没有使用 groupby() 和 mean() 聚合的数字类型

Question

我正在尝试确定流失率。如果我尝试使用.size() 执行，它可以工作。但如果我用.mean() 写它，它不会。我不明白为什么它不起作用，因为我需要找出平均值。

from sklearn.metrics import classification_report,confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings("ignore")
from pylab import rcParams
%matplotlib inline
import plotly.offline as pyoff
import plotly.graph_objs as go
import xgboost as xgb
from sklearn.model_selection import KFold, cross_val_score, train_test_split
import xgboost as xgb

#initate plotly
pyoff.init_notebook_mode()

#read data from csv and redo the data work we done before
df_data = pd.read_csv(r'C:\Users\aayus\OneDrive\Desktop\College Project\4. Churn Prediction\churn-data.csv', encoding='latin1')
df_data.head(10)
df_data.info()

df_data.loc[df_data.Churn=='No','Churn'] = 0
df_data.loc[df_data.Churn=='Yes','Churn'] = 1

df_plot = df_data.groupby('gender').Churn.size().reset_index()

这行得通。但是如果我用

改变最后一行

df_plot = df_data.groupby('gender').Churn.mean().reset_index()

它给出“没有要聚合的数字类型”。

DataError                                 Traceback (most recent call last)

<ipython-input-1-4875501f5fb5> in <module>
     26 df_data.loc[df_data.Churn=='No','Churn'] = 0
     27 df_data.loc[df_data.Churn=='Yes','Churn'] = 1
---> 28 f_plot = df_data.groupby('gender').Churn.mean().reset_index()

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in mean(self, *args, **kwargs)
   1222         """
   1223         nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
-> 1224         return self._cython_agg_general(
   1225             "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
   1226         )

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
    905 
    906         if len(output) == 0:
--> 907             raise DataError("No numeric types to aggregate")
    908 
    909         return self._wrap_aggregated_output(output)

DataError: No numeric types to aggregate

DataError                                 Traceback (most recent call last)

<ipython-input-1-4875501f5fb5> in <module>
     26 df_data.loc[df_data.Churn=='No','Churn'] = 0
     27 df_data.loc[df_data.Churn=='Yes','Churn'] = 1
---> 28 f_plot = df_data.groupby('gender').Churn.mean().reset_index()

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in mean(self, *args, **kwargs)
   1222         """
   1223         nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
-> 1224         return self._cython_agg_general(
   1225             "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
   1226         )

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
    905 
    906         if len(output) == 0:
--> 907             raise DataError("No numeric types to aggregate")
    908 
    909         return self._wrap_aggregated_output(output)

DataError: No numeric types to aggregate

DataError                                 Traceback (most recent call last)

<ipython-input-1-4875501f5fb5> in <module>
     26 df_data.loc[df_data.Churn=='No','Churn'] = 0
     27 df_data.loc[df_data.Churn=='Yes','Churn'] = 1
---> 28 f_plot = df_data.groupby('gender').Churn.mean().reset_index()

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in mean(self, *args, **kwargs)
   1222         """
   1223         nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
-> 1224         return self._cython_agg_general(
   1225             "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
   1226         )

~\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
    905 
    906         if len(output) == 0:
--> 907             raise DataError("No numeric types to aggregate")
    908 
    909         return self._wrap_aggregated_output(output)

DataError: No numeric types to aggregate

非常感谢您的帮助。 这是 df_data.head(10) 的输出

电信数据集

Answer 1

确保您在流失列中没有任何其他值。使用 df_data['Churn'].value_counts()。 如果您可以共享前 10 行数据，那就太棒了。