Here are some of the most frequently used Pandas commands, grouped by their primary functions:

1. Data Loading and Saving:

• pd.read_csv(): Reads data from a CSV file into a DataFrame.
• pd.read_excel(): Reads data from an Excel file into a DataFrame.
• pd.read_json(): Reads data from a JSON file into a DataFrame.
• pd.to_csv(): Saves a DataFrame to a CSV file.
• pd.to_excel(): Saves a DataFrame to an Excel file.
• pd.to_json(): Saves a DataFrame to a JSON file.

2. Data Inspection and Selection:

• df.head(): Displays the first few rows of a DataFrame.
• df.tail(): Displays the last few rows of a DataFrame.
• df.shape: Returns the dimensions (rows and columns) of a DataFrame.
• df.info(): Provides summary information about the DataFrame’s columns, including data types and missing values.
• df.describe(): Calculates descriptive statistics for numerical columns.
• df.columns: Returns a list of column names.
• df.loc[]: Selects data by label.
• df.iloc[]: Selects data by position.

3. Data Cleaning and Handling Missing Values:

• df.isnull(): Identifies missing values in a DataFrame.
• df.fillna(): Fills missing values with specified values.
• df.dropna(): Drops rows or columns with missing values.

4. Data Manipulation and Transformation:

• df.sort_values(): Sorts a DataFrame by one or more columns.
• df.groupby(): Groups data by one or more columns and applies aggregate functions.
• df.apply(): Applies a function to each row or column of a DataFrame.
• df.merge(): Combines DataFrames based on common columns (like SQL joins).
• df.concat(): Concatenates DataFrames along rows or columns.

5. Data Aggregation and Summary:

• df.sum(): Calculates the sum of values in a column.
• df.mean(): Calculates the mean of values in a column.
• df.median(): Calculates the median of values in a column.
• df.count(): Counts the number of non-null values in a column.
• df.max(): Finds the maximum value in a column.
• df.min(): Finds the minimum value in a column.