Importing and Exporting Data

Learn how to import data from various file formats into Pandas Data Frames and export Data Frames back into these formats for efficient data handling.

Overview

This lesson is centered on importing and exporting data using Pandas. We’ll cover how to read data from various sources like CSV, Excel, and YAML files into Pandas Data Frames, and similarly, how to export Data Frames into these file formats. Mastering these skills is essential for efficient data handling and analysis.

Importing Data

Reading from CSV

To read data from a CSV file into a Data Frame, use pd.read_csv():

import pandas as pd

# Reading from a CSV file
df = pd.read_csv('path/to/your/file.csv')
print(df)

Reading from Excel

Reading from an Excel file is just as straightforward:

# Reading from an Excel file
df = pd.read_excel('path/to/your/file.xlsx')
print(df)

Removing duplicates

To remove duplicate rows from a Data Frame, use df.drop_duplicates():

# Removing duplicate rows
df = df.drop_duplicates()

Reading from YAML

To read YAML data, you’ll need an additional library, PyYAML:

import yaml
import pandas as pd

# Reading from a YAML file
with open('path/to/your/file.yaml', 'r') as file:
    yaml_data = yaml.safe_load(file)

df = pd.DataFrame(yaml_data)
print(df)

Exporting Data

Writing to CSV

You can export a Data Frame to a CSV file using df.to_csv():

# Writing to a CSV file
df.to_csv('path/to/your/newfile.csv')

Writing to Excel

Similarly, to export to an Excel file:

# Writing to an Excel file
df.to_excel('path/to/your/newfile.xlsx')

Summary

In this lesson, we’ve covered the fundamentals of importing and exporting data using Pandas. You’ve learned how to work with different file formats, which is a key part of the data analysis workflow.

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