Created
August 6, 2017 19:25
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feel the NaN Handling of pandas dataframe
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| import numpy as np | |
| import pandas as pd | |
| nan = np.nan | |
| median_1 = np.median([1,2,3]) | |
| median_2 = np.median([0,1,2,3]) | |
| median_3 = np.median([0,0,1,2,3]) | |
| median_4 = np.median([0,0,0,1,2,3]) | |
| mean_1 = np.median([1,2,3]) | |
| mean_2 = np.median([0,1,2,3]) | |
| mean_3 = np.median([0,0,1,2,3]) | |
| mean_4 = np.median([0,0,0,0,1,2,3]) | |
| # the dataframe will ignore the nan, see the impact below | |
| df = pd.DataFrame([nan,nan,nan,1,2,3], columns=['A']) | |
| dfa = df.mean() | |
| dfb = df.median() | |
| print(df) | |
| print('\n') | |
| print('\n') | |
| print(dfa) | |
| print(dfb) | |
| print('\n') | |
| print('\n') | |
| # median | |
| print(median_1) | |
| print(median_2) | |
| print(median_3) | |
| print(median_4) | |
| print('\n') | |
| # mean | |
| print(mean_1) | |
| print(mean_2) | |
| print(mean_3) | |
| print(mean_4) | |
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