Create Multiple New Columns From Multiple Dictionaries

Question

I can successfully create a single new attribute on a dataframe called df using a single dictionary as follows:

1. Create Precursor DataFrame mye2_men:

In [13]: mye2_men = pd.read_csv("~/03_Maps_March_2020/mye2_men.csv",index_col="Code")   
    ...: mye2_men.head()                                                                                                                                                                                                                 
Out[13]: 
                        Name Geography1  All ages       0       1       2       3       4       5       6       7       8       9  ...      78      79      80      81      82      83      84      85     86     87     88     89      90
Code                                                                                                                               ...                                                                                                    
K02000001     UNITED KINGDOM    Country  32790202  382332  395273  408684  408882  412553  421934  434333  427809  419161  414994  ...  192839  186251  175626  160475  146314  132941  116050  103669  93155  81174  68110  55652  183486
K03000001      GREAT BRITAIN    Country  31864002  370474  382933  395754  396181  399764  409061  420947  414613  406062  401647  ...  188073  181546  171350  156506  142851  129815  113306  101194  91038  79342  66699  54387  179629
K04000001  ENGLAND AND WALES    Country  29215251  343642  355122  366722  366885  370156  379046  389944  382853  375940  370701  ...  172046  166392  157065  143896  131207  119193  104143   93055  83798  73224  61794  50297  167009
E92000001            ENGLAND    Country  27667942  327309  338368  349229  349199  352148  360688  370995  363496  356965  351790  ...  161540  156343  147733  135514  123492  112133   98000   87528  79030  69067  58264  47498  157788
E12000001         NORTH EAST     Region   1305486   13992   14423   15124   15159   15542   15839   16314   16283   16068   15748  ...    8130    8108    7601    6977    6118    5723    4958    4383   3889   3360   2747   2148    6822

[5 rows x 94 columns]

2. Create Target DataFrame df

In [14]: df = pd.DataFrame({"A":[num for num in range(0,430)],  
    ...:                    "B":[num**2 for num in range(0,430)],   
    ...:                    "Code":mye2_men.index})  
    ...: df.head()                                                                                                                                                                                                                       
Out[14]: 
   A   B       Code
0  0   0  K02000001
1  1   1  K03000001
2  2   4  K04000001
3  3   9  E92000001
4  4  16  E12000001

3. Create Dictionary To Use In Mapping:

In [15]: male_counts = mye2_men["All ages"].to_dict()  
    ...: male_counts                                                                                                                                                                                                                     
Out[15]: 
{'K02000001': 32790202,
 'K03000001': 31864002,
 'K04000001': 29215251,
 'E92000001': 27667942,
 'E12000001': 1305486,
 'E06000047': 259299,
 'E06000005': 51919,
 'E06000001': 45524 ....}

4. Map the male_counts Dictionary to DataFrame df To Create New Column "male_count":

In [19]: # CREATE NEW male_count COLUMN IN df 
    ...: df["male_count"] = df["Code"].map(male_counts) 
    ...: df.head()                                                                                                                                                                                                                       
Out[19]: 
   A   B       Code  male_count
0  0   0  K02000001    32790202
1  1   1  K03000001    31864002
2  2   4  K04000001    29215251
3  3   9  E92000001    27667942
4  4  16  E12000001     1305486

For the 2nd dictionary:

In [20]: female_counts = (mye2_men["All ages"]+10).to_dict() 
    ...: female_counts                                                                                                                                                                                                                   
Out[20]: 
{'K02000001': 32790212,
 'K03000001': 31864012,
 'K04000001': 29215261,
 'E92000001': 27667952,
 'E12000001': 1305496,
 'E06000047': 259309,
 'E06000005': 51929 ...}

I can successfully produce a second attribute called df["female_count"] by repeating step 4 above, but this time using the female_counts dictionary.

How can I create multiple new df columns (ie df["male_count"] and df["female_count"]) in a single step?

Many thanks

Note: The mye2_men data is from the "MYE2 - Males" tab of the following excel doc: https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fpopulationestimatesforukenglandandwalesscotlandandnorthernireland%2fmid2019april2020localauthoritydistrictcodes/ukmidyearestimates20192020ladcodes.xls

Answer 1

Create DataFrame from dictionaries and then use DataFrame.join:

new = pd.DataFrame({'male_count':male_counts, 'female_count':female_count})
df = df.join(new, on='Code')