CODEWITHSUNDEEP
pythonpandas
beyond_inifinity
beyond_inifinity

Reputation: 453

How to populate multiple dictionary with common keys to pandas dataframe?

I have a list of dictionaries where keys are identical but values in each dictionary is not same, and the order of each dictionary strictly preserved. I am trying to find an automatic solution to populate these dictionaries to pandas dataframe as new column, but didn't get the expected output.

original data on gist

here is the data that I have on old data on gist.

my attempt

here is my attempt to populate multiple dictionaries with same keys but different values (binary value), my goal is I want to write down handy function to vectorize the code. Here is my inefficient code but works on gist

import pandas as pd

dat= pd.read_csv('old_data.csv', encoding='utf-8')

dat['type']=dat['code'].astype(str).map(typ)
dat['anim']=dat['code'].astype(str).map(anim)
dat['bovin'] = dat['code'].astype(str).map(bov)
dat['catg'] = dat['code'].astype(str).map(cat)
dat['foot'] = dat['code'].astype(str).map(foo)

my code works but it is not vectorized (not efficient I think). I am wondering how can I make this few lines of a simple function. Any idea? how to we make this happen as efficiently as possible?

Here is my current and the desired output:

since I got correct output but code is not well efficient here. this is my current output on gist

Upvotes: 0

Views: 181

Answers (1)

sdhaus
sdhaus

Reputation: 1896

If you restructure your dictionaries into a dictionary of dictionaries you can one line it:

 for keys in values.keys():
        dat[keys]=dat['code'].astype(str).map(values[keys])

Full code:

values = {"typ" :{
    '20230' : 'A',
    '20130' : 'A',
    '20220' : 'A',
    '20120' : 'A',
    '20329' : 'A',
    '20322' : 'A',
    '20321' : 'B',
    '20110' : 'B',
    '20210' : 'B',
    '20311' : 'B'
    } ,

"anim" :{
    '20230' : 'AOB',
    '20130' : 'AOB',
    '20220' : 'AOB',
    '20120' : 'AOB',
    '20329' : 'AOC',
    '20322' : 'AOC',
    '20321' : 'AOC',
    '20110' : 'AOB',
    '20210' : 'AOB',
    '20311' : 'AOC'
    } ,

"bov" :{
    '20230' : 'AOD',
    '20130' : 'AOD',
    '20220' : 'AOD',
    '20120' : 'AOD',
    '20329' : 'AOE',
    '20322' : 'AOE',
    '20321' : 'AOE',
    '20110' : 'AOD',
    '20210' : 'AOD',
    '20311' : 'AOE'
    } ,

"cat" :{
    '20230' : 'AOF',
    '20130' : 'AOG',
    '20220' : 'AOF',
    '20120' : 'AOG',
    '20329' : 'AOF',
    '20322' : 'AOF',
    '20321' : 'AOF',
    '20110' : 'AOG',
    '20210' : 'AOF',
    '20311' : 'AOG'
    } ,

"foo" :{
    '20230' : 'AOL',
    '20130' : 'AOL',
    '20220' : 'AOM',
    '20120' : 'AOM',
    '20329' : 'AOL',
    '20322' : 'AOM',
    '20321' : 'AOM',
    '20110' : 'AOM',
    '20210' : 'AOM',
    '20311' : 'AOM'
    } 
}




import pandas as pd

dat= pd.read_csv('old_data.csv', encoding='utf-8')
for keys in values.keys():
    dat[keys]=dat['code'].astype(str).map(values[keys])

Upvotes: 1

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