How to skip rows while reading csv file using Pandas

Python's Pandas library provides the read_csv() function to read CSV files with flexible options for skipping rows. This is useful for data cleaning, removing headers, or filtering specific rows during data import.

Syntax

pandas.read_csv('filename.csv', skiprows=condition)

Parameters:

• filename.csv Path to the CSV file

• skiprows Rows to skip. Can be an integer, list, or lambda function

Creating Sample Data

Let's create a sample CSV file for demonstration ?

import pandas as pd

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank'],
    'Age': [25, 30, 35, 28, 32, 45],
    'City': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney', 'Berlin']
}

df_sample = pd.DataFrame(data)
df_sample.to_csv('sample.csv', index=False)
print("Sample CSV created:")
print(df_sample)

Sample CSV created:
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris
3    Diana   28     Tokyo
4      Eve   32    Sydney
5    Frank   45    Berlin

Method 1: Skip Single Row

Skip the first row (index 0) ?

import pandas as pd

# Skip first row
df = pd.read_csv('sample.csv', skiprows=1)
print(df)

    Alice  25  New York
0     Bob  30    London
1 Charlie  35     Paris
2   Diana  28     Tokyo
3     Eve  32    Sydney
4   Frank  45    Berlin

Method 2: Skip Multiple Specific Rows

Skip rows at specific indices using a list ?

import pandas as pd

# Skip rows at index 1 and 3 (Bob and Diana)
df = pd.read_csv('sample.csv', skiprows=[1, 3])
print(df)

      Name  Age      City
0    Alice   25  New York
1  Charlie   35     Paris
2      Eve   32    Sydney
3    Frank   45    Berlin

Method 3: Skip Rows Using Lambda Function

Skip odd-numbered rows using a lambda function ?

import pandas as pd

# Skip odd rows (keep even-indexed rows)
df = pd.read_csv('sample.csv', skiprows=lambda x: x % 2 != 0)
print(df)

      Name  Age      City
0    Alice   25  New York
1  Charlie   35     Paris
2      Eve   32    Sydney

Method 4: Skip First N Rows

Skip the first 3 rows ?

import pandas as pd

# Skip first 3 rows
df = pd.read_csv('sample.csv', skiprows=3)
print(df)

   Diana  28   Tokyo
0    Eve  32  Sydney
1  Frank  45  Berlin

Method 5: Combine skiprows with nrows

Skip specific rows and limit the number of rows to read ?

import pandas as pd

# Skip row 2, read only 3 rows total
df = pd.read_csv('sample.csv', skiprows=[2], nrows=3)
print(df)

     Name  Age      City
0   Alice   25  New York
1     Bob   30    London
2   Diana   28     Tokyo

Comparison

Conclusion

The skiprows parameter in read_csv() provides flexible options for data filtering during import. Use integers for simple skipping, lists for specific rows, or lambda functions for conditional logic based on your data cleaning needs.