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I have the following DataFrame in pandas:
A = [1,10,23,45,24,24,55,67,73,26,13,96,53,23,24,43,90],
B = [24,23,29, BW,49,59,72, BW,9,183,17, txt,2,49,BW,479,BW]
I want to create a new column and in that column I want to have values from column A based on the condition on column B. Conditions are if there is no ''txt'' in between two consecutive ''BW'', then I will have those on column C. But if there is ''txt'' between two consecutive ''BW'', I want to drop all those values. So the expected output should look like:
A = [1,10,23,45,24,24,55,67,73,26,13,96,53,23,24,43,90],
B = [24,23,29, BW,49,59,72, BW,9,183,17, txt,2,49,BW,479,BW]
C = [1,10,23, BW, 24,24,55, BW, nan, nan, nan, nan, nan, nan, BW, 43,BW]
I have no clue how to do it. Any help is much appreciated.
551
asked May 23 '26 05:05
EDIT:
Updated answer which was missing the values of BW in the final df.
import pandas as pd
import numpy as np
BW = 999
txt = -999
A = [1,10,23,45,24,24,55,67,73,26,13,96,53,23,24,43,90]
B = [24,23,29, BW,49,59,72, BW,9,183,17, txt,2,49,BW,479,BW]
df = pd.DataFrame({'A': A, 'B': B})
df = df.assign(group = (df[~df['B'].between(BW,BW)].index.to_series().diff() > 1).cumsum())
df['C'] = np.where(df.group == df[df.B == txt].group.values[0], np.nan, df.A)
df['C'] = np.where(df['B'] == BW, df['B'], df['C'])
df['C'] = df['C'].astype('Int64')
df = df.drop('group', axis=1)
In [435]: df
Out[435]:
A B C
0 1 24 1
1 10 23 10
2 23 29 23
3 45 999 999 <-- BW
4 24 49 24
5 24 59 24
6 55 72 55
7 67 999 999 <-- BW
8 73 9 <NA>
9 26 183 <NA>
10 13 17 <NA>
11 96 -999 <NA> <-- txt is in the middle of BW
12 53 2 <NA>
13 23 49 <NA>
14 24 999 999 <-- BW
15 43 479 43
16 90 999 999 <-- BW
You can achieve it like so, assuming BW and txt are specific values I just filled them with some random number to differentiate them
In [277]: BW = 999
In [278]: txt = -999
In [293]: A = [1,10,23,45,24,24,55,67,73,26,13,96,53,23,24,43,90]
...: B = [24,23,29, BW,49,59,72, BW,9,183,17, txt,49,BW,479,BW]
In [300]: df = pd.DataFrame({'A': A, 'B': B})
In [301]: df
Out[301]:
A B
0 1 24
1 10 23
2 23 29
3 45 999
4 24 49
5 24 59
6 55 72
7 67 999
8 73 9
9 26 183
10 13 17
11 96 -999
12 53 2
13 23 49
14 24 999
15 43 479
16 90 999
First lets split the different groups of values, here I am splitting them into unique groups where each group contains the values of B that are between the value BW and the next BW.
In [321]: df = df.assign(group = (df[~df['B'].between(BW,BW)].index.to_series().diff() > 1).cumsum())
In [322]: df
Out[322]:
A B group
0 1 24 0.00000000
1 10 23 0.00000000
2 23 29 0.00000000
3 45 999 NaN
4 24 49 1.00000000
5 24 59 1.00000000
6 55 72 1.00000000
7 67 999 NaN
8 73 9 2.00000000
9 26 183 2.00000000
10 13 17 2.00000000
11 96 -999 2.00000000
12 53 2 2.00000000
13 23 49 2.00000000
14 24 999 NaN
15 43 479 3.00000000
16 90 999 NaN
Next with the use of np.where() we can replace the values depending on the condition that you set.
In [360]: df['C'] = np.where(df.group == df[df.B == txt].group.values[0], np.nan, df.B)
In [432]: df
Out[432]:
A B group C
0 1 24 0.00000000 24.00000000
1 10 23 0.00000000 23.00000000
2 23 29 0.00000000 29.00000000
3 45 999 NaN 999.00000000
4 24 49 1.00000000 49.00000000
5 24 59 1.00000000 59.00000000
6 55 72 1.00000000 72.00000000
7 67 999 NaN 999.00000000
8 73 9 2.00000000 NaN
9 26 183 2.00000000 NaN
10 13 17 2.00000000 NaN
11 96 -999 2.00000000 NaN
12 53 2 2.00000000 NaN
13 23 49 2.00000000 NaN
14 24 999 NaN 999.00000000
15 43 479 3.00000000 479.00000000
16 90 999 NaN 999.00000000
Here we need to set the where B is equal to BW for C back to the values of B.
