Here is my code.
label_encoder = LabelEncoder()
rainfall['station_name'] = label_encoder.fit_transform(rainfall['station_name'])
Here is my code.
label_encoder = LabelEncoder()
rainfall['station_name'] = label_encoder.fit_transform(rainfall['station_name'])
It's possible that you have empty values within the columns. This can be checked with:
rainfall.isnull().any()
If you quickly want to get rid of any and all rows missing values you can use:
rainfall= rainfall.dropna()
If you only want to remove rows where the station_name is missing use:
rainfall.dropna(subset=['rainfall'])
label_encoder = LabelEncoder()
In order to resolve this, you need to completely convert your feature i.e., station_name into an str data type.
rainfall['station_name'] = rainfall['station_name'].astype(str)
Then encode the feature:
rainfall['station_name'] = label_encoder.fit_transform(rainfall['station_name'])