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.