In [488]: df['C'] = np.where(df['B'] == BW, df['B'], df['C'])
In [489]: df
Out[489]:
A B group C
0 1 24 0.00000000 24.00000000
1 10 23 0.00000000 23.00000000
2 23 29 0.00000000 29.00000000
3 45 999 NaN 999.00000000
4 24 49 1.00000000 49.00000000
5 24 59 1.00000000 59.00000000
6 55 72 1.00000000 72.00000000
7 67 999 NaN 999.00000000
8 73 9 2.00000000 NaN
9 26 183 2.00000000 NaN
10 13 17 2.00000000 NaN
11 96 -999 2.00000000 NaN
12 53 2 2.00000000 NaN
13 23 49 2.00000000 NaN
14 24 999 NaN 999.00000000
15 43 479 3.00000000 479.00000000
16 90 999 NaN 999.00000000
Lastly just convert the float column to int and drop the group column which we do not need anymore. If you want to maintain that the NaN values are np.nan then ignore the conversion to Int64.
In [396]: df.C = df.C.astype('Int64')
In [397]: df
Out[397]:
A B group C
0 1 24 0.00000000 24
1 10 23 0.00000000 23
2 23 29 0.00000000 29
3 45 999 NaN 999
4 24 49 1.00000000 49
5 24 59 1.00000000 59
6 55 72 1.00000000 72
7 67 999 NaN 999
8 73 9 2.00000000 <NA>
9 26 183 2.00000000 <NA>
10 13 17 2.00000000 <NA>
11 96 -999 2.00000000 <NA>
12 53 2 2.00000000 <NA>
13 23 49 2.00000000 <NA>
14 24 999 NaN 999
15 43 479 3.00000000 479
16 90 999 NaN 999
In [398]: df = df.drop('group', axis=1)
In [435]: df
Out[435]:
A B C
0 1 24 24
1 10 23 23
2 23 29 29
3 45 999 999
4 24 49 49
5 24 59 59
6 55 72 72
7 67 999 999
8 73 9 <NA>
9 26 183 <NA>
10 13 17 <NA>
11 96 -999 <NA>
12 53 2 <NA>
13 23 49 <NA>
14 24 999 999
15 43 479 479
16 90 999 999
I don't know if this is the most efficient way to do it, but you can create a new column called mask from mapping the values in column B the following way: 'BW' to True, 'txt' to False and all other values to np.nan.
Then if you forward fill the NaN from mask, and backward fill the NaN from mask and logically combine the results (set equal to True as long as one of the forward or backward filled columns is False), you can create a column called final_mask where all of the values between consecutive BW containing txt are filled in with True.
You can then use .apply to select the value of column A only when the final_mask is False and column B isn't 'BW', select column B if final_mask is False and column B is 'BW', and np.nan otherwise.
import numpy as np
import pandas as pd
A = [1,10,23,45,24,24,55,67,73,26,13,96,53,23,24,43,90]
B = [24,23,29, 'BW',49,59,72, 'BW',9,183,17, 'txt',2,49,'BW',479,'BW']
df = pd.DataFrame({'A':A,'B':B})
df["mask"] = df["B"].apply(lambda x: True if x == 'BW' else False if x == 'txt' else np.nan)
df["ffill"] = df["mask"].fillna(method="ffill")
df["bfill"] = df["mask"].fillna(method="bfill")
df["final_mask"] = (df["ffill"] == False) | (df["bfill"] == False)
df["C"] = df.apply(lambda x: x['A'] if (
(x['final_mask'] == False) & (x['B'] != 'BW'))
else x['B'] if ((x['final_mask'] == False) & (x['B'] == 'BW'))
else np.nan, axis=1
)
>>> df
A B mask ffill bfill final_mask C
0 1 24 NaN NaN True False 1
1 10 23 NaN NaN True False 10
2 23 29 NaN NaN True False 23
3 45 BW True True True False BW
4 24 49 NaN True True False 24
5 24 59 NaN True True False 24
6 55 72 NaN True True False 55
7 67 BW True True True False BW
8 73 9 NaN True False True NaN
9 26 183 NaN True False True NaN
10 13 17 NaN True False True NaN
11 96 txt False False False True NaN
12 53 2 NaN False True True NaN
13 23 49 NaN False True True NaN
14 24 BW True True True False BW
15 43 479 NaN True True False 43
16 90 BW True True True False BW
Dropping the columns we created along the way:
df.drop(columns=['mask','ffill','bfill','final_mask'])
A B C
0 1 24 1
1 10 23 10
2 23 29 23
3 45 BW BW
4 24 49 24
5 24 59 24
6 55 72 55
7 67 BW BW
8 73 9 NaN
9 26 183 NaN
10 13 17 NaN
11 96 txt NaN
12 53 2 NaN
13 23 49 NaN
14 24 BW BW
15 43 479 43
16 90 BW BW
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