How to Fill Missing Values in Pandas Using Conditional Column Matching

1. Introduction

Hello, I am a Python enthusiast. In a Python community a user asked a Pandas question about filling missing values based on another column. Below is the discussion screenshot.

The original data looks like this:

The goal is to fill one column with values from another column when certain conditions are met.

2. Solution

One contributor posted a quick answer (screenshot below):

Simple one‑liner: df["col1"].fillna(df["col2"]) A more robust solution using a helper function and apply:

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 based on exact time match
d2['gbvibforwardrms'] = d2['f1'].apply(match_description, args=(data1, 'u1', 'gbvibforwardrms'))
# Apply based on date match
d2['gbvibforwardrms1'] = d2['f2'].apply(match_description, args=(data1, 'eventdate', 'gbvibforwardrms'))

# Combine results, fill missing values, and replace remaining NaNs with empty string
d2['c'] = d2['gbvibforwardrms'].fillna(d2['gbvibforwardrms1']).fillna('')

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

The code successfully resolves the missing‑value issue.

3. Additional Tips

Several common pandas data‑analysis tricks were also shared:

Cheat‑sheet for quick reference: Pandas cheat‑sheet

4. Conclusion

This article presented a specific Pandas problem, offered both a concise one‑liner and a flexible function‑based solution, and provided additional data‑analysis tips to help readers handle column‑wise missing‑value filling effectively.