fill-missing-values

# fill with mode, mean, or median
df_mode, df_mean, df_median = df.mode().iloc[0], df.mean(), df.median()

df_fill_mode = df.fillna(df_mode)
df_fill_mean = df.fillna(df_mean)
df_fill_median = df.fillna(df_median)

# drop col with any missing values
df_drop_na_col = df.dropna(axis=1)





#To impute the null values with median
from sklearn.base import TransformerMixin

class DataFrameImputer(TransformerMixin):

    def __init__(self):
        """Impute missing values.

        Columns of dtype object are imputed with the most frequent value 
        in column.

        Columns of other types are imputed with mean of column.

        """
    def fit(self, X, y=None):

        self.fill = pd.Series([X[c].value_counts().index[0]
            if X[c].dtype == np.dtype('O') else X[c].mode() for c in X],
            index=X.columns)

        return self

    def transform(self, X, y=None):
        return X.fillna(self.fill)
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df_filtered3 = DataFrameImputer().fit_transform(df2)
df_filtered3.isnull().sum()
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