将pandas dataframe列数据分离成新的新列

Question

my data column combines information on two axes: wealth and life stage. I need to break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables. How would I go about separating and re-encoding this data?

This is the data dictionary for the column:

• -1: unknown
• 11: Wealthy Households - Pre-Family Couples & Singles
• 12: Wealthy Households - Young Couples With Children
• 13: Wealthy Households - Families With School Age Children
• 14: Wealthy Households - Older Families & Mature Couples
• 15: Wealthy Households - Elders In Retirement
• 21: Prosperous Households - Pre-Family Couples & Singles
• 22: Prosperous Households - Young Couples With Children
• 23: Prosperous Households - Families With School Age Children
• 24: Prosperous Households - Older Families & Mature Couples
• 25: Prosperous Households - Elders In Retirement
• 31: Comfortable Households - Pre-Family Couples & Singles
• 32: Comfortable Households - Young Couples With Children
• 33: Comfortable Households - Families With School Age Children
• 34: Comfortable Households - Older Families & Mature Couples
• 35: Comfortable Households - Elders In Retirement
• 41: Less Affluent Households - Pre-Family Couples & Singles
• 42: Less Affluent Households - Young Couples With Children
• 43: Less Affluent Households - Families With School Age Children
• 44: Less Affluent Households - Older Families & Mature Couples
• 45: Less Affluent Households - Elders In Retirement
• 51: Poorer Households - Pre-Family Couples & Singles
• 52: Poorer Households - Young Couples With Children
• 53: Poorer Households - Families With School Age Children
• 54: Poorer Households - Older Families & Mature Couples
• 55: Poorer Households - Elders In Retirement

I have tried np.where() and created conditions:

I have used.map():

data2.map({11:'Wealthy', 12:'Wealthy', 13:'Wealthy', 14:'Wealthy', 15:'Wealthy',
#          21:'Prosperous', 22:'Prosperous', 23:'Prosperous', 24:'Prosperous', 25:'Prosperous',
#          31:'Comfortable', 32:'Comfortable', 33:'Comfortable', 34:'Comfortable', 35:'Comfortable',
#          41:'Less_Afflutent', 42:'Less_Afflutent', 43:'Less_Afflutent', 44:'Less_Afflutent', 45:'Less_Afflutent',
#          51:'Poorer', 52:'Poorer', 53:'Poorer', 54:'Poorer', 55:'Poorer'})

I've tried a couple other things as well that didn't seem worth even posting. I'm new and still learning but I've never come across aa problem like this before and I've done SO much googling

Answer 1

If I understand the question correctly, you want to split your current status column into two new columns and then make mappings for each of those.

# Assuming your current column is called 'status'
df['tens_place'] = df['status']//10 # whole number of division, no remainder, gives -1 for -1
df['ones_place'] = df['status']%10 # just remainder, gives 9 for -1
tens_map = tens_map = {1:'Wealthy Households',2:'Prosperous households',
    3:'Comfortable Households',4:'Less Affluent households' ,5:'Poorer households',
    -1:'unknown'}
ones_map = ... # same sorta thing
df['wealth_status'] = df['tens_place'].map(tens_map)
df['family_status'] = df['ones_place'].map(ones_map)