split a series into multiple pandas columns-CodePudding

I have a pandas dataframe, one of the columns has a series in there. The structure is as follows:

Date          Col1
2022-01-02    'Amt_Mean                 2022.0\nAmt_Med                   5.0\nAmt_Std     877.0\ndtype: float64'


2022-01-03    'Amt_Mean                 2025.0\nAmt_Med                   75.0\nAmt_Std     27.0\ndtype: float64'

I want to reshape this such that I get the following output

Date        Amt_Mean   Amt_Med   Amt_Std
2022-01-02  2022.0     5.0       877.0
2022-01-03  2025.0     75.0      27.0

How can I achieve this? I tried df['Col1'][0][1] which gives me the first amount and I can potentially for loop it, but seems there should be a more easy (pythonic) way of doing this. Thanks!

CodePudding user response：

Some string processing to convert each string into a dict, convert those to a dataframe, and concatenate that with the original:

new_df = pd.DataFrame([dict(re.split(r'\s ', y) for y in x.split('\n')[:-1]) for x in df['Col1']])
df = pd.concat([df.drop('Col1', axis=1), new_df], axis=1)

Output:

>>> df
         Date Amt_Mean Amt_Med Amt_Std
0  2022-01-02   2022.0     5.0   877.0
1  2022-01-03   2025.0    75.0    27.0

CodePudding user response：

Assuming you have pd.Series objects, I would concatenate them using a chained pd.concat:

>>> pd.concat([df.Date, pd.concat([x for x in df.Col1], axis=1).T], axis=1)

         Date  Amt_Mean  Amt_Med  Amt_Std
0  2022-01-02      2022        5      877
1  2022-01-03      2025       75       27

This assumes your data is as follows:

df = pd.DataFrame([{'Date':'2022-01-02', 
                    'Col1': pd.Series({'Amt_Mean': 2022, "Amt_Med": 5, "Amt_Std":877})}, 
                   {'Date':'2022-01-03', 
                    'Col1': pd.Series({'Amt_Mean': 2025, "Amt_Med": 75, "Amt_Std":27})
                   }])