Custom mapping of categorical to numeric values
0
I have object type columns that have categorical values example 15-16 Years, 17-23 Years ..... I have converted them to category and then cat.codes. However,the coding values start from 0 for the first group 0-4 years and I want the coding to start from 1 ie 0-4->1, 5-12-> and @@->NaN
The suggested solution of using dictionary mapping still has issues. Following is mcve
import pandas as pd
data = ['0-4 Years', '5-12 Years','13-18 Years', '19-21 Years','22-25 Years','26-29 Years','30-35 Years',
'36-41 Years','42-45 Years','46-49 Years','50-55 Years', '56-63 Years']
df = pd.DataFrame(data,columns=['Age'],dtype=object)
df['Age']=df['Age'].astype('category')
cats = dict(enumerate(df['Age'].cat.categories, 2))
df['Age']=df['Age'].cat.codes.map(cats).astype('category')
df['Age']
and here is the output, as you can see if I change the enumeration start other than 0, there is an issue with values as NaN. Secondly the column is not coded either:
df['Age']
0 NaN
1 36-41 Years
2 NaN
3 NaN
4 0-4 Years
5 13-18 Years
6 19-21 Years
7 22-25 Years
8 26-29 Years
9 30-35 Years
10 42-45 Years
11 46-49 Years
Name: Age, dtype: category
Categories (9, object): [0-4 Years, 13-18 Years, 19-21 Years, 22-25 Years, ..., 30-35 Years, 36-41 Years, 42-45 Years, 46-49 Years]
How to fix this.
python mapping
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
add a comment |
0
I have object type columns that have categorical values example 15-16 Years, 17-23 Years ..... I have converted them to category and then cat.codes. However,the coding values start from 0 for the first group 0-4 years and I want the coding to start from 1 ie 0-4->1, 5-12-> and @@->NaN
The suggested solution of using dictionary mapping still has issues. Following is mcve
import pandas as pd
data = ['0-4 Years', '5-12 Years','13-18 Years', '19-21 Years','22-25 Years','26-29 Years','30-35 Years',
'36-41 Years','42-45 Years','46-49 Years','50-55 Years', '56-63 Years']
df = pd.DataFrame(data,columns=['Age'],dtype=object)
df['Age']=df['Age'].astype('category')
cats = dict(enumerate(df['Age'].cat.categories, 2))
df['Age']=df['Age'].cat.codes.map(cats).astype('category')
df['Age']
and here is the output, as you can see if I change the enumeration start other than 0, there is an issue with values as NaN. Secondly the column is not coded either:
df['Age']
0 NaN
1 36-41 Years
2 NaN
3 NaN
4 0-4 Years
5 13-18 Years
6 19-21 Years
7 22-25 Years
8 26-29 Years
9 30-35 Years
10 42-45 Years
11 46-49 Years
Name: Age, dtype: category
Categories (9, object): [0-4 Years, 13-18 Years, 19-21 Years, 22-25 Years, ..., 30-35 Years, 36-41 Years, 42-45 Years, 46-49 Years]
How to fix this.
python mapping
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
add a comment |
0
0
0
I have object type columns that have categorical values example 15-16 Years, 17-23 Years ..... I have converted them to category and then cat.codes. However,the coding values start from 0 for the first group 0-4 years and I want the coding to start from 1 ie 0-4->1, 5-12-> and @@->NaN
The suggested solution of using dictionary mapping still has issues. Following is mcve
import pandas as pd
data = ['0-4 Years', '5-12 Years','13-18 Years', '19-21 Years','22-25 Years','26-29 Years','30-35 Years',
'36-41 Years','42-45 Years','46-49 Years','50-55 Years', '56-63 Years']
df = pd.DataFrame(data,columns=['Age'],dtype=object)
df['Age']=df['Age'].astype('category')
cats = dict(enumerate(df['Age'].cat.categories, 2))
df['Age']=df['Age'].cat.codes.map(cats).astype('category')
df['Age']
and here is the output, as you can see if I change the enumeration start other than 0, there is an issue with values as NaN. Secondly the column is not coded either:
df['Age']
0 NaN
1 36-41 Years
2 NaN
3 NaN
4 0-4 Years
5 13-18 Years
6 19-21 Years
7 22-25 Years
8 26-29 Years
9 30-35 Years
10 42-45 Years
11 46-49 Years
Name: Age, dtype: category
Categories (9, object): [0-4 Years, 13-18 Years, 19-21 Years, 22-25 Years, ..., 30-35 Years, 36-41 Years, 42-45 Years, 46-49 Years]
How to fix this.
