How to Replace a Header with the Top Row in a Pandas DataFrame   

In this post you can learn how to replace a header with the top row in a Pandas DataFrame

1: Replace the Header with the First Row

If your DataFrame has no header and the first row contains the correct column names, you can promote it to the header:

import pandas as pd

# Example DataFrame without a header  
df = pd.DataFrame([
    ["Name", "Age", "City"],
    ["Alice", 25, "New York"],
    ["Bob", 30, "Los Angeles"]
])

df

the result is:

Now we can use the first row as a header by:

# Set the first row as header and remove it from data  
df.columns = df.iloc[0]  # Assign first row as column names  
df = df[1:]              # Drop the first row (now redundant)  
df.reset_index(drop=True, inplace=True)  # Reset index  

Result:

2: When Reading a CSV File

If you know your data structure beforehand, you can handle this during the import process:

CSV has no headers

df = pd.read_csv("file.csv", header=None)  # Read without header  
df.columns = ['header1', 'header2']

Use Some Row as a header

or use any row as a header:

df = pd.read_csv('file.csv', header=3)

Note: this might skip some rows from the file.

3. Multiple Header Rows

Sometimes datasets have multi-level headers spread across several rows. In such cases:

df.columns = df.iloc[0].astype(str) + '_' + df.iloc[1].astype(str)
df = df[2:].reset_index(drop=True)

4. Cleaning Header Names

After setting new headers, you might need to clean them:

df.columns = df.columns.str.strip().fillna('Unknown')

This will remove extra whitespace and handle NaN values.