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  • Book Overview & Buying Learning Pandas
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Learning Pandas

Learning Pandas

By : Michael Heydt
3.5 (2)
Buy this Book
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Learning Pandas

Learning Pandas

3.5 (2)
By: Michael Heydt
Buy this Book

Overview of this book

If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory.
Table of Contents (16 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
pandas and Data Analysis
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pandas and Data Analysis
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Introducing pandas
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Data manipulation, analysis, science, and pandas
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The process of data analysis
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Relating the book to the process
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Concepts of data and analysis in our tour of pandas
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Other Python libraries of value with pandas
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Summary
2
Up and Running with pandas
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Up and Running with pandas
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Installation of Anaconda
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IPython and Jupyter Notebook
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Introducing the pandas Series and DataFrame
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Visualization
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Summary
3
Representing Univariate Data with the Series
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Representing Univariate Data with the Series
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Configuring pandas
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Creating a Series
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The .index and .values properties
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The size and shape of a Series
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Specifying an index at creation
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Heads, tails, and takes
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Retrieving values in a Series by label or position
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Slicing a Series into subsets
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Alignment via index labels
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Performing Boolean selection
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Re-indexing a Series
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Modifying a Series in-place
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Summary
4
Representing Tabular and Multivariate Data with the DataFrame
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Representing Tabular and Multivariate Data with the DataFrame
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Configuring pandas
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Creating DataFrame objects
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Accessing data within a DataFrame
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Selecting rows using Boolean selection
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Selecting across both rows and columns
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Summary
5
Manipulating DataFrame Structure
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Manipulating DataFrame Structure
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Configuring pandas
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Renaming columns
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Adding new columns with [] and .insert()
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Adding columns through enlargement
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Adding columns using concatenation
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Reordering columns
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Replacing the contents of a column
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Deleting columns
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Appending new rows
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Concatenating rows
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Adding and replacing rows via enlargement
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Removing rows using .drop()
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Removing rows using Boolean selection
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Removing rows using a slice
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Summary
6
Indexing Data
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Indexing Data
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Configuring pandas
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The importance of indexes
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The pandas index types
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Working with Indexes
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Hierarchical indexing
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Summary
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Categorical Data
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Categorical Data
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Configuring pandas
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Creating Categoricals
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Renaming categories
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Appending new categories
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Removing categories
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Removing unused categories
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Setting categories
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Descriptive information of a Categorical
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Munging school grades
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Summary
8
Numerical and Statistical Methods
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Numerical and Statistical Methods
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Configuring pandas
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Performing numerical methods on pandas objects
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Performing statistical processes on pandas objects
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Summary
9
Accessing Data
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Accessing Data
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Configuring pandas
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Working with CSV and text/tabular format data
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Reading and writing data in Excel format
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Reading and writing JSON files
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Reading HTML data from the web
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Reading and writing HDF5 format files
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Accessing CSV data on the web
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Reading and writing from/to SQL databases
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Reading data from remote data services
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Summary
10
Tidying Up Your Data
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Tidying Up Your Data
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Configuring pandas
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What is tidying your data?
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How to work with missing data
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Handling duplicate data
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Transforming data
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Summary
11
Combining, Relating, and Reshaping Data
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Combining, Relating, and Reshaping Data
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Configuring pandas
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Concatenating data in multiple objects
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Merging and joining data
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Pivoting data to and from value and indexes
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Stacking and unstacking
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Performance benefits of stacked data
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Summary
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Data Aggregation
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Data Aggregation
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Configuring pandas
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The split, apply, and combine (SAC) pattern
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Data for the examples
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Splitting data
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Applying aggregate functions, transforms, and filters
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Transforming groups of data
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Filtering groups from aggregation
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Summary
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Time-Series Modelling
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Time-Series Modelling
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Setting up the IPython notebook
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Representation of dates, time, and intervals
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Introducing time-series data
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Calculating new dates using offsets
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Representing durations of time using Period
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Handling holidays using calendars
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Normalizing timestamps using time zones
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Manipulating time-series data
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Time-series moving-window operations
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Summary
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Visualization
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Visualization
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Configuring pandas
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Plotting basics with pandas
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Creating time-series charts
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Common plots used in statistical analyses
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Manually rendering multiple plots in a single chart
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Summary
15
Historical Stock Price Analysis
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Historical Stock Price Analysis
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Setting up the IPython notebook
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Obtaining and organizing stock data from Google
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Plotting time-series prices
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Plotting volume-series data
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Calculating the simple daily percentage change in closing price
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Calculating simple daily cumulative returns of a stock
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Resampling data from daily to monthly returns
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Analyzing distribution of returns
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Performing a moving-average calculation
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Comparison of average daily returns across stocks
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Correlation of stocks based on the daily percentage change of the closing price
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Calculating the volatility of stocks
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Determining risk relative to expected returns
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Summary

Summary

Visualizing your data is one of the best ways to quickly understand the story that is being told with the data. Python, pandas, and matplotlib (and a few other libraries) provide a means of very quickly, and with a few lines of code, getting the gist of what you are trying to discover, as well as the underlying message (and displaying it beautifully too).

In this chapter, we examined many of the most common means of visualizing data from pandas. There are also a lot of interesting visualizations that were not covered, and indeed, the concept of data visualization with pandas and/or Python is the subject of entire texts, but I believe this chapter provides a much-needed reference to get up and going with the visualizations that provide most of what is needed.

In the next and final chapter of the book, we will look at a few applied applications of pandas, which also demonstrates visualizing real-world information.

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Learning Pandas
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