Effective Pandas: Patterns for Data Manipulation
- Length: 497 pages
- Edition: 1
- Language: English
- Publisher: Independently published
- Publication Date: 2021-12-08
- ISBN-10: B09MYXXSFM
- ISBN-13: 9798772692936
- Sales Rank: #17185 (See Top 100 Books)
Best practices for manipulating data with Pandas. This book will arm you with years of knowledge and experience that are condensed into an easy to follow format. Rather than taking months reading blogs and websites and searching mailing lists and groups, this book will teach you how to write good Pandas code.
It covers:
- Series manipulation
- Creating columns
- Summary statistics
- Grouping, pivoting, and cross-tabulation
- Time series data
- Visualization
- Chaining
- Debugging code
- and more…
Introduction Who this book is for Data in this Book Hints, Tables, and Images Installation Anaconda Pip Jupyter Overview Summary Exercises Data Structures Summary Exercises Series Introduction The index abstraction The pandas Series The NaN value Optional Integer Support for NaN Similar to NumPy Categorical Data Summary Exercises Series Deep Dive Loading the Data Series Attributes Summary Exercises Operators (& Dunder Methods) Introduction Dunder Methods Index Alignment Broadcasting Iteration Operator Methods Chaining Summary Exercises Aggregate Methods Aggregations Count and Mean of an Attribute .agg and Aggregation Strings Summary Exercises Conversion Methods Automatic Conversion Memory Usage String and Category Types Ordered Categories Converting to Other Types Summary Exercises Manipulation Methods .apply and .where If Else with Pandas Missing Data Filling In Missing Data Interpolating Data Clipping Data Sorting Values Sorting the Index Dropping Duplicates Ranking Data Replacing Data Binning Data Summary Exercises Indexing Operations Prepping the Data and Renaming the Index Resetting the Index The .loc Attribute The .iloc Attribute Heads and Tails Sampling Filtering Index Values Reindexing Summary Exercises String Manipulation Strings and Objects Categorical Strings The .str Accessor Searching Splitting Optimizing .apply with Cython Replacing Text Summary Exercises Date and Time Manipulation Date Theory Loading UTC Time Data Loading Local Time Data Converting Local time to UTC Converting to Epochs Manipulating Dates Summary Exercises Dates in the Index Finding Missing Data Filling In Missing Data Interpolation Dropping Missing Values Shifting Data Rolling Average Resampling Gathering Aggregate Values (But Keeping Index) Groupby Operations Cumulative Operations Summary Exercises Plotting with a Series Plotting in Jupyter The .plot Attribute Histograms Box Plot Kernel Density Estimation Plot Line Plots Line Plots with Multiple Aggregations Bar Plots Pie Plots Styling Summary Exercises Categorical Manipulation Categorical Data Frequency Counts Benefits of Categories Conversion to Ordinal Categories The .cat Accessor Category Gotchas Generalization Summary Exercises Dataframes Database and Spreadsheet Analogues A Simple Python Version Dataframes Construction Dataframe Axis Summary Exercises Similarities with Series and DataFrame Getting the Data Viewing Data Summary Exercises Math Methods in DataFrames Index Alignment Duplicate Index Entries Summary Exercises Looping and Aggregation For Loops Aggregations The .apply Method Summary Exercises Columns Types, .assign, and Memory Usage Conversion Methods Memory Usage Summary Exercises Creating and Updating Columns Loading the Data More Column Cleanup Summary Exercises Dealing with Missing and Duplicated Data Missing Data Duplicates Summary Exercises Sorting Columns and Indexes Sorting Columns Sorting Column Order Setting and Sorting the Index Summary Exercises Filtering and Indexing Operations Renaming an Index Resetting the Index Dataframe Indexing, Filtering, & Querying Indexing by Position Indexing by Name Filtering with Functions & .loc .query vs .loc Summary Exercises Plotting with Dataframes Lines Plots Bar Plots Scatter Plots Area Plots and Stacked Bar Plots Column Distributions with KDEs, Histograms, and Boxplots Summary Exercises Reshaping Dataframes with Dummies Dummy Columns Undoing Dummy Columns Summary Exercises Reshaping By Pivoting and Grouping A Basic Example Using a Custom Aggregation Function Multiple Aggregations Per Column Aggregations Grouping by Hierarchy Grouping with Functions Summary Exercises More Aggregations Aggregations while Keeping Rows Filtering Parts of Groups Summary Exercises Cross-tabulation Deep Dive Cross-tabulation Summaries Adding Margins Normalizing Results Hierarchical Columns with Cross Tabulations Heatmaps Summary Exercises Melting, Transposing, and Stacking Data Melting Data Un-melting Data Transposing Data Stacking & Unstacking Stacking Flattening Hierarchical Indexes and Columns Summary Exercises Working with Time Series Loading the Data Adding Timezone Information Exploring the Data Slicing Time Series Missing Timeseries Data Exploring Seasonality Resampling Data Rules with Offset Aliases Combining Offset Aliases Anchored Offset Aliases Resampling to Finer-grain Frequency Grouping a Date Column with pd.Grouper Summary Exercises Joining Dataframes Adding Rows to Dataframes Adding Columns to Dataframes Joins Join Indicators Merge Validation Joining Data Example Dirty Devil Flow and Weather Data Joining Data Validating Joined Data Visualization of Merged Data Summary Exercises Exporting Data Dirty Devil Data Reading and Writing Creating CSV Files Exporting to Excel Feather SQL JSON Summary Exercises Styling Dataframes Loading the Data Sparklines The .style Attribute Formatting Embedding Bar Plots Highlighting Heatmaps and Gradients Captions CSS Properties Stickiness and Hiding Hiding the Index Summary Exercises Debugging Pandas Checking if Dataframes are Equal Debugging Chains Debugging Chains Part II Debugging Chains Part III Debugging Chains Part IV Debugging Apply (and Friends) Memory Usage Timing Information Summary Exercises Summary About the Author Index Also Available One more thing
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