How to Fill Missing Values Across Columns with Pandas: A Step‑by‑Step Guide

1. Introduction

Hello, I'm a Python advanced learner. In a WeChat group a user asked how to use Pandas to fill missing values in one column with values from another column.

The original data is shown below.

2. Solution

One contributor suggested a simple one‑liner: df["col1"].fillna(df[col2]) A more complete solution reads two Excel sheets, defines a helper function to match rows, applies it with apply, and writes the result back to Excel:

import pandas as pd

file = ""
output_filename = ""

data1 = pd.read_excel(file, sheet_name='Sheet1', dtype={'eventdate': 'datetime64[ns]', 'u1': 'datetime64[ns]'})
d2 = pd.read_excel(file, sheet_name='Sheet2', dtype={'f1': 'datetime64[ns]', 'f2': 'datetime64[ns]'})

def match_description(s, df, compare_col, value_col):
    """Return the first value of value_col where compare_col equals s, or None."""
    compare_data = df[df[compare_col] == s].copy()
    if compare_data.empty:
        return None
    return compare_data[value_col].values[0]

# Apply matching for two columns
d2['gbvibforwardrms'] = d2['f1'].apply(match_description, args=(data1, 'u1', 'gbvibforwardrms'))
d2['gbvibforwardrms1'] = d2['f2'].apply(match_description, args=(data1, 'eventdate', 'gbvibforwardrms'))

# Fill missing values
d2['c'] = d2['gbvibforwardrms'].fillna(d2['gbvibforwardrms1']).fillna('')

# Save result
d2['c'].to_excel(output_filename, sheet_name='data3')

This approach successfully resolves the original issue.

3. Conclusion

The article demonstrates a practical Pandas technique for filling missing values across columns, provides complete code, and acknowledges the contributors who shared their solutions.