将pandas df列数据转置为行

Question

I have a df as the following

email     | date | type
_________________________
xy@xy.com | 6/1  | order
xy@xy.com | 6/1  | return
cd@xy.com | 6/2  | return
ab@xy.com | 6/2  | return

I'm trying to individualize the type of column into each row keeping the data

email     | date | order | return
_________________________________
xy@xy.com | 6/1  | 1     |   0
xy@xy.com | 6/1  | 0     |   1
cd@xy.com | 6/2  | 0     |   0
ab@xy.com | 6/2  | 0     |   0

I've been trying to use pd.melt but the output doesn't seem to be what i'm looking for. referenced from Pandas dataframe transpose with original row and column values

Answer 1

You should have a look at how to create dummy variables from categorical columns.

There is a nice Pandas function to achieve that named "get_dummies":

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html

Demonstration

df.drop('type', 1).join(pd.get_dummies(df['type']))

       email date  order  return
0  xy@xy.com  6/1      1       0
1  xy@xy.com  6/1      0       1
2  cd@xy.com  6/2      0       1
3  ab@xy.com  6/2      0       1