Pandas

Fetch data from a spreadsheet into Python.

Run custom functions on DataFrame rows or columns.

Stack DataFrames vertically or horizontally.

Combine two DataFrames based on common columns.

Inner, left, right, and outer joins explained.

Reshape data into summary tables.

Count occurrences of unique values in a column.

Pull year, month, day from datetime columns.

Convert strings to datetime objects.

Work with text data using the str accessor.

Pull specific parts from strings using patterns.

Apply multiple functions to grouped data.

Group rows by column values for aggregate analysis.

Find null and NaN values in your data.

Remove rows or columns with null values.

Replace null values with meaningful substitutes.

Create new columns in a DataFrame.

Calculate new columns from existing ones.

Remove unwanted columns from a DataFrame.

Change column names in a DataFrame.

Order rows based on column values.

Select rows that match specific criteria.

Check if values are in a list of options.

Combine conditions with AND and OR logic.

Write filters as readable strings.

Access single or multiple columns from a DataFrame.

Access specific rows using iloc for position-based selection.

Build DataFrames from dictionaries, lists, and other sources.

Load data from CSV files into a DataFrame.

Quickly inspect your data with head, tail, and info.

Understanding the core data structure in pandas.