Data Loading#

Efficient data loading is fundamental to any data analysis process. Pandas offers several functions to read data from different formats, making it easier to manipulate and analyze the data. In this chapter, we will explore how to read data from CSV files, Excel files, and SQL databases using Pandas.

Read CSV File#

The read_csv function is used to load data from CSV files into a DataFrame. This function is highly customizable with numerous parameters to handle different formats and data types. Here is a basic example:

import pandas as pd

# Load data from a CSV file into a DataFrame
df = pd.read_csv('filename.csv')

This command reads data from 'filename.csv' and stores it in the DataFrame df. The file path can be a URL or a local file path.

Read Excel File#

To read data from an Excel file, use the read_excel function. This function supports reading from both xls and xlsx file formats and allows you to specify the sheet to be loaded.

# Load data from an Excel file into a DataFrame
df = pd.read_excel('filename.xlsx')

This reads the first sheet in the Excel workbook 'filename.xlsx' by default. You can specify a different sheet by using the sheet_name parameter.

Read from SQL Database#

Pandas can also load data directly from a SQL database using the read_sql function. This function requires a SQL query and a connection object to the database.

import sqlalchemy

# Create a connection to a SQL database
engine = sqlalchemy.create_engine('sqlite:///example.db')
query = "SELECT * FROM my_table"

# Load data from a SQL database into a DataFrame
df = pd.read_sql(query, engine)

This example demonstrates how to connect to a SQLite database and read data from 'my_table' into a DataFrame.