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']})
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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