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pythonpandas
Thornhale
Thornhale

Reputation: 2376

How do I split a string into several columns in a dataframe with pandas Python?

I am aware of the following questions:

1.) How to split a column based on several string indices using pandas? 2.) How do I split text in a column into multiple rows?

I want to split these into several new columns though. Suppose I have a dataframe that looks like this:

id    | string
-----------------------------
1     | astring, isa, string
2     | another, string, la
3     | 123, 232, another

I know that using:

df['string'].str.split(',')

I can split a string. But as a next step, I want to efficiently put the split string into new columns like so:

id    | string_1 | string_2 | string_3
-----------------|---------------------
1     | astring  | isa      | string
2     | another  | string   | la
3     | 123      | 232      | another
---------------------------------------

I could for example do this:

for index, row in df.iterrows():
    i = 0
    for item in row['string'].split():
        df.set_values(index, 'string_{0}'.format(i), item)
        i = i + 1

But how could one achieve the same result more elegantly?a

Upvotes: 16

Views: 19662

Answers (2)

juanpa.arrivillaga
juanpa.arrivillaga

Reputation: 96285

The str.split method has an expand argument:

>>> df['string'].str.split(',', expand=True)
         0        1         2
0  astring      isa    string
1  another   string        la
2      123      232   another
>>>

With column names:

>>> df['string'].str.split(',', expand=True).rename(columns = lambda x: "string"+str(x+1))
   string1  string2   string3
0  astring      isa    string
1  another   string        la
2      123      232   another

Much neater with Python >= 3.6 f-strings:

>>> (df['string'].str.split(',', expand=True)
...              .rename(columns=lambda x: f"string_{x+1}"))
  string_1 string_2  string_3
0  astring      isa    string
1  another   string        la
2      123      232   another

Upvotes: 20

ely
ely

Reputation: 77494

Slightly less concise than the expand option, but here is an alternative way:

In [29]: cols = ['string_1', 'string_2', 'string_3']   

In [30]: pandas.DataFrame(df.string.str.split(', ').tolist(), columns=cols)
Out[30]: 
  string_1 string_2 string_3
0  astring      isa   string
1  another   string       la
2      123      232  another

Upvotes: 1

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