# (1) Count NaN values under a single DataFrame column:
df['column name'].isna().sum()
#(2) Count NaN values under an entire DataFrame:
df.isna().sum().sum()
#(3) Count NaN values across a single DataFrame row:
df.loc[[index value]].isna().sum().sum()#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()
# will give count of nan values of every column.
df.isna().sum()
# Count NaN values under a single DataFrame column:
df['column name'].isna().sum()
# Count NaN values under an entire DataFrame:
df.isna().sum().sum()
# Count NaN values across a single DataFrame row:
df.loc[[index value]].isna().sum().sum()df[col].count()