The Pandas library is a popular data manipulation tool among data scientists and analysts. It provides data structures for efficiently storing and manipulating large datasets and tools for working with structured data.

Functions

Here's a comprehensive list of functions available in Pandas:

Data Structures

• pandas.DataFrame: This function creates a 2-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or a SQL table.
• pandas.Series: This function creates a one-dimensional labeled array capable of holding any data type.

Input/Output Functions

• pandas.read_csv: This function reads a CSV file into a Pandas DataFrame.
• pandas.read_excel: This function reads an Excel file into a Pandas DataFrame.

Data Manipulation Functions

• pandas.concat: This function concatenates Pandas objects along a particular axis with optional set logic along the other axes.
• pandas.merge: This function merges DataFrame or named Series objects with a database-style join.
• pandas.pivot_table: This function creates a spreadsheet-style pivot table as a DataFrame.
• pandas.crosstab: This function computes a simple cross-tabulation of two (or more) factors.
• pandas.DataFrame.groupby: This function groups DataFrame using a mapper or by a Series of columns.
• pandas.DataFrame.apply: This function applies a function along an axis of the DataFrame.
• pandas.DataFrame.filter: This function returns a subset of the DataFrame columns based on the column names.
• pandas.DataFrame.fillna: This function fills missing values in the DataFrame.
• pandas.DataFrame.drop: This function drops specified labels from rows or columns of a DataFrame.
• pandas.DataFrame.sort_values: This function sorts a DataFrame by its values.