python mapping
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
I have object type columns that have categorical values example 15-16 Years, 17-23 Years ..... I have converted them to category and then cat.codes. However,the coding values start from 0 for the first group 0-4 years and I want the coding to start from 1 ie 0-4->1, 5-12-> and @@->NaN
The suggested solution of using dictionary mapping still has issues. Following is mcve
import pandas as pd
data = ['0-4 Years', '5-12 Years','13-18 Years', '19-21 Years','22-25 Years','26-29 Years','30-35 Years',
'36-41 Years','42-45 Years','46-49 Years','50-55 Years', '56-63 Years']
df = pd.DataFrame(data,columns=['Age'],dtype=object)
df['Age']=df['Age'].astype('category')
cats = dict(enumerate(df['Age'].cat.categories, 2))
df['Age']=df['Age'].cat.codes.map(cats).astype('category')
df['Age']
and here is the output, as you can see if I change the enumeration start other than 0, there is an issue with values as NaN. Secondly the column is not coded either:
df['Age']
0 NaN
1 36-41 Years
2 NaN
3 NaN
4 0-4 Years
5 13-18 Years
6 19-21 Years
7 22-25 Years
8 26-29 Years
9 30-35 Years
10 42-45 Years
11 46-49 Years
Name: Age, dtype: category
Categories (9, object): [0-4 Years, 13-18 Years, 19-21 Years, 22-25 Years, ..., 30-35 Years, 36-41 Years, 42-45 Years, 46-49 Years]
How to fix this.
python mapping
python mapping
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
edited Nov 22 '18 at 10:21
aus_fas
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
asked Nov 22 '18 at 4:03
aus_fasaus_fas
207
207
add a comment |
add a comment |
1 Answer
1
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You can create you own dictionary that maps codes and categories with:
cats = dict(enumerate(df['Age'].cat.categories, 1))
And use this dictionary to map it in the dataframe
df['Age'].cat.codes.map(cats).astype('category')
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
add a comment |
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1 Answer
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0
You can create you own dictionary that maps codes and categories with:
cats = dict(enumerate(df['Age'].cat.categories, 1))
And use this dictionary to map it in the dataframe
df['Age'].cat.codes.map(cats).astype('category')
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
add a comment |
0
You can create you own dictionary that maps codes and categories with:
cats = dict(enumerate(df['Age'].cat.categories, 1))
And use this dictionary to map it in the dataframe
df['Age'].cat.codes.map(cats).astype('category')
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
add a comment |
0
0
0
You can create you own dictionary that maps codes and categories with:
cats = dict(enumerate(df['Age'].cat.categories, 1))
And use this dictionary to map it in the dataframe
df['Age'].cat.codes.map(cats).astype('category')
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
You can create you own dictionary that maps codes and categories with:
cats = dict(enumerate(df['Age'].cat.categories, 1))
And use this dictionary to map it in the dataframe
df['Age'].cat.codes.map(cats).astype('category')
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
answered Nov 22 '18 at 4:16
b-fgb-fg
1,96411422
1,96411422
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
add a comment |
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
But what do you want to store in the column, the codes themselves?
– b-fg
Nov 22 '18 at 5:48
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
Also as I change the start of enumerate to any value higher than '1' then df['Age'] starts to have NaN for the categories where the mapping was available. The code upto cats is fine as I can see the categories based on dictionary but the second line seems to have an issue
– aus_fas
Nov 22 '18 at 6:08
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
The first line of code only creates a dictionary, so is not very useful on its own. That's why there a second line where you use the dictionary to map it to your dataframe.
– b-fg
Nov 22 '18 at 6:32
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
Maybe if you had provided a Minimum Complete and Verifiable Example this would not be the issue. So I encourage you to edit your question with some more content that other people can use to provide a better answer.
– b-fg
Nov 22 '18 at 6:43
add a comment |
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