# Pandas DataFrame The Pandas DataFrame is a two-dimensional, size-mutable, and possibly heterogeneous tabular data format with labelled axes. A data frame is a two-dimensional data structure in which the data can be organised in rows and columns. Pandas DataFrames are comprised of three main components: data, rows, and columns. In the real world, Pandas DataFrames are formed by importing datasets from existing storage, which can be a Excel file, a SQL database or CSV file. Pandas DataFrames may be constructed from lists, dictionaries, or lists of dictionaries, etc. Features of Pandas `DataFrame`: - **Size mutable**: DataFrames are mutable in size, meaning that new rows and columns can be added or removed as needed. - **Labeled axes**: DataFrames have labeled axes, which makes it easy to keep track of the data. - **Arithmetic operations**: DataFrames support arithmetic operations on rows and columns. - **High performance**: DataFrames are highly performant, making them ideal for working with large datasets. ### Installation of libraries `pip install pandas`
`pip install xlrd` - **Note**: The `xlrd` library is used for Excel operations. Example for reading data from an Excel File: ```python import pandas as pd l = pd.read_excel('example.xlsx') d = pd.DataFrame(l) print(d) ``` Output: ```python Name Age 0 John 12 ``` Example for Inserting Data into Excel File: ```python import pandas as pd l = pd.read_excel('file_name.xlsx') d = {'Name': ['Bob', 'John'], 'Age': [12, 28]} d = pd.DataFrame(d) L = pd.concat([l, d], ignore_index = True) L.to_excel('file_name.xlsx', index = False) print(L) ``` Output: ```python Name Age 0 Bob 12 1 John 28 ``` ### Usage of Pandas DataFrame: - Can be used to store and analyze financial data, such as stock prices, trading data, and economic data. - Can be used to store and analyze sensor data, such as data from temperature sensors, motion sensors, and GPS sensors. - Can be used to store and analyze log data, such as web server logs, application logs, and system logs