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import pandas as pd | |
import numpy as np | |
#setting up a comparable dataframe | |
df = pd.DataFrame(np.random.randint(20,100,size=(50, 4)), columns=['A','B','C','D']) | |
#these two columns become a multi-column index | |
df['year_idx'] = np.random.randint(2000,2004,50) | |
df['id_idx'] = np.random.randint(10000,19999,50) | |
df.drop_duplicates(subset=['year_idx','id_idx'],inplace=True) | |
df.set_index(['year_idx','id_idx'], inplace=True) | |
#add the year again as a normal column for easy access | |
df.loc[:,'year'] = df.index.get_level_values('year_idx') | |
#print(df) | |
#split out one of the years for separate analysis | |
#NOTE: THIS IS WHAT WAS GENERATING THE WARNING. NEEDED TO ADD .COPY() up here | |
#LOL after adding .loc everywhere because of the warning... | |
df_low = df.loc[(df['A'] < 90), :] #.copy() | |
df_high = df.loc[(df['A'] >= 90), :] #.copy() | |
#now going to group the values in column B into 5 bins and create a new column with the bin number | |
df_low['B_bin'] = pd.qcut(df_low['B'],5,labels=False) | |
#print(df) | |
#display the minimum value per bin | |
print('\nmin B value per bin:\n',df_low.groupby(['B_bin'])['B'].min()) | |
#now want to do the same thing, but WANT THE BINS TO BE DETERMINED BY YEAR | |
for yr in df_low['year'].unique(): | |
#this works, but gives warning | |
df_low.loc[df_low['year'] == yr,'B_bin_by_year'] = pd.qcut(df_low.loc[df_low['year'] == yr,'B'],8,labels=False) | |
print(df) | |
#display the minimum value per bin per year | |
print('\nmin B value per bin:\n',df_low.groupby(['year','B_bin_by_year'])['B'].min().unstack()) |
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