CODEWITHSUNDEEP
pythonpandas
Kusisi Karem
Kusisi Karem

Reputation: 107

Extracting values into a new column

I have a column in a dataframe as follows:

Data 
[special_request=nowhiterice, waiter=Janice]
[allegic=no, waiter=Janice, tip=20]
[allergic=no, tip=20]
[special_request=nogreens]

May I know how could I make it such that one data = 1 column ?

special_request   allegic   waiter   tip

Upvotes: 1

Views: 149

Answers (2)

Tranbi
Tranbi

Reputation: 12701

You can make a Dictionary by splitting the elements of your series and build your Dataframe from it (s being your column here):

import pandas as pd

s = pd.Series([['special_request=nowhiterice', 'waiter=Janice'],
['allegic=no', 'waiter=Janice', 'tip=20'],
['allergic=no', 'tip=20'],
['special_request=nogreens']])

df = pd.DataFrame([dict(e.split('=') for e in row) for row in s])
print(df)

Output:

  special_request  waiter allegic  tip allergic
0     nowhiterice  Janice     NaN  NaN      NaN
1             NaN  Janice      no   20      NaN
2             NaN     NaN     NaN   20       no
3        nogreens     NaN     NaN  NaN      NaN

Edit: if the column values are actual strings, you first should split your string (also stripping [, ]and whitespaces):

s = pd.Series(['[special_request=nowhiterice, waiter=Janice]',
'[allegic=no, waiter=Janice, tip=20]',
'[allergic=no, tip=20]',
'[special_request=nogreens]'])

df = pd.DataFrame([dict(map(str.strip, e.split('=')) for e in row.strip('[]').split(',')) for row in s])
print(df)

Upvotes: 1

Ynjxsjmh
Ynjxsjmh

Reputation: 29982

You can split the column value of string type into dict then use pd.json_normalize to convert dict to columns.

df_ = pd.json_normalize(df['Data'].apply(lambda x: dict([map(str.strip, i.split('=')) for i in x.strip("[]").split(',')])))

print(df_)

  special_request  waiter allegic  tip allergic
0     nowhiterice  Janice     NaN  NaN      NaN
1             NaN  Janice      no   20      NaN
2             NaN     NaN     NaN   20       no
3        nogreens     NaN     NaN  NaN      NaN

Upvotes: 0

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