Data manipulation is a crucial aspect of data science, and Python provides several libraries and functions to facilitate this process. Here’s a list of important functions and methods primarily used for data manipulation in Python:
1. Pandas Library:
pandas: A powerful library for data manipulation and analysis.DataFrame(): Creates a two-dimensional labeled data structure.Series(): Creates a one-dimensional labeled array.read_csv(),read_excel(),read_sql(): Reads data from different file formats or databases.head(),tail(): Displays the first or last n rows of a DataFrame.info(): Provides a concise summary of a DataFrame.describe(): Generates descriptive statistics of a DataFrame.shape: Returns the dimensions (rows, columns) of a DataFrame.columns: Returns the column labels of a DataFrame.loc[],iloc[]: Accesses a group of rows and columns by label or integer position.drop(): Removes specified rows or columns from a DataFrame.fillna(): Fills missing values in a DataFrame.groupby(): Groups data based…
