How to calculate mean of specific rows based on value and column in numpy matrix?
I'm loading in a file to a pandas dataframe that looks something like:
A 3 2 4 1
B 1 3 5 2
C 2 8 9 1
A 4 1 2 3
I converted the dataframe to a numpy matrix because I'd like to store each mean and variance in separate 26 x 4 numpy matrices that will hold the variance and mean for each feature of each letter. My question is how to do I find the mean and variance for a specific letter and specific column? Also is there a better way to do this than going from the dataframe to matrix or is this an ok way to go about it?
Output I'd be expecting for A would be 3.5 for column one, 1.5 for column 2, 3 for column 3 and 2 for column 4. I would append each of these to a numpy matrix. Eventually the matrix I'd like to generate would look something like:
[[3.5, 1.5, 3, 2]
[1, 3, 5, 2]
[2, 8, 9, 1]]
So the first array are the means of letter A column 1-4, the next array are the means of letter B column 1-4, then letter C columns 1-4. For my actual dataset this would include all 26 letters.
Edit: Honest question, why am I getting downvotes for this? I googled this question and couldn't find any specific answers.
python numpy
asked Nov 15 '18 at 1:23
jj2593jj2593
245
add a comment |
I'm loading in a file to a pandas dataframe that looks something like:
A 3 2 4 1
B 1 3 5 2
C 2 8 9 1
A 4 1 2 3
I converted the dataframe to a numpy matrix because I'd like to store each mean and variance in separate 26 x 4 numpy matrices that will hold the variance and mean for each feature of each letter. My question is how to do I find the mean and variance for a specific letter and specific column? Also is there a better way to do this than going from the dataframe to matrix or is this an ok way to go about it?
Output I'd be expecting for A would be 3.5 for column one, 1.5 for column 2, 3 for column 3 and 2 for column 4. I would append each of these to a numpy matrix. Eventually the matrix I'd like to generate would look something like:
[[3.5, 1.5, 3, 2]
[1, 3, 5, 2]
[2, 8, 9, 1]]
So the first array are the means of letter A column 1-4, the next array are the means of letter B column 1-4, then letter C columns 1-4. For my actual dataset this would include all 26 letters.
Edit: Honest question, why am I getting downvotes for this? I googled this question and couldn't find any specific answers.
python numpy
asked Nov 15 '18 at 1:23
jj2593jj2593
245
how is usingnp.mean(axis=...)a problem?
– Julien
Nov 15 '18 at 1:25
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30
add a comment |
I'm loading in a file to a pandas dataframe that looks something like:
A 3 2 4 1
B 1 3 5 2
C 2 8 9 1
A 4 1 2 3
I converted the dataframe to a numpy matrix because I'd like to store each mean and variance in separate 26 x 4 numpy matrices that will hold the variance and mean for each feature of each letter. My question is how to do I find the mean and variance for a specific letter and specific column? Also is there a better way to do this than going from the dataframe to matrix or is this an ok way to go about it?
Output I'd be expecting for A would be 3.5 for column one, 1.5 for column 2, 3 for column 3 and 2 for column 4. I would append each of these to a numpy matrix. Eventually the matrix I'd like to generate would look something like:
[[3.5, 1.5, 3, 2]
[1, 3, 5, 2]
[2, 8, 9, 1]]
So the first array are the means of letter A column 1-4, the next array are the means of letter B column 1-4, then letter C columns 1-4. For my actual dataset this would include all 26 letters.
Edit: Honest question, why am I getting downvotes for this? I googled this question and couldn't find any specific answers.
python numpy
asked Nov 15 '18 at 1:23
jj2593jj2593
245
I'm loading in a file to a pandas dataframe that looks something like:
A 3 2 4 1
B 1 3 5 2
C 2 8 9 1
A 4 1 2 3
I converted the dataframe to a numpy matrix because I'd like to store each mean and variance in separate 26 x 4 numpy matrices that will hold the variance and mean for each feature of each letter. My question is how to do I find the mean and variance for a specific letter and specific column? Also is there a better way to do this than going from the dataframe to matrix or is this an ok way to go about it?
Output I'd be expecting for A would be 3.5 for column one, 1.5 for column 2, 3 for column 3 and 2 for column 4. I would append each of these to a numpy matrix. Eventually the matrix I'd like to generate would look something like:
[[3.5, 1.5, 3, 2]
[1, 3, 5, 2]
[2, 8, 9, 1]]
So the first array are the means of letter A column 1-4, the next array are the means of letter B column 1-4, then letter C columns 1-4. For my actual dataset this would include all 26 letters.
Edit: Honest question, why am I getting downvotes for this? I googled this question and couldn't find any specific answers.
python numpy
python numpy
asked Nov 15 '18 at 1:23
jj2593jj2593
245
asked Nov 15 '18 at 1:23
jj2593jj2593
245
edited Nov 15 '18 at 3:09
jj2593
asked Nov 15 '18 at 1:23
jj2593jj2593
245
asked Nov 15 '18 at 1:23
jj2593jj2593
245
asked Nov 15 '18 at 1:23
jj2593jj2593
245
245
how is usingnp.mean(axis=...)a problem?
– Julien
Nov 15 '18 at 1:25
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30
add a comment |
how is usingnp.mean(axis=...)a problem?
– Julien
Nov 15 '18 at 1:25
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30
how is using
np.mean(axis=...) a problem?– Julien
Nov 15 '18 at 1:25
how is using
np.mean(axis=...) a problem?– Julien
Nov 15 '18 at 1:25
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30
add a comment |
1 Answer
1
active
oldest
votes
This should do it but you’ll need to specify column headers and keep your data in a dataframe.
df[column_name].iloc[row_index].mean(axis=0)
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
add a comment |
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1 Answer
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1 Answer
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oldest
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This should do it but you’ll need to specify column headers and keep your data in a dataframe.
df[column_name].iloc[row_index].mean(axis=0)
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
add a comment |
This should do it but you’ll need to specify column headers and keep your data in a dataframe.
df[column_name].iloc[row_index].mean(axis=0)
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
add a comment |
This should do it but you’ll need to specify column headers and keep your data in a dataframe.
df[column_name].iloc[row_index].mean(axis=0)
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
This should do it but you’ll need to specify column headers and keep your data in a dataframe.
df[column_name].iloc[row_index].mean(axis=0)
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
answered Nov 15 '18 at 1:34
grantaguinaldograntaguinaldo
217
217
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
add a comment |
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
I got an error when I tried this and I'm not sure this is what I'm looking for. Maybe I'm not being very clear in my initial post. I have a dataset of around 15,000 rows. The first column has some letter A through Z. I need the mean for each column of each specific letter. So in total with 4 features I'd need 112 total means, 26 means for each column.
– jj2593
Nov 15 '18 at 1:49
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
Can you update your question and show an example of the output you’re looking for? What you’ve asked for and what you want seem to be different.
– grantaguinaldo
Nov 15 '18 at 2:01
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
I just added the output. My question is more specific to how to calculate the means, not necessarily add them to my matrix but I think my edit should give you a better idea of what I'm trying to do. I also got my code to work using the dataframe but I'd still like to know how to do it with a numpy matrix.
– jj2593
Nov 15 '18 at 3:10
add a comment |
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how is using
np.mean(axis=...)a problem?– Julien
Nov 15 '18 at 1:25
That won't give me the mean of the specific letter right? I need the mean for column 1 for letter 'A', then the mean for column 1 for letter 'B', etc. until I have the means for each letter and every column. So in total I'd have 112 means.
– jj2593
Nov 15 '18 at 1:30