Python - CSV 文件中的列包含多个分隔符和结果

Question

I have quite a large CSV file that has multiple columns (no delimiters) and one column which contains results that use three delimiters.

The main delimiter is ";", which separates days of results.

The second delimiter is ":", which separates results per day (I am only using 2 results out of a possible of 6).

The third delimiter is "/", which separates the result day and the calendar value of the result.

I want to avoid looping through the "X&Y" column as much as possible as the column itself contains many delimited results, and there are a lot of rows.

Col1	Col2	X&Y
A	B	20200331/1D::::1:2;20200401/2D::::3:4;20200402/3D::::5:6
AA	BB	20210330/1Y::::11:22;20220330/2Y::::33:44;20230330/3Y::::55:66

I want to see:

Col1	Col2	Date	CalendarValue	X	Y
A	B	20200331	1D	1	2
A	B	20200401	2D	3	4
A	B	2020040	3D	5	6
AA	BB	20210330	1Y	11	22
AA	BB	20220330	2Y	33	44
AA	BB	20220330	3Y	55	66

import pandas as pd
df = pd.DataFrame({'Col1':['A','AA'], 'Col2':['B', 'BB'], 'Col3':['20200331/1D::::1:2;20200401/2D::::3:4;20200402/3D::::5:6','20210330/1Y::::11:22;20220330/2Y::::33:44;20230330/3Y::::55:66']})

Answer 1

Here is a solution you can try out, split based on delimiter (;) followed by explode to transform into rows. Followed by extract & finally concat the frames to get resultant frame.

import pandas as pd
import re

df = pd.DataFrame({'Col1': ['A', 'AA'], 'Col2': ['B', 'BB'],
                   'Col3': ['20200331/1D::::1:2;20200401/2D::::3:4;20200402/3D::::5:6',
                            '20210330/1Y::::11:22;20220330/2Y::::33:44;20230330/3Y::::55:66']})

df['Col3'] = df['Col3'].str.split(";")

# extract features from the string
extract_ = re.compile(r"(?P<Date>\w+)/(?P<CalendarValue>\w+):+(?P<X>.+):(?P<Y>.+)")

pd.concat([
    df.drop(columns='Col3'),
    df['Col3'].explode().str.extract(extract_, expand=True)
], axis=1)

---

Out[*]:

  Col1 Col2      Date CalendarValue   X   Y
0    A    B  20200331            1D   1   2
0    A    B  20200401            2D   3   4
0    A    B  20200402            3D   5   6
1   AA   BB  20210330            1Y  11  22
1   AA   BB  20220330            2Y  33  44
1   AA   BB  20230330            3Y  55  66

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