[英]how to unstack (or pivot?) in pandas
I have a dataframe that looks like the following: 我有一个如下所示的数据框:
import pandas as pd
datelisttemp = pd.date_range('1/1/2014', periods=3, freq='D')
s = list(datelisttemp)*3
s.sort()
df = pd.DataFrame({'BORDER':['GERMANY','FRANCE','ITALY','GERMANY','FRANCE','ITALY','GERMANY','FRANCE','ITALY' ], 'HOUR1':[2 ,2 ,2 ,4 ,4 ,4 ,6 ,6, 6],'HOUR2':[3 ,3 ,3, 5 ,5 ,5, 7, 7, 7], 'HOUR3':[8 ,8 ,8, 12 ,12 ,12, 99, 99, 99]}, index=s)
This gives me: 这给了我:
Out[458]: df
BORDER HOUR1 HOUR2 HOUR3
2014-01-01 GERMANY 2 3 8
2014-01-01 FRANCE 2 3 8
2014-01-01 ITALY 2 3 8
2014-01-02 GERMANY 4 5 12
2014-01-02 FRANCE 4 5 12
2014-01-02 ITALY 4 5 12
2014-01-03 GERMANY 6 7 99
2014-01-03 FRANCE 6 7 99
2014-01-03 ITALY 6 7 99
I want the final dataframe to look something like: 我希望最终的数据框看起来像这样:
HOUR GERMANY FRANCE ITALY
2014-01-01 1 2 2 2
2014-01-01 2 3 3 3
2014-01-01 3 8 8 8
2014-01-02 1 4 4 4
2014-01-02 2 5 5 5
2014-01-02 3 12 12 12
2014-01-03 1 6 6 6
2014-01-03 2 7 7 7
2014-01-03 3 99 99 99
I've done the following but I'm not quite there: 我做了以下但是我不在那里:
df['date_col'] = df.index
df2 = melt(df, id_vars=['date_col','BORDER'])
#Can I keep the same index after melt or do I have to set an index like below?
df2.set_index(['date_col', 'variable'], inplace=True, drop=True)
df2 = df2.sort()
df DF
Out[465]: df2
BORDER value
date_col variable
2014-01-01 HOUR1 GERMANY 2
HOUR1 FRANCE 2
HOUR1 ITALY 2
HOUR2 GERMANY 3
HOUR2 FRANCE 3
HOUR2 ITALY 3
HOUR3 GERMANY 8
HOUR3 FRANCE 8
HOUR3 ITALY 8
2014-01-02 HOUR1 GERMANY 4
HOUR1 FRANCE 4
HOUR1 ITALY 4
HOUR2 GERMANY 5
HOUR2 FRANCE 5
HOUR2 ITALY 5
HOUR3 GERMANY 12
HOUR3 FRANCE 12
HOUR3 ITALY 12
2014-01-03 HOUR1 GERMANY 6
HOUR1 FRANCE 6
HOUR1 ITALY 6
HOUR2 GERMANY 7
HOUR2 FRANCE 7
HOUR2 ITALY 7
HOUR3 GERMANY 99
HOUR3 FRANCE 99
HOUR3 ITALY 99
I thought I could unstack df2 to get something that resembles my final dataframe but I get all sorts of errors. 我以为我可以拆开df2来获得类似于我最终数据帧的东西,但是我得到了各种各样的错误。 I have also tried to pivot this dataframe but can't quite get what I want.
我也尝试过调整这个数据框但是不能完全得到我想要的。
We want values (eg 'GERMANY'
) to become column names, and column names (eg 'HOUR1'
) to become values -- a swap of sorts. 我们希望值(例如
'GERMANY'
)成为列名,列名(例如'HOUR1'
)成为值 - 交换排序。
The stack
method turns column names into index values, and the unstack
method turns index values into column names. stack
方法将列名转换为索引值, unstack
方法将索引值转换为列名。
So by shifting the values into the index, we can use stack
and unstack
to perform the swap. 因此,通过将值移动到索引中,我们可以使用
stack
和unstack
来执行交换。
import pandas as pd
datelisttemp = pd.date_range('1/1/2014', periods=3, freq='D')
s = list(datelisttemp)*3
s.sort()
df = pd.DataFrame({'BORDER':['GERMANY','FRANCE','ITALY','GERMANY','FRANCE','ITALY','GERMANY','FRANCE','ITALY' ], 'HOUR1':[2 ,2 ,2 ,4 ,4 ,4 ,6 ,6, 6],'HOUR2':[3 ,3 ,3, 5 ,5 ,5, 7, 7, 7], 'HOUR3':[8 ,8 ,8, 12 ,12 ,12, 99, 99, 99]}, index=s)
df = df.set_index(['BORDER'], append=True)
df.columns.name = 'HOUR'
df = df.unstack('BORDER')
df = df.stack('HOUR')
df = df.reset_index('HOUR')
df['HOUR'] = df['HOUR'].str.replace('HOUR', '').astype('int')
print(df)
yields 产量
BORDER HOUR FRANCE GERMANY ITALY
2014-01-01 1 2 2 2
2014-01-01 2 3 3 3
2014-01-01 3 8 8 8
2014-01-02 1 4 4 4
2014-01-02 2 5 5 5
2014-01-02 3 12 12 12
2014-01-03 1 6 6 6
2014-01-03 2 7 7 7
2014-01-03 3 99 99 99
Using your df2
: 使用你的
df2
:
>>> df2.pivot_table(values='value', index=['DATE', 'variable'], columns="BORDER")
BORDER FRANCE GERMANY ITALY
DATE variable
2014-01-01 HOUR1 2 2 2
HOUR2 3 3 3
HOUR3 8 8 8
2014-01-02 HOUR1 4 4 4
HOUR2 5 5 5
HOUR3 12 12 12
2014-01-03 HOUR1 6 6 6
HOUR2 7 7 7
HOUR3 99 99 99
[9 rows x 3 columns]
There is still a bit of cleanup to do if you want to convert the index level "variable" into a column called "HOUR" and strip out the text "HOUR" from the values, but I think that is the basic format you want. 如果要将索引级别“变量”转换为名为“HOUR”的列并从值中删除文本“HOUR”,仍然需要进行一些清理,但我认为这是您想要的基本格式。
Try using pivot. 尝试使用pivot。 You can make it in one line.
你可以把它放在一行。 Eg.
例如。
df.pivot(index='start_time', columns='venue_name', values='ocupation')
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