I/O with Pandas

I/O with Pandas

Installing Pandas

!pip install pandas

Importing Pandas

import pandas as pd

Reading Data with Pandas

Note: We need a raw form of data in csv, xlsv, sql and json format to execute this code. Make sure that name of the file should same in the code. For example, data.csv,data.xlsx, and data.json.

1. Reading Data from CSV

import pandas as pd
data = pd.read_csv('data.csv')
print(data)

Output

2. Reading Data from Excel

import pandas as pd
data = pd.read_excel('data.xlsx')
print(data)

Output

3. Reading Data from SQL Database

import pandas as pd
import sqlite3
connection = sqlite3.connect(‘codingninjas.db')
query = "SELECT * FROM players"
data = pd.read_sql(query, connection)
connection.close()
print(data)

Output

4. Reading Data from JSON

import pandas as pd
data = pd.read_json('data.json')
print(data)

Output

Writing Data to Different Formats

Writing Data to CSV

import pandas as pd
data = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Aditya', 'Shubh', 'Hritik'], 'Age': [19, 20, 23]})
data.to_csv('output.csv', index=False)

We use Pandas to create a DataFrame named 'data' with three columns: 'ID,' 'Name', and 'Age', containing data. Then we use the to_csv() function to save this DataFrame as a CSV file named 'output.csv.' The index=False argument confirms that the DataFrame's index is not in the CSV file.

Writing Data to Excel

import pandas as pd
data = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Aditya', 'Shubh', 'Hritik'], 'Age': [19, 20, 23]})
data.to_excel('output.xlsx', index=False)

We use Pandas to create a DataFrame named 'data' with three columns: 'ID,' 'Name', and 'Age', containing data. Then we use the to_excel() method to save this DataFrame as a Excel file named 'output.xlsx.' The index=False argument confirms that the DataFrame's index is not in the Excel file.

Writing Data to SQL Database

import pandas as pd
import sqlite3
data = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Aditya', 'Shubh', 'Hritik'], 'Age': [19, 20, 23]})
connection = sqlite3.connect('codingninjas.db')
data.to_sql(players, connection, if_exists='replace', index=False)
connection.close()

We Imported Pandas and SQLite libraries and created a DataFrame called 'data' with data. Connects to an SQLite database named 'codingninjas.db'. Writes the data from the DataFrame to an SQLite table named 'players,' replacing it if it already exists.Closes the database connection.

Writing Data to JSON

import pandas as pd
data = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Aditya', 'Shubh', 'Hritik'], 'Age': [19, 20, 23]})
data.to_json('output.json', orient='records')

We use Pandas to create a DataFrame named 'data' with three columns: 'ID,' 'Name', and 'Age', containing data. Then we use the to_json() method to save this DataFrame as a JSON file named 'output.json.'' The orient='records' parameter specifies the JSON format, then each row of the DataFrame becomes a separate JSON file.

Frequently Asked Questions

Can Pandas work with databases?

Yes, Pandas can work with various databases using libraries like SQLAlchemy. We can read data from databases using pd.read_sql() and write data using to_sql().

How to import Pandas in a Python script?

We can import Pandas in our script by import pandas as pd. The handle pd is generally used for Pandas to make code more straightforward.

Is Pandas suitable for handling huge datasets?

Pandas are optimized for in-memory data processing, and performance can be problematic with big datasets. For big data, distributed computing frameworks like Apache Spark might be more suitable.

Conclusion

Mastering input and output operations with Pandas opens a world of data manipulation and analysis possibilities. By seamlessly reading and writing data from and to various sources, Pandas empowers users to harness the insights hidden within datasets. 

In this article, we learn about I/O with Pandas. We also learn about Pandas. We concluded the article by discussing I/O with Pandas.

To better understand the topic, refer to 

• Getting started with Pandas 
• Pandas and Datasets 
• Advanced functions in Pandas
• Must Know Functions in pandas 

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