df[df.isnull().any(axis=1)]
df.isnull().values.any()
# position of NaN values in terms of index
df.loc[pandas.isna(df["b"]), :].index
# position of NaN values in terms of rows that cotnain NaN
df.loc[pandas.isna(df["b"]), :]
df['your column name'].isnull().sum()
df['your column name'].isnull().values.any()
# to mark NaN column as True
df['your column name'].isnull()
df['your column name'].isnull().values.any()
df.isnull().sum().sum()
# Check for nan values and store them in dataset named (nan_values)
nan_data = data.isna()
nan_data.head()