Mean, median & mode imputations

Dealing with Missing Data in Python

Suraj Donthi

Deep Learning & Computer Vision Consultant

Basic imputation techniques

  • constant (e.g. 0)
  • mean
  • median
  • mode or most frequent
Dealing with Missing Data in Python

Mean Imputation

from sklearn.impute import SimpleImputer

diabetes_mean = diabetes.copy(deep=True)

mean_imputer = SimpleImputer(strategy='mean')
Dealing with Missing Data in Python

Mean Imputation

from sklearn.impute import SimpleImputer
diabetes_mean = diabetes.copy(deep=True)
mean_imputer = SimpleImputer(strategy='mean')
diabetes_mean.iloc[:, :] = mean_imputer.fit_transform(diabetes_mean)
Dealing with Missing Data in Python

Median imputation

diabetes_median = diabetes.copy(deep=True)
median_imputer = SimpleImputer(strategy='median')
diabetes_median.iloc[:, :] = median_imputer.fit_transform(diabetes_median)
Dealing with Missing Data in Python

Mode imputation

diabetes_mode = diabetes.copy(deep=True)
mode_imputer = SimpleImputer(strategy='most_frequent')
diabetes_mode.iloc[:, :] = mode_imputer.fit_transform(diabetes_mode)
Dealing with Missing Data in Python

Imputing a constant

diabetes_constant = diabetes.copy(deep=True)
constant_imputer = SimpleImputer(strategy='constant', fill_value=0))
diabetes_constant.iloc[:, :] = constant_imputer.fit_transform(diabetes_constant)
Dealing with Missing Data in Python

Scatterplot of imputation

nullity = diabetes['Serum_Insulin'].isnull()+diabetes['Glucose'].isnull()

diabetes_mean.plot(x='Serum_Insulin', y='Glucose', kind='scatter', alpha=0.5,

                   c=nullity, cmap='rainbow', title='Mean Imputation')

Scatterplot of mean imputed diabetes dataframe

Dealing with Missing Data in Python

Visualizing imputations

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))

nullity = diabetes['Serum_Insulin'].isnull()+diabetes['Glucose'].isnull()

imputations = {'Mean Imputation': diabetes_mean, 
               'Median Imputation': diabetes_median, 
               'Most Frequent Imputation': diabetes_mode, 
               'Constant Imputation': diabetes_constant}

for ax, df_key in zip(axes.flatten(), imputations):

    imputations[df_key].plot(x='Serum_Insulin', y='Glucose', kind='scatter', 
                             alpha=0.5, c=nullity, cmap='rainbow', ax=ax, 
                             colorbar=False, title=df_key)
Dealing with Missing Data in Python

Graphical visualization of imputations of diabetes dataframe

Dealing with Missing Data in Python

Summary

You learned to

  • Impute with statistical parameters like mean, median and mode
  • Graphically compare the imputations
  • Analyze the imputations
Dealing with Missing Data in Python

Let's practice!

Dealing with Missing Data in Python

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