Question

Hauptmenü (0, 'Information') (1, 'Beenden') (2, 'Hilfe') (3, 'Trennzeichen') (4, 'Lesen') (5, 'csv_suche') Option: bitte einen Menüpunkt eingeben: Information Version : 1.0 Datum der letzten Version : 04.01.2020 (0, 'Information') (1, 'Beenden') (2, 'Hilfe') (3, 'Trennzeichen') (4, 'Lesen') (5, 'csv_suche') Option: bitte einen Menüpunkt eingeben:

我的出场在下面

df_n_gender_grp = df_n_gender_prod_cat.groupby(['Gender','prod_cat'])

我的期望值是

第一个数据帧按值降序

    Gender  prod_cat
0   M   Books
1   M   Books
2   M   Electronics
3   M   Electronics
4   M   Books
100 F   Electronics
101 F   Electronics
102 F   Electronics
103 F   Electronics
104 F   Electronics
105 F   Clothing
106 F   Clothing
107 F   Clothing
108 F   Clothing

第二个数据帧，其值按降序排列

M Books       2
M Electronics 3

Answer 1

与.tif GroupBy.size和Series.sort_index一起用于> writeRaster(s, paste0(names(s),".tif"), bylayer=TRUE, format="GTiff")：

> dir()
[1] "layer.1.tif" "layer.2.tif" "layer.3.tif" "layer.tif"

然后按Series

进行过滤

MultiIndex

对于DataFrame是必需的，请添加Series.reset_index：

s = df_n_gender_prod_cat.groupby(['Gender','prod_cat']).size().sort_index(ascending=False)
print (s)
Gender  prod_cat   
M       Electronics    2
        Books          3
F       Electronics    5
        Clothing       4
dtype: int64

或先添加Series.reset_index：

loc

然后按boolean indexing进行过滤：

df1 = s.loc[['F']]
df2 = s.loc[['M']]

分组后如何分别获得计数

1 个答案: