问题
I am new to pandas and python.
I am trying to group items by one column and list the information from the data frame per group.
My dataframe:
B C D E F
1 Honda USA 2000 Washington New
2 Honda USA 2001 Salt Lake Used
3 Ford Canada 2005 Washington New
4 Toyota USA 2010 Ney York Used
5 Honda USA 2001 Salt Lake Used
6 Honda Canada 2011 Salt Lake Crashed
7 Ford Italy 2014 Rome New
I am trying to group my dataframe by column B and list how many C, D, E, F column values are in group B. For example we see that in column B there are 4 Honda which I am grouping it together. Then I want to list the following information - USA(3), Canada(1), 2000(1),2001(2), 2011(1), Washington(1), Salt Lake(3), New(1), Used(2), Crashed(1) and do the same per every group ( car make ) in column B:
Car Country Year City Condition
1 Honda(4) USA(3) 2000(1) Washington(1) New(1)
Canada(1) 2001(2) Salt Lake(3) Used(2)
2011(1) Crashed(1)
2 Ford(2) Canada(1) 2005(5) Washington(1) New(2)
Italy(1) 2014(1) Rome(1)
...
What I've tried so far:
df.groupby(['B'])
Which gives me back <pandas.core.groupby.generic.DataFrameGroupBy object at 0x11d559080>
At this point, I am not sure how I should code moving on forward getting the desired results after grouping the column B.
Thank you for your suggestions.
回答1:
You need lambda function with custom function for processing each column separately with Series.value_counts and then join values of index to values of counts of Series together:
def f(x):
x = x.value_counts()
y = x.index.astype(str) + '(' + x.astype(str) + ')'
return y.reset_index(drop=True)
df1 = df.groupby(['B']).apply(lambda x: x.apply(f)).reset_index(drop=True)
print (df1)
B C D E F
0 Ford(2) Italy(1) 2014(1) Washington(1) New(2)
1 NaN Canada(1) 2005(1) Rome(1) NaN
2 Honda(4) USA(3) 2001(2) Salt Lake(3) Used(2)
3 NaN Canada(1) 2011(1) Washington(1) Crashed(1)
4 NaN NaN 2000(1) NaN New(1)
5 Toyota(1) USA(1) 2010(1) Ney York(1) Used(1)
来源：https://stackoverflow.com/questions/59542245/listing-unique-value-counts-per-groups-in-pandas-dataframe