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By Kevin McAleer, 2 Minutes
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.
To read data from a CSV file into a Data Frame, use pd.read_csv():
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 an Excel file is just as straightforward:
# Reading from an Excel file df = pd.read_excel('path/to/your/file.xlsx') print(df)
To remove duplicate rows from a Data Frame, use df.drop_duplicates():
df.drop_duplicates()
# Removing duplicate rows df = df.drop_duplicates()
To read YAML data, you’ll need an additional library, PyYAML:
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)
You can export a Data Frame to a CSV file using df.to_csv():
df.to_csv()
# Writing to a CSV file df.to_csv('path/to/your/newfile.csv')
Similarly, to export to an Excel file:
# Writing to an Excel file df.to_excel('path/to/your/newfile.xlsx')
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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