python - Split cell into multiple rows in pandas dataframe

Question

I have a dataframe contains orders data, each order has multiple packages stored as comma separated string [package & package_code] columns

I want to split the packages data and create a row for each package including its order details

Here is a sample input dataframe:

import pandas as pd
df = pd.DataFrame({"order_id":[1,3,7],"order_date":["20/5/2018","22/5/2018","23/5/2018"], "package":["p1,p2,p3","p4","p5,p6"],"package_code":["#111,#222,#333","#444","#555,#666"]})

And this is what I am trying to achieve as output:

How can I do that with pandas?

question from:https://stackoverflow.com/questions/50731229/split-cell-into-multiple-rows-in-pandas-dataframe

Answer 1

pandas >= 0.25

Assuming all splittable columns have the same number of comma separated items, you can split on comma and then use Series.explode on each column:

(df.set_index(['order_id', 'order_date'])
   .apply(lambda x: x.str.split(',').explode())
   .reset_index())                                                   

   order_id order_date package package_code
0         1  20/5/2018      p1         #111
1         1  20/5/2018      p2         #222
2         1  20/5/2018      p3         #333
3         3  22/5/2018      p4         #444
4         7  23/5/2018      p5         #555
5         7  23/5/2018      p6         #666

Details

Set the columns not to be touched as the index,

df.set_index(['order_id', 'order_date'])

                      package    package_code
order_id order_date                          
1        20/5/2018   p1,p2,p3  #111,#222,#333
3        22/5/2018         p4            #444
7        23/5/2018      p5,p6       #555,#666

The next step is a 2-step process: Split on comma to get a column of lists, then call explode to explode the list values into their own rows.

_.apply(lambda x: x.str.split(',').explode())

                    package package_code
order_id order_date                     
1        20/5/2018       p1         #111
         20/5/2018       p2         #222
         20/5/2018       p3         #333
3        22/5/2018       p4         #444
7        23/5/2018       p5         #555
         23/5/2018       p6         #666

Finally, reset the index.

_.reset_index()

   order_id order_date package package_code
0         1  20/5/2018      p1         #111
1         1  20/5/2018      p2         #222
2         1  20/5/2018      p3         #333
3         3  22/5/2018      p4         #444
4         7  23/5/2018      p5         #555
5         7  23/5/2018      p6         #666

---

pandas <= 0.24

This should work for any number of columns like this. The essence is a little stack-unstacking magic with str.split.

(df.set_index(['order_date', 'order_id'])
   .stack()
   .str.split(',', expand=True)
   .stack()
   .unstack(-2)
   .reset_index(-1, drop=True)
   .reset_index()
)

  order_date  order_id package package_code
0  20/5/2018         1      p1         #111
1  20/5/2018         1      p2         #222
2  20/5/2018         1      p3         #333
3  22/5/2018         3      p4         #444
4  23/5/2018         7      p5         #555
5  23/5/2018         7      p6         #666

There is another performant alternative involving chain, but you'd need to explicitly chain and repeat every column (a bit of a problem with a lot of columns). Choose whatever fits the description of your problem best, as there's no single answer.

Details

First, set the columns that are not to be touched as the index.

df.set_index(['order_date', 'order_id'])
 
                      package    package_code
order_date order_id                          
20/5/2018  1         p1,p2,p3  #111,#222,#333
22/5/2018  3               p4            #444
23/5/2018  7            p5,p6       #555,#666

Next, stack the rows.

_.stack()

order_date  order_id              
20/5/2018   1         package               p1,p2,p3
                      package_code    #111,#222,#333
22/5/2018   3         package                     p4
                      package_code              #444
23/5/2018   7         package                  p5,p6
                      package_code         #555,#666
dtype: object

We have a series now. So call str.split on comma.

_.str.split(',', expand=True)

                                     0     1     2
order_date order_id                               
20/5/2018  1        package         p1    p2    p3
                    package_code  #111  #222  #333
22/5/2018  3        package         p4  None  None
                    package_code  #444  None  None
23/5/2018  7        package         p5    p6  None
                    package_code  #555  #666  None

We need to get rid of NULL values, so call stack again.

_.stack()

order_date  order_id                 
20/5/2018   1         package       0      p1
                                    1      p2
                                    2      p3
                      package_code  0    #111
                                    1    #222
                                    2    #333
22/5/2018   3         package       0      p4
                      package_code  0    #444
23/5/2018   7         package       0      p5
                                    1      p6
                      package_code  0    #555
                                    1    #666
dtype: object

We're almost there. Now we want the second last level of the index to become our columns, so unstack using unstack(-2) (unstack on the second last level)

_.unstack(-2)

                      package package_code
order_date order_id                       
20/5/2018  1        0      p1         #111
                    1      p2         #222
                    2      p3         #333
22/5/2018  3        0      p4         #444
23/5/2018  7        0      p5         #555
                    1      p6         #666

Get rid of the superfluous last level using reset_index:

_.reset_index(-1, drop=True)

                    package package_code
order_date order_id                     
20/5/2018  1             p1         #111
           1             p2         #222
           1             p3         #333
22/5/2018  3             p4         #444
23/5/2018  7             p5         #555
           7             p6         #666

And finally,

_.reset_index()

  order_date  order_id package package_code
0  20/5/2018         1      p1         #111
1  20/5/2018         1      p2         #222
2  20/5/2018         1      p3         #333
3  22/5/2018         3      p4         #444
4  23/5/2018         7      p5         #555
5  23/5/2018         7      p6         #666