Skipping Rows During CSV Import with Pandas

When using pandas.read_csv() to import CSV data, you may want to skip certain rows. However, the skiprows parameter can be confusing, as it accepts both a list and an integer.

The skiprows parameter allows you to specify rows to skip from the beginning of the file. If you provide a list of row numbers, it will skip those rows. If you provide an integer, it will skip that number of rows.

For example, if you have a CSV file where the second row contains unnecessary data and you want to skip it, you can use any of the following methods:

Skiprow as a List (Recommended)

import pandas as pd
from io import StringIO

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

# Skip the second row using a list
df = pd.read_csv(StringIO(s), skiprows=[1], header=None)

# Output: Row with index 1 skipped
print(df)

Skiprow as an Integer

# Skip the second row using an integer
df = pd.read_csv(StringIO(s), skiprows=1, header=None)

# Output: Row with index 1 skipped
print(df)

Note that using skiprows=1 skips the first row, while skiprows=[1] skips the row with index 1. This is because Python uses 0-based indexing, where the first element in a list has index 0.

Conclusion

By understanding the behavior of the skiprows parameter, you can effectively skip unwanted rows during CSV import using pandas.