Combine two columns of numbers in dataframe into single column using pandas/python - pandas

Question

I'm very new to Pandas and Python.
I have a 3226 x 61 dataframe and I would like to combine two columns into a single one.
The two columns I would like to combine are both integers - one has either one or two digits (1 through 52) while the other has three digits (e.g., 1 or 001, 23 or 023). I need the output to be a five digit integer (e.g., 01001 or 52023). There will be no mathematical operations with the resulting integers - I will need them only for look-up purposes.
Based on some of the other posts on this fantastic site, I tried the following:
df['YZ'] = df['Y'].map(str) + df['Z'].map(str)
But that returns "1.00001 for a first column of "1" and second column of "001", I believe because making "1" a str turns it into "1.0", which "001" is added to the end.
I've also tried:
df['YZ'] = df['Y'].join(df['Z'])
Getting the following error:
AttributeError: 'Series' object has no attribute 'join'
I've also tried:
df['Y'] = df['Y'].astype(int)
df['Z'] = df['Z'].astype(int)
df['YZ'] = df[['Y','Z']].apply(lambda x: ''.join(x), axis=1)
Getting the following error:
TypeError: ('sequence item 0: expected str instance, numpy.int32
found', 'occurred at index 0')
A copy of the columns is below:
1 1
1 3
1 5
1 7
1 9
1 11
1 13
I understand there are two issues here:
Combining the two columns
Getting the correct format (five digits)
Frankly, I need help with both but would be most appreciative of the column combining problem.

Answer 1

I think you need convert columns to string, add 0 by zfill and simply sum by +:
df['YZ'] = df['Y'].astype(str).str.zfill(2) + df['Z'].astype(str).str.zfill(3)
Sample:
df=pd.DataFrame({'Y':[1,3,5,7], 'Z':[10,30,51,74]})
print (df)
Y Z
0 1 10
1 3 30
2 5 51
3 7 74
df['YZ'] = df['Y'].astype(str).str.zfill(2) + df['Z'].astype(str).str.zfill(3)
print (df)
Y Z YZ
0 1 10 01010
1 3 30 03030
2 5 51 05051
3 7 74 07074
If need also change original columns:
df['Y'] = df['Y'].astype(str).str.zfill(2)
df['Z'] = df['Z'].astype(str).str.zfill(3)
df['YZ'] = df['Y'] + df['Z']
print (df)
Y Z YZ
0 01 010 01010
1 03 030 03030
2 05 051 05051
3 07 074 07074
Solution with join:
df['Y'] = df['Y'].astype(str).str.zfill(2)
df['Z'] = df['Z'].astype(str).str.zfill(3)
df['YZ'] = df[['Y','Z']].apply('-'.join, axis=1)
print (df)
Y Z YZ
0 01 010 01-010
1 03 030 03-030
2 05 051 05-051
3 07 074 07-074
And without change original columns:
df['YZ'] = df['Y'].astype(str).str.zfill(2) + '-' + df['Z'].astype(str).str.zfill(3)
print (df)
Y Z YZ
0 1 10 01-010
1 3 30 03-030
2 5 51 05-051
3 7 74 07-074