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pandas convert columns to percentages of the totals

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7

3

I have a dataframe with 4 columns an ID and three categories that results fell into

 <80% 80-90 >90
id
1 2 4 4
2 3 6 1
3 7 0 3

I would like to convert it to percentages ie:

 <80% 80-90 >90
id
1 20% 40% 40%
2 30% 60% 10%
3 70% 0% 30%

this seems like it should be within pandas capabilities but I just can't figure it out.

Thanks in advance!

python pandas

share|improve this question

edited Feb 2 '17 at 16:02

DTATSO

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Please provide an example dataframe, your numbers are a bit hard to interpret at first glace.
  
  – instant
  Feb 2 '17 at 15:41
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I'm not sure how to post the dataframe and I appologize my example lost its format but I have an index of ID and colums for <80%, 80%-90% and >90%. then I have data in the rows so row 0 may be iindex 1 with [3, 4,3] . I would like row 0 index 1 to have 30%, 40%, 30%. I am very new to pandas sorry i am still explaining it poorly.
  
  – DTATSO
  Feb 2 '17 at 15:56
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I guess it actually looks more like this: results <80%, 80%-90%, >90% id 1 3 4 3 2 7 3 0 and I want: results <80%, 80%-90%, >90% id 1 30% 40% 30% 2 70% 30% 0%
  
  – DTATSO
  Feb 2 '17 at 15:57
  
  

  
  
  
  

add a comment | 

7

3

I have a dataframe with 4 columns an ID and three categories that results fell into

 <80% 80-90 >90
id
1 2 4 4
2 3 6 1
3 7 0 3

I would like to convert it to percentages ie:

 <80% 80-90 >90
id
1 20% 40% 40%
2 30% 60% 10%
3 70% 0% 30%

this seems like it should be within pandas capabilities but I just can't figure it out.

Thanks in advance!

python pandas

share|improve this question

edited Feb 2 '17 at 16:02

DTATSO

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Please provide an example dataframe, your numbers are a bit hard to interpret at first glace.
  
  – instant
  Feb 2 '17 at 15:41
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I'm not sure how to post the dataframe and I appologize my example lost its format but I have an index of ID and colums for <80%, 80%-90% and >90%. then I have data in the rows so row 0 may be iindex 1 with [3, 4,3] . I would like row 0 index 1 to have 30%, 40%, 30%. I am very new to pandas sorry i am still explaining it poorly.
  
  – DTATSO
  Feb 2 '17 at 15:56
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I guess it actually looks more like this: results <80%, 80%-90%, >90% id 1 3 4 3 2 7 3 0 and I want: results <80%, 80%-90%, >90% id 1 30% 40% 30% 2 70% 30% 0%
  
  – DTATSO
  Feb 2 '17 at 15:57
  
  

  
  
  
  

add a comment | 

I have a dataframe with 4 columns an ID and three categories that results fell into

 <80% 80-90 >90
id
1 2 4 4
2 3 6 1
3 7 0 3

I would like to convert it to percentages ie:

 <80% 80-90 >90
id
1 20% 40% 40%
2 30% 60% 10%
3 70% 0% 30%

this seems like it should be within pandas capabilities but I just can't figure it out.

Thanks in advance!

python pandas

share|improve this question

edited Feb 2 '17 at 16:02

DTATSO

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

 <80% 80-90 >90
id
1 2 4 4
2 3 6 1
3 7 0 3

 <80% 80-90 >90
id
1 20% 40% 40%
2 30% 60% 10%
3 70% 0% 30%

share|improve this question

edited Feb 2 '17 at 16:02

DTATSO

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

share|improve this question

edited Feb 2 '17 at 16:02

DTATSO

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

asked Feb 2 '17 at 15:39

DTATSODTATSO

5215

1 Answer
1

active

oldest

votes

9

You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

• Calculate the sum of each column (df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.


• Divide the dataframe by the resulting series (df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.


• To finish, multiply the results by 100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).

share|improve this answer

edited Mar 7 at 2:51

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thank you so much this did the trick. Thanks for explaining the portions as well. Pandas seems to be a great tool that hopefully I'll get better at soon.
  
  – DTATSO
  Feb 2 '17 at 16:23
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  "Proportions" are percentages. 0.1 IS 10%. The % is basically a "divide by 100" operator. Putting 100 there is wrong and will probably lead to all kinds of errors down the line.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:25
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ChristophTerasa I'm not sure I follow. I understand that you can express the same value as 0.1 or as 10%, but the OP asked for the latter. Whether or not that leads to problems down the line depends on the OP's use case - maybe it needs to be in % format for some reason.
  
  – ASGM
  Feb 2 '17 at 16:38
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  OK, maybe "wrong" is not the correct word here. Your solution certainly is correct. I should probably say it is a dangerous paradigm. You cannot store 10% in a DataFrame/array, but you can store 0.1.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:52
  
  

  
  
  
  

add a comment | 

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9

You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

• Calculate the sum of each column (df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.


• Divide the dataframe by the resulting series (df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.


• To finish, multiply the results by 100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).

share|improve this answer

edited Mar 7 at 2:51

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thank you so much this did the trick. Thanks for explaining the portions as well. Pandas seems to be a great tool that hopefully I'll get better at soon.
  
  – DTATSO
  Feb 2 '17 at 16:23
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  "Proportions" are percentages. 0.1 IS 10%. The % is basically a "divide by 100" operator. Putting 100 there is wrong and will probably lead to all kinds of errors down the line.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:25
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ChristophTerasa I'm not sure I follow. I understand that you can express the same value as 0.1 or as 10%, but the OP asked for the latter. Whether or not that leads to problems down the line depends on the OP's use case - maybe it needs to be in % format for some reason.
  
  – ASGM
  Feb 2 '17 at 16:38
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  OK, maybe "wrong" is not the correct word here. Your solution certainly is correct. I should probably say it is a dangerous paradigm. You cannot store 10% in a DataFrame/array, but you can store 0.1.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:52
  
  

  
  
  
  

add a comment | 

9

You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

• Calculate the sum of each column (df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.


• Divide the dataframe by the resulting series (df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.


• To finish, multiply the results by 100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).

share|improve this answer

edited Mar 7 at 2:51

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thank you so much this did the trick. Thanks for explaining the portions as well. Pandas seems to be a great tool that hopefully I'll get better at soon.
  
  – DTATSO
  Feb 2 '17 at 16:23
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  "Proportions" are percentages. 0.1 IS 10%. The % is basically a "divide by 100" operator. Putting 100 there is wrong and will probably lead to all kinds of errors down the line.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:25
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @ChristophTerasa I'm not sure I follow. I understand that you can express the same value as 0.1 or as 10%, but the OP asked for the latter. Whether or not that leads to problems down the line depends on the OP's use case - maybe it needs to be in % format for some reason.
  
  – ASGM
  Feb 2 '17 at 16:38
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  OK, maybe "wrong" is not the correct word here. Your solution certainly is correct. I should probably say it is a dangerous paradigm. You cannot store 10% in a DataFrame/array, but you can store 0.1.
  
  – Jan Christoph Terasa
  Feb 2 '17 at 16:52
  
  

  
  
  
  

add a comment | 

You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

• Calculate the sum of each column (df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.


• Divide the dataframe by the resulting series (df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.


• To finish, multiply the results by 100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).

share|improve this answer

edited Mar 7 at 2:51

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

share|improve this answer

edited Mar 7 at 2:51

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740

answered Feb 2 '17 at 15:57

ASGMASGM

6,9511740