用Pandas计算NPS

Question

我对Python非常非常陌生，我正在考虑如何计算NPS分数。

计算结果如下：

(记分9-10/总分0-10) -(记分0-6/总分0-10分)

我正在使用的数据框架：

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需要分别计算每个理事会的核动力源。这是我在这里的第一篇文章，希望这是有意义的。如果有人能为我指明正确的方向，我将不胜感激。

干杯，本。

Answer 1

假设数据在data.csv中

import pandas as pd
from collections import defaultdict

df = pd.read_csv('data.csv')

high_nps = defaultdict(lambda: 0)
low_nps = defaultdict(lambda: 0)

high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council])  for council in total_nps}

print(nps_score)

指纹：

{'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}

Answer 2

def npsForField(df,column,fid):
    nps={}

    # first make sure our column has numeric values:
    subject = pd.DataFrame(columns=[column],data=pd.to_numeric(df[df['field_id']==fid][column]))

    # calculate all NPS components:
    nps['total']      = subject[column].count()
    nps['detractors'] = subject[subject[column]<7][column].count()
    nps['passives']   = subject[(subject[column]>6) & (subject[column]<9)][column].count()
    nps['promoters']  = subject[subject>8][column].count()

    nps['nps']        = (nps['promoters'] - nps['detractors']) / nps['total']

    return nps

然后，假设您希望为df的一个名为answer的列计算NPS，但只在df[df['field_id']==fid]中计算。就这样说吧：

npsForField(df, column='answer', fid='abc123')

抽样结果：

{'total': 979,
 'detractors': 313,
 'passives': 291,
 'promoters': 375,
 'nps': 0.06332992849846783}