CODEWITHSUNDEEP
pythonpandas
Tushar Agrawal
Tushar Agrawal

Reputation: 93

Split column into unknown number of columns according to number of words- Pandas

I have a pandas data frame with one of its column containing some string. I want to split that column into an unknown number of columns according to word count.

Suppose, I have DataFrame df :

Index        Text
0          He codes
1          He codes well in python
2          Python is great language
3          Pandas package is very handy

Now I want to divide the text column into multiple columns, each containing 2 words each.

Index         0                 1                 2
0          He codes          NaN               NaN
1          He codes          well in           python
2          Python is         great language    NaN
3          Pandas package    is very           handy

How can I do this in python? Please help. Thanks in advance.

Upvotes: 4

Views: 3681

Answers (2)

Umar.H
Umar.H

Reputation: 23099

IIUC, we can str.split groupby cumcount with floor division and unstack

s = (
    df["Text"]
    .str.split("\s", expand=True)
    .stack()
    .to_frame("words")
    .reset_index(1, drop=True)
)
s["count"] = s.groupby(level=0).cumcount() // 2
final = s.rename_axis("idx").groupby(["idx", "count"])["words"].agg(" ".join).unstack(1)

print(final)

count               0               1       2
idx                                          
0            He codes             NaN     NaN
1            He codes         well in  python
2           Python is  great language     NaN
3      Pandas package         is very   handy

Upvotes: 2

mabergerx
mabergerx

Reputation: 1213

Given a dataframe df where in the Text column we have sentences that need to be split by two words:

import pandas as pd

def splitter(s):
    spl = s.split()
    return [" ".join(spl[i:i+2]) for i in range(0, len(spl), 2)]

df_new = pd.DataFrame(df["Text"].apply(splitter).to_list())

#           0        1       2
# 0  He codes     well    None
# 1  He codes  well in  Python

Upvotes: 7

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