Write a Pandas program to select the rows where the score is missing, i.e. is NaN.
Example 1:
Input: DataFrame with some 'score' values as NaN Output: Rows with 'score' as NaN
Example 2:
Input: DataFrame with some 'score' values as NaN Output: Rows with 'score' as NaN
Use the pandas isnull() function to identify rows with NaN values.
import pandas as pd
import numpy as np
def select_nan_rows(df):
return df[df['score'].isnull()]
df1 = pd.DataFrame({'name': ['Tom', 'Jack', 'Steve', 'Ricky'], 'score': [85, np.nan, 87, 91]})
df2 = pd.DataFrame({'name': ['Adam', 'Eve', 'Stark', 'Rogers'], 'score': [88, 92, np.nan, 97]})
print(select_nan_rows(df1))
print(select_nan_rows(df2))
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