CODEWITHSUNDEEP
pythonpandassplitdataset
Sam B
Sam B

Reputation: 47

How can we split the contents of a whole column into separate columns within the same table?

Say I use pandas to read in datatable.csv, and the table looks like this:

Table 1

How could we split Column 3 such that datatable subsequently looks like this:

Table 2

I've read plenty of answers to similar questions and tried using the likes of .str.rpartition('-'), .split('-') and .apply() combinations, but I haven't managed to extract the separated values into columns.

Upvotes: 2

Views: 115

Answers (2)

S_Ymln
S_Ymln

Reputation: 421

You can use this

df['col3']= ['1-d-w','s-3-q']

df
Out[4]: 
     col
0  1-d-w
1  s-3-q
df['col4']= df['col3'].apply((lambda x: x.split('-'))).apply((lambda x:x[0]))
df['col5']= df['col3'].apply((lambda x: x.split('-'))).apply((lambda x:x[1]))
df['col6']= df['col3'].apply((lambda x: x.split('-'))).apply((lambda x:x[2]))
df
Out[33]: 
    col3  col4  col5  col6
0  1-d-w     1     d     w
1  s-3-q     s     3     q

Upvotes: 0

Scott Boston
Scott Boston

Reputation: 153500

Use split with expand=True:

df[['Column 3','Column 4','Column 5']] = df['Column 3'].str.split('-', expand=True)

Example 

df = pd.DataFrame({'Column 3':['X-X-0','1-0-X','X-Y-X','X-0-0','1-X-Y']})
df[['Column 3','Column 4','Column 5']] = df['Column 3'].str.split('-', expand=True)

Output:

  Column 3 Column 4 Column 5
0        X        X        0
1        1        0        X
2        X        Y        X
3        X        0        0
4        1        X        Y

Upvotes: 2

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