Pandas - split column with dtype object (string) to dtype list by specifying delimiter

Question

Given data in a csv like below

A,B,C-1,D,BTP,Type C1,Type C2
0,1,0,0,0,,Type B
0,2,1,1,14,Type B,Type B
0,3,2,2,28,Type A,Type B
0,4,3,3,42,"Type A,Type B","Type A,Type B"
0,5,4,4,56,Type A,"Type A,Type B"

I'm reading this into a dataframe df. Need to split Type C1' column by ,and store as a list in place such that I can do some lookup with%in% of operator. Here is what is being done.

df["Type C1"] = df["Type C1"].str.split(",", n = 1, expand = True)

Was expecting to get a list for column Type C1 - however it was still a string with the part from , stripped out as below.

      A  B    C-1          D    BTP                        Type C1                Type C2
0     0  1    0            0     0                            NaN                         Type B
1     0  2    1            1    14                         Type B                         Type B
2     0  3    2            2    28                         Type A                         Type B
3     0  4    3            3    42                         Type A                  Type A,Type B
4     0  5    4            4    56                         Type A                  Type A,Type B

For row #3 was expecting [Type A,Type B] for column Type C1

The reference I'm using to do this is from Pandas Split strings into two List/Columns using str.split() Example #1 output.

Don't use expand=True.

Shubham Sharma
– Shubham Sharma

2020-06-27 05:03:00 +00:00
Commented Jun 27, 2020 at 5:03 — Shubham Sharma
– Shubham Sharma, Commented Jun 27, 2020 at 5:03

YOLO · Accepted Answer · 2020-06-27 05:04:22Z

0

You are almost correct, split() returns a list by default:

df['Type C1'] = df['Type C1'].str.split(',')
df['Type C2'] = df['Type C2'].str.split(',')

   A  B  C-1  D  BTP           Type C1           Type C2
0  0  1    0  0    0               NaN          [Type B]
1  0  2    1  1   14          [Type B]          [Type B]
2  0  3    2  2   28          [Type A]          [Type B]
3  0  4    3  3   42  [Type A, Type B]  [Type A, Type B]
4  0  5    4  4   56          [Type A]  [Type A, Type B]

answered Jun 27, 2020 at 5:04

YOLO

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