Read Text File into DataFrame in Python 📄

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Certainly! To read values from a text file and put them into a DataFrame in Python, you can use the pandas library. Pandas provides a convenient read_csv function that can also handle other delimited text files. In this tutorial, we'll focus on a basic example using a comma-separated values (CSV) file.
If you haven't installed the pandas library, you can install it using the following command:
Create a text file with comma-separated values (CSV) for this example. Let's assume you have a file named data.txt with the following content:
Create a Python script (e.g., read_file_to_dataframe.py) and import the pandas library. Use the read_csv function to read the data from the text file into a DataFrame.
Save the script and run it using the following command:
Specify Delimiter:
If your file has a different delimiter (e.g., tab-separated values), you can specify it using the sep parameter in the read_csv function:
Header and Index:
By default, read_csv assumes the first row of the file as the header. If your file doesn't have a header, you can set header=None and manually provide column names. You can also specify the index column using the index_col parameter.
Skipping Rows:
If your file contains non-data lines (e.g., comments) at the beginning, you can skip them using the skiprows parameter.
Adjust these options based on the specifics of your text file. The pandas library is versatile and can handle various file formats and configurations.
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