#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()
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.isnull().sum().sum()
import numpy as np
import pandas as pd
val = np.nan
print(pd.isnull(val))
# True
import pandas as pd
## df1 as an example data frame
## col1 name of column for which you want to calculate the nan values
sum(pd.isnull(df1['col1']))
df['your column name'].isnull()
# Check for nan values and store them in dataset named (nan_values)
nan_data = data.isna()
nan_data.head()