How to Read Files with Semi-Colon Separators in Pandas?

Reading Files with Semi-Colon Separators in Pandas

Introduction:
Pandas provides convenient functions to read and parse data from various file formats. Here, we explore how to read semi-colon separated files using the read_csv function.

Problem Statement:
When attempting to import a comma-separated file, pandas compresses all columns into a single entry. The task is to read the file correctly, splitting values into columns using the semi-colon (;) separator.

Solution:
The solution lies in the sep parameter of the read_csv function. By default, sep is set to ',' (comma). To handle semi-colon separated files, explicitly provide sep=';' as follows:

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

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

Explanation:
By specifying sep=';', pandas recognizes the semi-colon as the field separator and correctly parses the data into separate columns. This addresses the issue of having all columns crammed into a single entry.

Additional Information:
The sep parameter in read_csv supports character-based or regular expression-based separators, allowing for customization in parsing data from different formats.

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