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

Patricia Arquette
Release: 2024-10-29 18:30:02
Original
462 people have browsed it

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))
Copy after login

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.

The above is the detailed content of How does the `skiprows` argument in Pandas CSV import work with integers and lists?. For more information, please follow other related articles on the PHP Chinese website!

source:php.cn
Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Latest Articles by Author
Popular Tutorials
More>
Latest Downloads
More>
Web Effects
Website Source Code
Website Materials
Front End Template
About us Disclaimer Sitemap
php.cn:Public welfare online PHP training,Help PHP learners grow quickly!