relative directories
(b) calling functions
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