How to Skip Rows During CSV Import in Pandas

When working with large datasets in Python, importing data from CSV files is a common task. Pandas is a popular library for data manipulation and analysis that provides a simple and powerful way to read CSV files into a pandas DataFrame. However, sometimes you may need to skip rows during the CSV import process. In this article, we will show you how to do this using Pandas.

Table of Contents

1. The Problem: Why Skip Rows During CSV Import?
2. The Solution: How to Skip Rows During CSV Import
3. Common Errors and How to Handle Them
4. Conclusion

The Problem: Why Skip Rows During CSV Import?

Sometimes, CSV files may contain rows that are not relevant to the analysis or contain information that is not useful. These rows could be header rows, summary rows, or simply rows that are not structured the same way as the rest of the data.

Skipping these rows during CSV import can help improve the efficiency of your analysis by reducing the size of the DataFrame and eliminating irrelevant data. Additionally, it can prevent errors that may arise from trying to analyze data that is not structured correctly.

The Solution: How to Skip Rows During CSV Import

Pandas provides several parameters that allow you to skip rows during CSV import. These parameters are:

• skiprows: This parameter allows you to specify the number of rows to skip from the top of the CSV file.
• header: This parameter allows you to specify the row number(s) to use as the column names. If you set this parameter to an integer, Pandas will skip all rows up to and including the specified row. If you set it to a list of integers, Pandas will use those rows as the column names and skip all other rows up to the row specified by skiprows.
• skipfooter: This parameter allows you to specify the number of rows to skip from the bottom of the CSV file. Let’s consider the following csv file

|   Name   | Age | Salary |
|----------|-----|--------|
|  John    |  25 | 50000  |
|  Alice   |  30 | 60000  |
|   Bob    |  28 | 55000  |
|   Eva    |  35 | 70000  |
| Charlie  |  22 | 48000  |
|  David   |  32 | 62000  |
| Sophia   |  29 | 58000  |
|  Frank   |  40 | 75000  |
|  Grace   |  26 | 52000  |
|  Oliver  |  33 | 64000  |

Example 1: Skip Rows at the Top of the CSV File

Suppose you have a CSV file that contains a header row and several summary rows at the top of the file that you want to skip during import. Here’s how you can do it using the skiprows parameter:

import pandas as pd

# Import CSV file and skip the first 3 rows
df = pd.read_csv('data.csv', skiprows=3)
print(df)

In this example, we imported the CSV file named data.csv and skipped the first 3 rows using the skiprows parameter. Pandas will read the CSV file starting from the fourth row and create a DataFrame with the remaining rows.

Output:

       Bob  28  55000
0      Eva  35  70000
1  Charlie  22  48000
2    David  32  62000
3   Sophia  29  58000
4    Frank  40  75000
5    Grace  26  52000
6   Oliver  33  64000

Example 2: Skip Rows at the Bottom of the CSV File

Suppose you have a CSV file that contains a summary row at the bottom of the file that you want to skip during import. Here’s how you can do it using the skipfooter parameter:

import pandas as pd

# Import CSV file and skip the last row
df = pd.read_csv('data.csv', skipfooter=1)

In this example, we imported the CSV file named data.csv and skipped the last row using the skipfooter parameter. Pandas will read the CSV file up to the second-to-last row and create a DataFrame with the remaining rows.

Output:

      Name  Age  Salary
0     John   25   50000
1    Alice   30   60000
2      Bob   28   55000
3      Eva   35   70000
4  Charlie   22   48000
5    David   32   62000
6   Sophia   29   58000
7    Frank   40   75000
8    Grace   26   52000

Example 3: header Parameter and Custom Skip

By adjusting the header parameter, you can skip rows dynamically. If your header is on, say, the third row, set header=2.

import pandas as pd

df = pd.read_csv('data.csv', header=2)
print(df)

Output:

     Alice  30  60000
0      Bob  28  55000
1      Eva  35  70000
2  Charlie  22  48000
3    David  32  62000
4   Sophia  29  58000
5    Frank  40  75000
6    Grace  26  52000
7   Oliver  33  64000

Common Errors and How to Handle Them

Error 1: Inconsistent Data Types

When your CSV file contains columns with inconsistent data types, Pandas may raise a TypeError.

Solution:

Specify the dtype parameter to enforce data types during import.

Error 2: Blank or Missing Values

Blank or missing values can cause unexpected behavior during import, leading to ParserError.

Solution:

Use the na_values parameter to handle missing values.

Error 3: Incorrect Header Detection

If the header is not correctly detected, column alignment errors may occur.

Solution:

Manually specify the header using the header parameter.

df = pd.read_csv('example.csv', dtype={'Age': int}, na_values=['N/A'], header=0)
print(df)

Conclusion

Skipping rows during CSV import in Pandas is a simple and powerful way to reduce the size of your DataFrame and eliminate irrelevant data. By using the skiprows, header, and skipfooter parameters, you can skip rows at the top or bottom of the CSV file and set the column names for the DataFrame. By following the examples in this article, you should now be able to skip rows during CSV import in Pandas and improve the efficiency and accuracy of your data analysis.

SHARE:

About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Request a demo today to learn more.