| import pandas as pd |
| import numpy as np |
| import matplotlib.pyplot as plt |
| import scipy.stats as stats |
| import pylab |
| |
|
|
| |
| def diagnostic_plots(df, variable): |
| |
| |
| |
| plt.figure(figsize=(15,6)) |
| plt.subplot(1, 2, 1) |
| df[variable].hist() |
|
|
| plt.subplot(1, 2, 2) |
| stats.probplot(df[variable], dist="norm", plot=pylab) |
|
|
| plt.show() |
| |
| |
| def log_transform(data,cols=[]): |
| """ |
| Logarithmic transformation |
| """ |
| |
| data_copy = data.copy(deep=True) |
| for i in cols: |
| data_copy[i+'_log'] = np.log(data_copy[i]+1) |
| print('Variable ' + i +' Q-Q plot') |
| diagnostic_plots(data_copy,str(i+'_log')) |
| return data_copy |
|
|
|
|
| def reciprocal_transform(data,cols=[]): |
| """ |
| Reciprocal transformation |
| """ |
| |
| data_copy = data.copy(deep=True) |
| for i in cols: |
| data_copy[i+'_reciprocal'] = 1/(data_copy[i]) |
| print('Variable ' + i +' Q-Q plot') |
| diagnostic_plots(data_copy,str(i+'_reciprocal')) |
| return data_copy |
|
|
|
|
| def square_root_transform(data,cols=[]): |
| """ |
| square root transformation |
| """ |
| |
| data_copy = data.copy(deep=True) |
| for i in cols: |
| data_copy[i+'_square_root'] = (data_copy[i])**(0.5) |
| print('Variable ' + i +' Q-Q plot') |
| diagnostic_plots(data_copy,str(i+'_square_root')) |
| return data_copy |
|
|
|
|
| def exp_transform(data,coef,cols=[]): |
| """ |
| exp transformation |
| """ |
| |
| data_copy = data.copy(deep=True) |
| for i in cols: |
| data_copy[i+'_exp'] = (data_copy[i])**coef |
| print('Variable ' + i +' Q-Q plot') |
| diagnostic_plots(data_copy,str(i+'_exp')) |
| return data_copy |
|
|
|
|
