How Do I Parse Semi-Colon Separated CSV Files Using Pandas?

Parsing Semi Colon Separated .CSV Files Using Pandas

When dealing with comma-separated values (CSV) files, it's essential to properly handle separators to ensure accurate data parsing. Pandas provides a straightforward solution for reading CSV files with non-standard separators, such as semi colons.

Consider this scenario: you have a .csv file with a format similar to the following:

a1;b1;c1;d1;e1;...
a2;b2;c2;d2;e2;...    

To import this file into a pandas DataFrame, you can use the read_csv() function. However, by default, pandas assumes that the separator is a comma. To specify a semi colon separator, use the sep parameter as follows:

<code class="python">import pandas as pd

csv_path = "C:...."
data = pd.read_csv(csv_path, sep=';')</code>

If you forget to specify the sep parameter, the default behavior of pandas is to treat all data as a single column, resulting in erroneous results when printing the DataFrame.

The reason for this default behavior is that pandas assumes that commas are the most common separator. By providing the sep parameter, you explicitly instruct pandas to use semi colons as separators, ensuring the correct parsing of your data.

In summary, when dealing with semi colon-separated CSV files in pandas, always remember to specify sep=';' in the read_csv() function to obtain accurate data parsing.

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