How does the `skiprows` argument in Pandas CSV import work with integers and lists?

Understanding Skiprows Argument in Pandas csv Import

When importing CSV files into pandas, the skiprows argument can be used to exclude or include specific rows in the dataset. However, its usage can be ambiguous, raising questions about its functionality.

As per the pandas documentation, skiprows can take a list-like argument or an integer. If a list-like argument is provided, it represents the row numbers to skip (0-indexed). However, if an integer is given, it signifies the number of rows to skip at the start of the file.

The crux of the question lies in understanding how the integer value differentiates between skipping the first row and the row with index 1. To simplify, let's consider an example:

import pandas as pd
from io import StringIO

s = """1, 2
... 3, 4
... 5, 6"""

print(pd.read_csv(StringIO(s), skiprows=[1], header=None))

print(pd.read_csv(StringIO(s), skiprows=1, header=None))

Here, we provide both a list and an integer value to skiprows. As you can observe:

• When skiprows=[1], it excludes the row with index 1 (i.e., the third line).
• When skiprows=1, it skips the first row completely.

This behavior clarifies that:

• If skiprows is a list, it specifically skips the rows with the corresponding indices.
• If skiprows is an integer, it always skips the first n rows, where n is the integer value provided.

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