Handling Missing Values

Use isnull().sum() to count missing values per column:

import pandas as pd
import numpy as np
 
data = {
    'Name': ['Alice','Bob','Carol','Dan'],
    'Age': [25, np.nan, 30, np.nan],
    'Score': [88, 92, np.nan, 79]
}
df = pd.DataFrame(data)
print(df.isnull().sum())

Python

Output

Use dropna() to remove all rows that contain at least one missing value:

import pandas as pd
import numpy as np
 
data = {'A': [1, np.nan, 3, 4],
        'B': [5, 6, np.nan, 8]}
df = pd.DataFrame(data)
clean = df.dropna()
print(clean)

Python

Output

Use fillna() to replace missing values with a fixed value or a computed one:

import pandas as pd
import numpy as np
 
data = {'Score': [88, np.nan, 91, np.nan, 76]}
df = pd.DataFrame(data)
 
# Fill with a fixed value
fixed = df['Score'].fillna(0)
 
# Fill with the column mean
mean_filled = df['Score'].fillna(
    df['Score'].mean()
)
print(fixed.tolist())
print(mean_filled.tolist())

Python

Output

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Handling Missing Values

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