I'm looking at a data set of scores.
I want to know the probability of each score based on the bin the score falls in using pd.cut
How can I take a value and assign it a probability based on the outputted table?
Code as follows
import pandas as pd
data = pd.DataFrame({'scores':[168.0, 44.0, 352.0, 128.0, 268.0, 228.0, 160.0, 376.0, 304.0, 124.0, 360.0, 36.0, 224.0, 176.0, 40.0, 28.0, 264.0, 292.0, 228.0, 80.0, 216.0, 132.0, 88.0, 220.0, 284.0, 308.0, 256.0, 360.0, 364.0, 128.0, 268.0, 72.0, 100.0, 320.0, 224.0, 300.0, 232.0, 316.0, 196.0, 248.0, 24.0, 396.0, 8.0, 248.0, 244.0, 392.0, 240.0, 28.0, 260.0, 220.0, 120.0, 56.0, 232.0, 216.0, 228.0, 232.0, 332.0, 280.0, 148.0, 84.0, 284.0, 268.0, 176.0, 324.0, 52.0, 112.0, 344.0, 296.0, 164.0, 28.0, 304.0, 344.0, 232.0, 340.0, 324.0, 248.0, 232.0, 400.0, 396.0, 36.0, 52.0, 204.0, 292.0, 96.0, 68.0, 392.0, 260.0, 224.0, 236.0, 248.0, 316.0, 292.0, 212.0, 276.0, 304.0, 124.0, 216.0, 48.0, 64.0, 228.0]})
frequencyTable = pd.cut(data['scores'], bins = 20, include_lowest=True, ordered=True, precision=4, right=False)
frequencyTable = frequencyTable.value_counts(sort=False)
frequencyTable = frequencyTable.reset_index()
frequencyTable['probability'] = frequencyTable['scores']/len(data)
print(frequencyTable)Output as follows
index scores probability
0 [8.0, 27.6) 2 0.02
1 [27.6, 47.2) 7 0.07
2 [47.2, 66.8) 5 0.05
3 [66.8, 86.4) 4 0.04
4 [86.4, 106.0) 3 0.03
5 [106.0, 125.6) 4 0.04
6 [125.6, 145.2) 3 0.03
7 [145.2, 164.8) 3 0.03
8 [164.8, 184.4) 3 0.03
9 [184.4, 204.0) 1 0.01
10 [204.0, 223.6) 7 0.07
11 [223.6, 243.2) 14 0.14
12 [243.2, 262.8) 8 0.08
13 [262.8, 282.4) 6 0.06
14 [282.4, 302.0) 7 0.07
15 [302.0, 321.6) 7 0.07
16 [321.6, 341.2) 4 0.04
17 [341.2, 360.8) 5 0.05
18 [360.8, 380.4) 2 0.02
19 [380.4, 400.392) 5 0.05I'd like to be able to take input = 265 and return 6%
1条答案
按热度按时间l7wslrjt1#
frequencyTable是一个表，其中第一列是Interval，第三列是百分比。因此，为了得到你想要的结果，你可以遍历这个表，寻找输入值（v=265）在该行的Interval中的项，如果是，你就取第三列中的值。所以，类似这样：结果：