Concatenate on specific condition python

Question

• EDITED

I want to write an If loop with conditions on cooncatenating strings. i.e. If cell A1 contains a specific format of text, then only do you concatenate, else leave as is.

example: If bill number looks like: CM2/0000/, then concatenate this string with the date column (month - year), else leave the bill number as it is.

Sample Data

Answer 1

You can create function which does what you need and use df.apply() to execute it on all rows.

I use example data from @Boomer answer.

EDIT: you didn't show what you really have in dataframe and it seems you have datetime in bill_date but I used strings. I had to convert strings to datetime to show how to work with this. And now it needs .strftime('%m-%y') or sometimes .dt.strftime('%m-%y') instead of .str[3:].str.replace('/','-'). Because pandas uses different formats to display dateitm for different countries so I couldn't use str(x) for this because it gives me 2019-09-15 00:00:00 instead of yours 15/09/19

import pandas as pd

df = pd.DataFrame({
    'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
    'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])

def convert(row):
    if row['bill_number'].endswith('/'):
        #return row['bill_number'] + row['bill_date'].str[3:].replace('/','-')
        return row['bill_number'] + row['bill_date'].strftime('%m-%y')
    else:
        return row['bill_number']

df['bill_number'] = df.apply(convert, axis=1)

print(df)

Result:

      bill_number bill_date
0  CM2/0000/09-19  15/09/19
1        CM2/0000  15/09/19
2  CM3/0000/09-19  15/09/19
3        CM3/0000  15/09/19

---

Second idea is to create mask

 mask = df['bill_number'].str.endswith('/')

and later use it for all values

 #df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].str[3:].str.replace('/','-')
 df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].dt.strftime('%m-%y')

or

 #df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].str[3:].str.replace('/','-')
 df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].dt.strftime('%m-%y')

Left side needs .loc[mask,'bill_number'] instead of `[mask]['bill_number'] to correctly assing values - but right side doesn't need it.

import pandas as pd

df = pd.DataFrame({
    'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
    'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])

mask = df['bill_number'].str.endswith('/')

#df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].str[3:].str.replace('/','-')
# or
#df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].str[3:].str.replace('/','-')

df.loc[mask,'bill_number'] = df[mask]['bill_number'] + df[mask]['bill_date'].dt.strftime('%m-%y')
#or
#df.loc[mask,'bill_number'] = df.loc[mask,'bill_number'] + df.loc[mask,'bill_date'].dt.strftime('%m-%y')

print(df)

---

Third idea is to use numpy.where()

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'bill_number': ['CM2/0000/', 'CM2/0000', 'CM3/0000/', 'CM3/0000'],
    'bill_date': ['15/09/19', '15/09/19', '15/09/19', '15/09/19']
})
df['bill_date'] = pd.to_datetime(df['bill_date'])

df['bill_number'] = np.where(
                       df['bill_number'].str.endswith('/'), 
                       #df['bill_number'] + df['bill_date'].str[3:].str.replace('/','-'), 
                       df['bill_number'] + df['bill_date'].dt.strftime('%m-%y'), 
                       df['bill_number'])

print(df)