Mastering Exploratory Analysis with pandas
Build an end-to-end data analysis workflow with Python
BIRMINGHAM - MUMBAI
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Working with Different Kinds of Datasets
- Using advanced options while reading data from CSV files
- Importing modules
- Advanced read options
- Specifying a different row as a header
- Specifying a column as an index
- Choosing a subset of columns to be read
- Handling missing and NA data
- Choosing whether to skip over blank rows
- Skipping rows from the footer or end of the file
- Reading the subset of a file or a certain number of rows
- Reading data from Excel files
- Handling missing data while reading
- Reading data from other popular formats
- Summary
- Using advanced options while reading data from CSV files
- Data Selection
- Manipulating, Transforming, and Reshaping Data
- Modifying a pandas DataFrame using the inplace parameter
- Using the groupby method
- Handling missing values in pandas
- Indexing in pandas DataFrames
- Renaming columns in a pandas DataFrame
- Removing columns from a pandas DataFrame
- Working with date and time series data
- Handling SettingWithCopyWarning
- Applying a function to a pandas series or DataFrame
- Merging and concatenating multiple DataFrames into one
- Summary
- Visualizing Data Like a Pro
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