如何使用 pandas 高效地为每个客户附加多个 KPI 值？
pivoted df 与 customers df 的联接会产生一些问题，因为国家/地区是透视数据框的索引，而国籍不在索引中。
countryKPI = pd.DataFrame({'country':['Austria','Germany', 'Germany', 'Austria'],
'indicator':['z','x','z','x'],
'value':[7,8,9,7]})
customers = pd.DataFrame({'customer':['first','second'],
'nationality':['Germany','Austria'],
'value':[7,8]})
查看所需的粉红色结果:
最佳答案
我认为你可以使用concat :
df_pivoted = countryKPI.pivot_table(index='country',
columns='indicator',
values='value',
fill_value=0)
print (df_pivoted)
indicator x z
country
Austria 7 7
Germany 8 9
print (pd.concat([customers.set_index('nationality'), df_pivoted], axis=1))
customer value x z
Austria second 8 7 7
Germany first 7 8 9
print (pd.concat([customers.set_index('nationality'), df_pivoted], axis=1)
.reset_index()
.rename(columns={'index':'nationality'})
[['customer','nationality','value','x','z']])
customer nationality value x z
0 second Austria 8 7 7
1 first Germany 7 8 9
按评论编辑:
问题是 customers.nationality 列的 dtypes 和 countryKPI.country 是 category 并且如果某些类别是缺失，会引发错误:
ValueError: incompatible categories in categorical concat
解决方案通过 union 查找常见类别然后set_categories :
import pandas as pd
import numpy as np
countryKPI = pd.DataFrame({'country':['Austria','Germany', 'Germany', 'Austria'],
'indicator':['z','x','z','x'],
'value':[7,8,9,7]})
customers = pd.DataFrame({'customer':['first','second'],
'nationality':['Slovakia','Austria'],
'value':[7,8]})
customers.nationality = customers.nationality.astype('category')
countryKPI.country = countryKPI.country.astype('category')
print (countryKPI.country.cat.categories)
Index(['Austria', 'Germany'], dtype='object')
print (customers.nationality.cat.categories)
Index(['Austria', 'Slovakia'], dtype='object')
all_categories =countryKPI.country.cat.categories.union(customers.nationality.cat.categories)
print (all_categories)
Index(['Austria', 'Germany', 'Slovakia'], dtype='object')
customers.nationality = customers.nationality.cat.set_categories(all_categories)
countryKPI.country = countryKPI.country.cat.set_categories(all_categories)
df_pivoted = countryKPI.pivot_table(index='country',
columns='indicator',
values='value',
fill_value=0)
print (df_pivoted)
indicator x z
country
Austria 7 7
Germany 8 9
Slovakia 0 0
print (pd.concat([customers.set_index('nationality'), df_pivoted], axis=1)
.reset_index()
.rename(columns={'index':'nationality'})
[['customer','nationality','value','x','z']])
customer nationality value x z
0 second Austria 8.0 7 7
1 NaN Germany NaN 8 9
2 first Slovakia 7.0 0 0
如果需要更好的性能，请改为 pivot_table使用groupby :
df_pivoted1 = countryKPI.groupby(['country','indicator'])
.mean()
.squeeze()
.unstack()
.fillna(0)
print (df_pivoted1)
indicator x z
country
Austria 7.0 7.0
Germany 8.0 9.0
Slovakia 0.0 0.0
时间:
In [177]: %timeit countryKPI.pivot_table(index='country', columns='indicator', values='value', fill_value=0)
100 loops, best of 3: 6.24 ms per loop
In [178]: %timeit countryKPI.groupby(['country','indicator']).mean().squeeze().unstack().fillna(0)
100 loops, best of 3: 4.28 ms per loop
关于python - Pandas 为一列附加多列，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/39634312/