Python - How to drop the null rows from a Pandas DataFrame

To drop null (NaN) rows from a Pandas DataFrame, use the dropna() method. This method removes any row containing at least one null value by default.

Creating a DataFrame with Null Values

Let's create a sample DataFrame with some null values to demonstrate ?

import pandas as pd
import numpy as np

# Create sample data with NaN values
data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0, np.nan, np.nan, 120.0, 100.0]
}

dataFrame = pd.DataFrame(data)
print("Original DataFrame:")
print(dataFrame)
print(f"\nShape: {dataFrame.shape}")

Original DataFrame:
          Car       Place  UnitsSold
0        Audi   Bangalore       80.0
1     Porsche      Mumbai      110.0
2  RollsRoyce        Pune        NaN
3         BMW       Delhi      200.0
4    Mercedes   Hyderabad       80.0
5 Lamborghini  Chandigarh        NaN
6        Audi      Mumbai        NaN
7    Mercedes        Pune      120.0
8 Lamborghini       Delhi      100.0

Shape: (9, 3)

Using dropna() to Remove Null Rows

The dropna() method removes all rows containing any null values ?

import pandas as pd
import numpy as np

# Create sample data with NaN values
data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0, np.nan, np.nan, 120.0, 100.0]
}

dataFrame = pd.DataFrame(data)

# Remove rows with null values
clean_dataFrame = dataFrame.dropna()
print("DataFrame after removing null values:")
print(clean_dataFrame)
print(f"\nUpdated Shape: {clean_dataFrame.shape}")

DataFrame after removing null values:
          Car       Place  UnitsSold
0        Audi   Bangalore       80.0
1     Porsche      Mumbai      110.0
3         BMW       Delhi      200.0
4    Mercedes   Hyderabad       80.0
7    Mercedes        Pune      120.0
8 Lamborghini       Delhi      100.0

Updated Shape: (6, 3)

dropna() Parameters

The dropna() method has several useful parameters ?

import pandas as pd
import numpy as np

# Create DataFrame with multiple null columns
data = {
    'A': [1, 2, np.nan, 4],
    'B': [np.nan, 2, 3, 4],
    'C': [1, np.nan, np.nan, 4]
}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Drop rows where ALL values are null (how='all')
print("\nUsing how='all':")
print(df.dropna(how='all'))

# Drop rows based on specific subset of columns
print("\nUsing subset=['A', 'B']:")
print(df.dropna(subset=['A', 'B']))

Original DataFrame:
     A    B    C
0  1.0  NaN  1.0
1  2.0  2.0  NaN
2  NaN  3.0  NaN
3  4.0  4.0  4.0

Using how='all':
     A    B    C
0  1.0  NaN  1.0
1  2.0  2.0  NaN
2  NaN  3.0  NaN
3  4.0  4.0  4.0

Using subset=['A', 'B']:
     A    B    C
1  2.0  2.0  NaN
2  NaN  3.0  NaN
3  4.0  4.0  4.0

Key Parameters

Conclusion

Use dropna() to remove rows with null values from a DataFrame. Use parameters like how='all' or subset to control which rows are dropped based on your specific needs.