Question

I have a data frame like

import pandas as pd
data = { Type : [ Fruits ,  Fruits ,  Fruits ,  Fruits ],
         Name : [ Mango ,  Mango ,  Mango ,  Mango ],
         Variety : [ Alphonso ,  Dasheri ,  Langra ,  Raspuri ],
         April :[120,110,90,60],
         May :[110,80,50,40],
         June :[80,110,76,65],
         July :[85,87,55,50]}
df = pd.DataFrame(data)
df=df[[ Type , Name , Variety , April , May , June , July ]]

     Type   Name   Variety  April  May  June  July
0  Fruits  Mango  Alphonso    120  110    80    85
1  Fruits  Mango   Dasheri    110   80   110    87
2  Fruits  Mango    Langra     90   50    76    55
3  Fruits  Mango   Raspuri     60   40    65    50

当我超负荷工作时,我正在像现在这样做。

ndf=df.melt(id_vars=[ Type , Name , Variety ],var_name="Month",value_name="Price")

     Type   Name   Variety  Month  Price
0   Fruits  Mango  Alphonso  April    120
1   Fruits  Mango   Dasheri  April    110
2   Fruits  Mango    Langra  April     90
3   Fruits  Mango   Raspuri  April     60
...........
11  Fruits  Mango   Raspuri   June     65
12  Fruits  Mango  Alphonso   July     85
13  Fruits  Mango   Dasheri   July     87
14  Fruits  Mango    Langra   July     55
15  Fruits  Mango   Raspuri   July     50

但实际上,我需要根据“惯例”而不是“月”订购的数据框架。预期的数据范围类似

     Type   Name   Variety  Month  Price
0   Fruits  Mango  Alphonso  April    120
1   Fruits  Mango  Alphonso    May    110
2   Fruits  Mango  Alphonso   June     80
3   Fruits  Mango  Alphonso   July     85
4   Fruits  Mango   Dasheri  April    110
5   Fruits  Mango   Dasheri    May     80
.................................
13  Fruits  Mango   Raspuri    May     40
14  Fruits  Mango   Raspuri   June     65
15  Fruits  Mango   Raspuri   July     50

有什么解决办法?

Answer 1

仅需要将数据框中的数值按该栏,例如:

ndf = ndf.sort_values(by=[ Variety ], ascending = False)

Answer 2

在您使用pd.melt之后,你可以根据Valiety栏对数据进行分类。

ndf.sort_values(by=[ Variety ]).reset_index(drop=True, inplace=True)
ndf

Here s the documentation for the sort_values method https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html

结果:

	Type	Name	Variety	Month	Price
0	Fruits	Mango	Alphonso	April	120
1	Fruits	Mango	Alphonso	May	110
2	Fruits	Mango	Alphonso	June	80
3	Fruits	Mango	Alphonso	July	85
4	Fruits	Mango	Dasheri	April	110
5	Fruits	Mango	Dasheri	May	80
6	Fruits	Mango	Dasheri	June	110
7	Fruits	Mango	Dasheri	July	87
8	Fruits	Mango	Langra	April	90
9	Fruits	Mango	Langra	May	50
10	Fruits	Mango	Langra	June	76
11	Fruits	Mango	Langra	July	55
12	Fruits	Mango	Raspuri	April	60
13	Fruits	Mango	Raspuri	May	40
14	Fruits	Mango	Raspuri	June	65
15	Fruits	Mango	Raspuri	July	50

Edit 1: As per mozway s suggestion you don t need to reset the index after, but can instead pass ignore_index=True as a parameter to sort_values()

ndf.sort_values(by=[ Variety ], ignore_index=True, inplace=True)
ndf

Answer 3

在<代码>melt后不打上列栏,你还可以改变重塑方法。

If you stack this will directly preserve the original order of rows/columns:

out = (df.rename_axis(columns= Month )
         .set_index([ Type , Name , Variety ])
         .stack().reset_index(name= Price )
      )

产出:

      Type   Name   Variety  Month  Price
0   Fruits  Mango  Alphonso  April    120
1   Fruits  Mango  Alphonso    May    110
2   Fruits  Mango  Alphonso   June     80
3   Fruits  Mango  Alphonso   July     85
4   Fruits  Mango   Dasheri  April    110
5   Fruits  Mango   Dasheri    May     80
6   Fruits  Mango   Dasheri   June    110
7   Fruits  Mango   Dasheri   July     87
8   Fruits  Mango    Langra  April     90
9   Fruits  Mango    Langra    May     50
10  Fruits  Mango    Langra   June     76
11  Fruits  Mango    Langra   July     55
12  Fruits  Mango   Raspuri  April     60
13  Fruits  Mango   Raspuri    May     40
14  Fruits  Mango   Raspuri   June     65
15  Fruits  Mango   Raspuri   July     50

Answer 4

一种选择是pivot_longer ,其中通过True<> > > > 编码至/code>,第1段,并确保代码>成为第1栏:


# pip install pyjanitor
import pandas as pd

(df
.pivot_longer(
    index=[ Variety , Type , Name ],
    names_to= Month ,
    values_to= Price ,
    sort_by_appearance=True)
)

     Variety    Type   Name  Month  Price
0   Alphonso  Fruits  Mango  April    120
1   Alphonso  Fruits  Mango    May    110
2   Alphonso  Fruits  Mango   June     80
3   Alphonso  Fruits  Mango   July     85
4    Dasheri  Fruits  Mango  April    110
5    Dasheri  Fruits  Mango    May     80
6    Dasheri  Fruits  Mango   June    110
7    Dasheri  Fruits  Mango   July     87
8     Langra  Fruits  Mango  April     90
9     Langra  Fruits  Mango    May     50
10    Langra  Fruits  Mango   June     76
11    Langra  Fruits  Mango   July     55
12   Raspuri  Fruits  Mango  April     60
13   Raspuri  Fruits  Mango    May     40
14   Raspuri  Fruits  Mango   June     65
15   Raspuri  Fruits  Mango   July     50

友情链接