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