dropna Method¶

The dropna() method removes rows or columns containing missing values. It is useful when missing data cannot be reliably imputed.

Basic Usage¶

Drop rows with any missing values.

1. Drop Rows¶

import pandas as pd
import numpy as np

url = "https://raw.githubusercontent.com/codebasics/py/master/pandas/5_handling_missing_data_fillna_dropna_interpolate/weather_data.csv"
df = pd.read_csv(url, index_col='day', parse_dates=True)
print(df)

dg = df.dropna()
print(dg)

2. Drop Columns¶

df.dropna(axis=1)  # Drop columns with any NaN

3. Return Copy¶

# dropna returns a new DataFrame
dg = df.dropna()
# Original df is unchanged

LeetCode Example: Student Names¶

Drop students with missing names.

1. Problem Data¶

students = pd.DataFrame({
    'id': [1, 2, 3, 4, 5],
    'name': ['Alice', None, 'Bob', None, 'Charlie'],
    'grade': ['A', 'B', 'B+', 'A-', 'C']
})

2. Drop Missing Names¶

result = students.dropna(subset=['name'])
print(result)

3. Result¶

   id     name grade
0   1    Alice     A
2   3      Bob    B+
4   5  Charlie     C

LeetCode Example: Employee Data¶

Drop employees with any missing values.

1. Problem Data¶

filtered_employees = pd.DataFrame({
    'employee_id': [1, 2, 3, 4, 5],
    'manager_id': [2.0, None, None, 2.0, 3.0],
    'salary': [25000, 35000, 28000, None, 32000]
})

2. Drop All NaN Rows¶

cleaned_employees = filtered_employees.dropna()
print(cleaned_employees)

3. Result¶

Only rows with complete data remain:

   employee_id  manager_id  salary
0            1         2.0   25000
4            5         3.0   32000

Practical Considerations¶

When to use dropna vs fillna.

1. Use dropna When¶

Missing data is random and limited
Filling would introduce bias
Sufficient data remains after dropping

2. Avoid dropna When¶

Missing data is systematic
Dropping loses too much information
Missing values can be reasonably estimated

3. Check Impact¶

print(f"Before: {len(df)} rows")
print(f"After: {len(df.dropna())} rows")
print(f"Dropped: {len(df) - len(df.dropna())} rows")