Pandas 如何将多列分组为逗号分隔的输出
我有以下数据帧Pandas 如何将多列分组为逗号分隔的输出,pandas,Pandas,我有以下数据帧 import pandas as pd d= { 'ID':[1,2,3,4,5], 'Fruit':['Jack','Apple','Guava','Orange','Apple], 'Market':['k','r','r','t','r] } df= pd.DataFrame(data=d) df 对于groupby水果和市场,以下是代码 df.groupby('Fruit')['Market'].value_counts().reset_inde
import pandas as pd
d= {
'ID':[1,2,3,4,5],
'Fruit':['Jack','Apple','Guava','Orange','Apple],
'Market':['k','r','r','t','r]
}
df= pd.DataFrame(data=d)
df
对于groupby水果和市场,以下是代码
df.groupby('Fruit')['Market'].value_counts().reset_index(name='Count')
但是如何获得以下输出呢
Market Fruit1 Fruit2 Count Individual-Count1 Individual-Count2
r Apple Guava 3 2 1
k Jack 1 1
t Orange 1 1
只能在水果1、水果2上使用唯一值。
i、 e按市场和水果分组,在计数列中计数,在新列中以逗号分隔的值对水果进行单独计数。我认为您需要:
f = lambda x: ','.join(x.value_counts().astype(str))
d = {'Market':'count', 'ID':'Individual-Count'}
df1 = (df.groupby('Market')
.agg({'Fruit':','.join, 'Market':'size', 'ID':f})
.rename(columns=d)
.reset_index())
print (df1)
Market Fruit count Individual-Count
0 k Jack 1 1
1 r Apple,Guava 2 1,1
2 t Orange 1 1
编辑:
编辑:
@耶斯雷尔是的,这是正确的,不是身份证,而是水果的个体计数?你们明白了吗?agg上不需要ID,只需要将水果的单个计数作为逗号分隔的值。@panda-有3次groupby,因此,解决方案被简化了。是否可以将逗号分隔的值作为水果和单个计数的单独列,并以增量顺序使用一些列名?您能否就上述问题提供帮助?@panda-是否可以更改相关数据?
def f(x):
v = x['Fruit'].value_counts()
a = pd.Series(v.index)
b = pd.Series(v.values)
return pd.DataFrame({'Fruit':a, 'Individual-Count':b})
df1 = df.groupby('Market').apply(f).unstack()
df1.columns = [f'{a}{b+1}' for a, b in df1.columns]
df1['count'] = df1.index.map(df['Market'].value_counts().get)
df1 = df1.reset_index()
print (df1)
Market Fruit1 Fruit2 Individual-Count1 Individual-Count2 count
0 k Jack NaN 1.0 NaN 1
1 r Apple Guava 2.0 1.0 3
2 t Orange NaN 1.0 NaN 1
def f(x):
v = x['Fruit'].value_counts()
return pd.Series({'Fruit':', '.join(v.index),
'Individual-Count':','.join(v.astype(str).values)})
df1 = df.groupby('Market').apply(f)
df1['count'] = df1.index.map(df['Market'].value_counts().get)
df1 = df1.reset_index()
print (df1)
Market Fruit Individual-Count count
0 k Jack 1 1
1 r Apple, Guava 2,1 3
2 t Orange 1 1