How to map value from nested dictionary to multiple columns in dataframe

Question

For example: I have df like this:

id      Status         Country       Income
1          4               2          3
2          5               3          2

and dictionary like this:

d_dict = {Status : { '4':'Married', '5':'UnMarried'},
        Country: { '2': 'Japan' , '3': 'China'},
        Income: {'3': "5000-10000", 2: "11000-20000"}}

I want to map the values based on nested dictionary. I can do for one column like this:

for k,v in d_dict.items():
    max_d[k] = max(v, key=v.get)
df['Status'] = df['Status'].map(max_d)

But I have more than 2000 columns and I am not sure how I can do for multiple columns.

BENY · Accepted Answer · 2020-03-10 15:30:41Z

1

You can try replace

df=df.astype(str).replace(d_dict)
df
Out[259]: 
   id     Status Country      Income
0   1    Married   Japan  5000-10000
1   2  UnMarried   China 11000-20000

edited Mar 10, 2020 at 15:30

answered Mar 10, 2020 at 15:21

BENY

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5 Comments

s_khan92 Over a year ago

there are some columns where i don't need dictionary or i can't use... Is there any way to handle that?

s_khan92 Over a year ago

Key of dictionary is always the name of column and if it matches then it should use the dictionary.

BENY Over a year ago

@s_khan92 not sure what you mean , the replace is match the columns first , then replace value within the dict

BENY Over a year ago

@s_khan92 first key in dict will match the columns , the second key will look the columns value replace the value in dict

s_khan92 Over a year ago

I dont know why but its not able to match.. and giving wrong values

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