I'm trying to pivot the values in a column to column headers, but maintain the rest of the data. Here's my full code, along with the closest I can get to what I'm looking for. The only problem with this is that I can't figure out how to keep the end
column:
import pandas as pd
starts = pd.date_range(start = '1/1/2017', freq = '31d', periods = 4).tolist()
ends = pd.date_range(start = '1/31/2017', freq = '31d', periods = 4).tolist()
df = pd.DataFrame({ 'id':['XXX','XXX','XXX','XXX','YYY','YYY','YYY','YYY'],
'start': starts + starts,
'end': ends + ends,
'type':['car','car','car','car','truck','truck','truck','truck']
}, columns = ['id','start','end','type'])
Original Dataframe:
id start end type
0 XXX 2017-01-01 2017-01-31 car
1 XXX 2017-02-01 2017-03-03 car
2 XXX 2017-03-04 2017-04-03 car
3 XXX 2017-04-04 2017-05-04 car
4 YYY 2017-01-01 2017-01-31 truck
5 YYY 2017-02-01 2017-03-03 truck
6 YYY 2017-03-04 2017-04-03 truck
7 YYY 2017-04-04 2017-05-04 truck
My closest current pivot attempt:
print df.pivot(index = 'start', columns = 'id', values = 'type').reset_index()
Current output:
id start XXX YYY
0 2017-01-01 car truck
1 2017-02-01 car truck
2 2017-03-04 car truck
3 2017-04-04 car truck
Desired output:
start end XXX YYY
0 2017-01-01 2017-01-31 car truck
1 2017-02-01 2017-03-03 car truck
2 2017-03-04 2017-04-03 car truck
3 2017-04-04 2017-05-04 car truck
I've tried both this and this, with no success.
Any help would be appreciated.
pd.pivot_table(df,index=['start','end'],columns='id',values='type',aggfunc='sum').reset_index()
Out[1587]:
id start end XXX YYY
0 2017-01-01 2017-01-31 car truck
1 2017-02-01 2017-03-03 car truck
2 2017-03-04 2017-04-03 car truck
3 2017-04-04 2017-05-04 car truck
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With