How to Import and Export Data Using Pandas

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How to Import and Export Data Using Pandas 

In data science and analytics, Pandas is a powerful Python library used to import, analyze, and export data efficiently. Whether you're working with CSVs, Excel files, JSON, or SQL databases, Pandas makes the process easy and seamless.

📥 Importing Data

To start, first import Pandas:

python
import pandas as pd

1. CSV File

python
df = pd.read_csv('data.csv')

2. Excel File

python
df = pd.read_excel('data.xlsx')

3. JSON File

python
df = pd.read_json('data.json')

4. SQL Database

python
import sqlite3
conn = sqlite3.connect('data.db')
df = pd.read_sql('SELECT * FROM table_name', conn)

You can also handle missing values, set column names, and specify data types during import using optional parameters like na_values, names, or dtype.

📤 Exporting Data

Once data is cleaned or transformed, you can export it for sharing or further use:

1. To CSV

python
df.to_csv('output.csv', index=False)

2. To Excel

python
df.to_excel('output.xlsx', index=False)

3. To JSON

python
df.to_json('output.json')

4. To SQL

python
df.to_sql('table_name', conn, if_exists='replace', index=False)

Read more:

Understanding Data Types in Python for Analytics

Installing and Setting Up Python for Data Analysis

Your First Data Analytics Project in Python

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