Pandas transform rows with specific character - pandas

Question

i am working on features transformation, and ran into this issue. Let me know what you think. Thanks!
I have a table like this
And I want to create an output column like this
Some info:
All the outputs will be based on numbers that end with a ':'
I have 100M+ rows in this table. Need to consider performance issue.
Let me know if you have some good ideas. Thanks!
Here is some copy and paste-able sample data:
df = pd.DataFrame({'Number': {0: '1000',1: '1000021', 2: '15:00', 3: '23424234',
4: '23423', 5: '3', 6 : '9:00', 7: '3423', 8: '32', 9: '7:00'}})

Answer 1

Solution #1:
You can use .str.contains(':') with np.where() to identify the values, otherwise return np.nan. Then, use ffill() to fill down on nan values:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Number': {0: '1000',1: '1000021', 2: '15:00', 3: '23424234',
4: '23423', 5: '3', 6 : '9:00', 7: '3423', 8: '32', 9: '7:00'}})
df['Output'] = np.where(df['Number'].str.contains(':'),df['Number'].str.split(':').str[0],np.nan)
df['Output'] = df['Output'].ffill()
df
Solution #2 - Even easier and potentially better performance you can do some regex with str.extract() and then again ffill():
df['Output'] = df['Number'].str.extract('^(\d+):').ffill()
df
Out[1]:
Number Output
0 1000 NaN
1 1000021 NaN
2 15:00 15
3 23424234 15
4 23423 15
5 3 15
6 9:00 9
7 3423 9
8 32 9
9 7:00 7

Answer 2

I think this is what you are looking for:
import pandas as pd
c = ['Number']
d = ['1:00',100,1001,1321,3254,'15:00',20,60,80,90,'4:00',26,45,90,89]
df = pd.DataFrame(data=d,columns=c)
temp= df['Number'].str.split(":", n = 1, expand = True)
df['New_Val'] = temp[0].ffill()
print(df)
The output of this will be as follows:
Number New_Val
0 1:00 1
1 100 1
2 1001 1
3 1321 1
4 3254 1
5 15:00 15
6 20 15
7 60 15
8 80 15
9 90 15
10 4:00 4
11 26 4
12 45 4
13 90 4
14 89 4
Looks like your DataFrame has string values. I considered them as a mix of numbers and strings.
Here's the solution if df['Number'] is all strings.
df1 = pd.DataFrame({'Number': {0: '1000',1: '1000021', 2: '15:00', 3: '23424234',
4: '23423', 5: '3', 6 : '9:00', 7: '3423', 8: '32', 9: '7:00'}})
temp= df1['Number'].str.split(":", n = 1, expand = True)
temp.loc[temp[1].astype(bool) != False, 'New_val'] = temp[0]
df1['New_val'] = temp['New_val'].ffill()
print (df1)
The output of df1 will be:
Number New_val
0 1000 NaN
1 1000021 NaN
2 15:00 15
3 23424234 15
4 23423 15
5 3 15
6 9:00 9
7 3423 9
8 32 9
9 7:00 7