Say I have a dataframe, df:
>>> df
Age Score
19 1
20 2
24 3
19 2
24 3
24 1
24 3
20 1
19 1
20 3
22 2
22 1
I want to construct a new dataframe that bins Age and stores their average scores of the bins in Score:
Age Score
19-21 1.6667
22-24 2.1667
This is my way of doing it, which I feel is kind of convoluted:
import numpy as np
import pandas as pd
data = pd.DataFrame(columns=['Age', 'Score'])
data['Age'] = [19,20,24,19,24,24,24,20,19,20,22,22]
data['Score'] = [1,2,3,2,3,1,3,1,1,3,2,1]
_, bins = np.histogram(data['Age'], 2)
df1 = data[data['Age']<int(bins[1])]
df2 = data[data['Age']>int(bins[1])]
new_df = pd.DataFrame(columns=['Age', 'Score'])
new_df['Age'] = [str(int(bins[0]))+'-'+str(int(bins[1])), str(int(bins[1]))+'-'+str(int(bins[2]))]
new_df['Score'] = [np.mean(df1.Score), np.mean(df2.Score)]
Apart from being lengthy, this way doesn't scale well for more bins (as we'd need to write each entry for each bin in new_df).
Is there a more efficient, clean way of doing this?
Use cut for bin values into discrete intervals, last aggregate mean:
bins = [19, 21, 24]
#dynamically create labels
labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])]
labels[0] = '{}-{}'.format(bins[0], bins[1])
print (labels)
['19-21', '22-24']
binned = pd.cut(data['Age'], bins=bins, labels=labels, include_lowest=True)
df = data.groupby(binned)['Score'].mean().reset_index()
print (df)
Age Score
0 19-21 1.666667
1 22-24 2.166667