1st method
If you have a dataset that is uniform , you can use .info() to figure out , which column has missing values.
housingdata.info()
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
# Column Non-Null Count Dtype
0 longitude 20640 non-null float64
1 latitude 20640 non-null float64
2 housing_median_age 20640 non-null float64
3 total_rooms 20640 non-null float64
4 total_bedrooms 20433 non-null float64
5 population 20640 non-null float64
6 households 20640 non-null float64
7 median_income 20640 non-null float64
8 median_house_value 20640 non-null float64
9 ocean_proximity 20640 non-null object
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
Look at 4 total_bedrooms , it has less non-null values , hence it has missing data
Second method
housingdata.isnull().sum()
longitude 0
latitude 0
housing_median_age 0
total_rooms 0
total_bedrooms 207
population 0
households 0
median_income 0
median_house_value 0
ocean_proximity 0
dtype: int64
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