Python

df.isnull().values.any()

#return a subset of the dataframe where the column name value == NaN 
df.loc[df['column name'].isnull() == True]

df.isnull().sum().sum()
5

> df.isnull().any().any()
True

df['your column name'].isnull().sum()

# to mark NaN column as True
df['your column name'].isnull()

df['your column name'].isnull().values.any()

df.isna().sum().sum()

df.isnull().sum().sum()

import numpy as np
import pandas as pd

val = np.nan

print(pd.isnull(val))
# True

for i, row in df.iterrows():
value = row["Name"]
if pd.isnull(value):
    dosomething()

# Check for nan values and store them in dataset named (nan_values)
nan_data = data.isna()
nan_data.head()

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