大熊猫对数据透视表中的值进行排序

Question

我有一个数据框，我想将所有行分组在 id 中，其中在 country = russia 和 month = March 的行之后跟随着 country != russia 的行

输入数据框：

import pandas as pd
import numpy as np
data = {'fruit': ['pear','pear','pear','banana', 'banana', 'banana', 'apricot', 'pear','watermelon','pear','banana', 'banana', 'banana','banana', 'melon', 'cherry','banana', 'kiwi', 'kiwi', 'kiwi'],
'country': ['france','france', 'france', 'russia', 'russia', 'russia','russia', 'france','russia','usa', 'russia', 'ghana','ghana','ghana', 'ghana', 'albania','andorra', 'russia', 'russia', 'russia'],
'id': ['01','01','01','01','01','01','02','02','03','03','011', '011', '011','011', '6', '6','6', '5', '5', '5'],
'id1': ['01','01','01','01','01','01','02','02','03','03','011', '011', '011','011', '6', '6','6', '5', '5', '5'],
'month': ['january','september','january','january','september','january','january', 'september','march','march', 'november', 'march', 'january','january', 'march', 'january','july', 'march', 'march', 'april']        
}
df = pd.DataFrame(data, columns = ['fruit','country', 'id','id1', 'month'])

我用 pd.pivot_table(df, values='id', index=['fruit','country'], columns='id1', aggfunc='count') 制作了数据透视表，但有很多无用的行，其中有 NaN 或非常小的数字。

如何对数据透视表进行排序以获取数字不小于 3 的行？谁能看到问题

我需要获取这个数据框

data = {'fruit': ['banana', 'banana', 'kiwi','pear'],
'country': [ 'ghana','russia','russia','france'],
'01': [np.nan,3,np.nan,3],
'011': [3,1,np.nan,np.nan],
'5': [np.nan,np.nan,3,np.nan]
}
df = pd.DataFrame(data, columns = ['fruit','country', '01', '011','5'])

Answer 1

如果 df2 是您的数据透视表，您可以这样做：

row_mask = np.any((df2 >= 3).values, axis=1)
col_mask = np.any((df2 >= 3).values, axis=0)
df2.loc[row_mask, col_mask]

            id1  01     011       5
fruit   country             
banana  ghana   NaN     3.0     NaN
        russia  3.0     1.0     NaN
kiwi    russia  NaN     NaN     3.0
pear    france  3.0     NaN     NaN 

Answer 2

这是您想要的结果吗？它是“获得数字不少于 3 的行”，但与您的结果图片不同..

df = df.pivot_table(index=['fruit','country'], columns='id1', values='id', aggfunc='count')
df['total'] = df.sum(axis=1)
df.drop(df.loc[df['total']<3].index, inplace=True)
df.dropna(how='all', axis=1, inplace=True)

输出

         id1    01  011 02  5   total
fruit   country                 
banana  ghana   NaN 3.0 NaN NaN 3.0
banana  russia  3.0 1.0 NaN NaN 4.0
kiwi    russia  NaN NaN NaN 3.0 3.0
pear    france  3.0 NaN 1.0 NaN 4.0