Checking for Missing Values in a Pandas DataFrame
Pandas: Data Validation Exercise-1 with Solution
Write a Pandas program to check for missing values in a DataFrame.
This exercise demonstrates how to check for missing values in a DataFrame using isna() and any().
Sample Solution :
Code :
import pandas as pd
# Create a sample DataFrame with missing values
df = pd.DataFrame({
'Name': ['Orville', 'Arturo', 'Ruth', None],
'Age': [25, 30, None, 22],
'Salary': [50000, None, 70000, 60000]
})
# Check if there are any missing values in the DataFrame
missing_values = df.isna().any()
# Output the result
print(missing_values)
Output:
Name True Age True Salary True dtype: bool
Explanation:
- Created a DataFrame with missing values.
- Used isna() to check for missing values and any() to determine if any column contains missing values.
- Displayed the result, which shows True for columns with missing data.
Python-Pandas Code Editor:
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