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Counting Missing Values in Pandas – Best Techniques 2026

Accurately counting missing values is the foundation of any good data cleaning process. In 2026, Pandas provides several efficient and informative ways to count missing values, from simple totals to detailed per-column and per-row breakdowns.

TL;DR — Most Useful Commands

df.isna().sum() – Count missing values per column
df.isna().sum().sum() – Total missing values in the entire DataFrame
df.isna().mean() * 100 – Percentage of missing values per column
df.isna().sum(axis=1) – Missing values per row

1. Basic Counting of Missing Values

import pandas as pd

df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])

# Count missing values in each column
missing_count = df.isna().sum()
print("Missing values per column:")
print(missing_count.sort_values(ascending=False))

# Total number of missing values in the whole dataset
total_missing = df.isna().sum().sum()
print(f"
Total missing values in dataset: {total_missing}")

2. Percentage of Missing Values

# Percentage missing per column (very useful for reporting)
missing_pct = df.isna().mean() * 100
print("Percentage of missing values per column:")
print(missing_pct.round(2).sort_values(ascending=False))

3. Row-wise Missing Value Count

# How many missing values are in each row?
row_missing = df.isna().sum(axis=1)

print(f"Rows with at least one missing value: {(row_missing > 0).sum()}")
print("
Distribution of missing values per row:")
print(row_missing.value_counts().sort_index())

4. Best Practices in 2026

Always start with df.isna().sum() to see missing values per column
Use df.isna().mean() * 100 to understand the percentage of missing data
Sort the results with .sort_values(ascending=False) to focus on the worst columns first
Check row-wise missingness with df.isna().sum(axis=1) when rows are important
Combine counting with visualization (bar plot or heatmap) for better understanding

Conclusion

Counting missing values accurately is the first critical step in data cleaning. In 2026, the combination of df.isna().sum() for counts and df.isna().mean() * 100 for percentages gives you a clear picture of data quality. Always analyze both column-wise and row-wise missingness before deciding how to handle the missing data.

Next steps: