Add multiple columns and values to a pandas dataframe

I will ask my question with a small dataframe, but the real one is 1000s of lines.

ID  Name     #Required
123 New York  5
234 Boston    6
345 Miami     2
345 Dallas    7

I need the to the number of columns equals to the max in the '#Required' column and fill them in like this:

ID  Name     #Required J01     J02      J03     J04      J05     J06     J07      
123 New York  5        123W001 123W002  123W003 123W004 123W005  "blank"    "blank"
234 Boston    6        234W001 234W002  234W003 234W005 234W005  234W006 "blank"
345 Miami     2        345W001 345W002  "blank" "blank" "blank"  "blank"   "blank"
399 Dallas    7        399W001 399W002  399W003 399W004 399W005  399W006    399W007           

The number of values for each row depends on the number in the "#Required" column. with a "W" after "ID" and also I need to know how to handle the values if the number in "#Required" is greater than 10, because then the entry should be 123W010, only 1 leading 0.

ID  Name     #Required J01     J02      J03     J04      J05     J06     J07     
123 New York  5        123W001 123W002  123W003 123W004 123W005  "blank"  "blank"
234 Boston    6        234W001 234W002  234W003 234W005 234W005  234W006 "blank"
345 Miami     2        345W001 345W002  "blank" "blank" "blank"  "blank" "blank"
399 Dallas    7        399W001 399W002  399W003 399W004 399W005  399W006   399W007    

Interesting problem. Here is a pandas way of doing this. Explanation in the second section -

# Your dummy dataset
df = pd.DataFrame({'ID': [123, 234, 345, 399],
                   'Name': ['New York', 'Boston', 'Miami', 'Dallas'],
                   '#Required': [5, 6, 2, 7]})

# Creating list of the required values per row
new_lists = df.apply(lambda x: [str(x['ID'])+'W'+format(i, '03d') for i in range(1,x['#Required']+1)], 1)

# Converting the list to individual columns with nan values
new_cols = new_lists.apply(pd.Series)

# Creating column names
col_names = ['J'+format(i, '02d') for i in range(1,len(new_cols.columns)+1)]

# Adding new columns to original dataframe
df[col_names] = new_cols

# Replacing nan values via "blank"
df = df.fillna('"blank"')

df

Explanation

1. The format(x, '03d') gives you are 3 length string formed with a digit, so for 1 -> 001 and for 10 -> 010
2. You can form the required strings for each row using str(x['ID'])+'W'+format(i, '03d') where i is the range of digits from 1 to the value in the #Required column.
3. The [str(x['ID'])+'W'+format(i, '03d') for i in range(1,x['#Required']+1)] is a list comprehension that gives you the result as a list. So, if you print the new_lists, it will look like this -

print(new_lists)

0        [123W001, 123W002, 123W003, 123W004, 123W005]
1    [234W001, 234W002, 234W003, 234W004, 234W005, ...
2                                   [345W001, 345W002]
3    [399W001, 399W002, 399W003, 399W004, 399W005, ...
dtype: object

4. Next, with the .apply(pd.Series) you get the lists, expanded to individual columns, and the smaller length lists fill up the rest of the columns with Nan values. Printing the new_cols at this stage results in this -

print(new_cols)
         0        1        2        3        4        5        6
0  123W001  123W002  123W003  123W004  123W005      NaN      NaN
1  234W001  234W002  234W003  234W004  234W005  234W006      NaN
2  345W001  345W002      NaN      NaN      NaN      NaN      NaN
3  399W001  399W002  399W003  399W004  399W005  399W006  399W007

5. Now to create the column names, we again use just a simple list comprehension, with the range of the number of columns in the new_cols and leverage the previously used format(x, '02d') but this time for 2 digits.

print(col_names)

['J01', 'J02', 'J03', 'J04', 'J05', 'J06', 'J07']

6. Finally you add the new_cols as new columns to the original dataframe df by using df[col_names] = new_cols

7. And, last but not the least, you replace the nan values with "blank" as your question show, using a simple df.fillna('"blank"')

Bonus

Here is how the code works if you have double digit integers in #Required column, such as 10 or 12

# Sample dataframe with 12 and 10 values in #Required
df = df = pd.DataFrame({'ID': [123, 234, 345, 345],
                   'Name': ['New', 'Boston', 'Miami', 'Dallas'],
                   '#Required': [5, 10, 2, 12]})

new_lists = df.apply(lambda x: [str(x['ID'])+'W'+format(i, '03d') for i in range(1,x['#Required']+1)], 1)
new_cols = new_lists.apply(pd.Series)
col_names = ['J'+format(i, '02d') for i in range(1,len(new_cols.columns)+1)]
df[col_names] = new_cols
df = df.fillna('"blank"')
df

Notice that the column names went from J01 to J12 and the values for the new columns for the rows with #Required values like 10 or 12 look like 234W010 or 399W012.

EDIT

For 1-2 digit ids you can modify the code with the same format logic as before.

# Sample dataframe 2 digit IDs
df = df = pd.DataFrame({'ID': [123, 34, 345, 99],
                   'Name': ['New York', 'Boston', 'Miami', 'Dallas'],
                   '#Required': [5, 3, 2, 4]})

new_lists = df.apply(lambda x: [format(x['ID'], '03d')+'W'+format(i, '03d') for i in range(1,x['#Required']+1)], 1)
new_cols = new_lists.apply(pd.Series)
col_names = ['J'+format(i, '02d') for i in range(1,len(new_cols.columns)+1)]
df[col_names] = new_cols
df = df.fillna('"blank"')
df

Proposed code

import pandas as pd

# Your dummy dataset
df = pd.DataFrame({'ID': [123, 234, 345, 399],
                   'Name': ['New York', 'Boston', 'Miami', 'Dallas'],
                   '#Required': [5, 6, 2, 7]})

m = max(df['#Required']) # Maximum number in '#Required'
lm = len(str(m)) # length of the maximum number in '#Required'

how_to_fill = {'J%s'%str(n).zfill(1+lm):['%sW%s'%(i,str(n).zfill(2+lm)) if n <= r else 'Blank' for i,r in zip(df['ID'], df['#Required'])] for n in range(1, m+1)}

df = df.join(pd.DataFrame(how_to_fill)).set_index('ID')

print(df)

Result :

         Name  Required      J01      J02  ...      J04      J05      J06      J07
ID                                          ...                                    
123  New York          5  123W001  123W002  ...  123W004  123W005    Blank    Blank
234    Boston          6  234W001  234W002  ...  234W004  234W005  234W006    Blank
345     Miami          2  345W001  345W002  ...    Blank    Blank    Blank    Blank
399    Dallas          7  399W001  399W002  ...  399W004  399W005  399W006  399W007

Note :

I suggest you the use of np.nan with import numpy as np instead of "Blank"