'Piling' an Array, Collapse by Summation

Given a numpy array, I'd like to sum up uniform chunks of elements to form a new, smaller, array. It's similar to binning, but not by frequency. I'm not sure how else to describe it other than by example (below).

The question: Is there either a function for this or cleaner approach (using numpy/scipy)? I've looked into digitize and histogram, but think their implementations are lengthy. I've also thought about crafty indexing, but it's beyond me and might make a long ugly line of code.

import numpy as np  



old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3

new_data = np.zeros(int(np.size(old_data) / bin_size))

for ind, val in enumerate(new_data):

    leap = ind*bin_size

    new_data[ind] = 

np.sum(old_data[leap:leap+bin_size])

print(old_data, '->', bin_size, ':', new_data)



# [0 1 2 3 4 5 6 7 8] -> 3 : [ 3. 12. 21.]

asked Nov 22 '18 at 2:52

Captain Morgan

1406

maybe np.array([sum(old_data[bin_size*i: bin_size*(i+1)]) for i in range(old_data.size // bin_size)])

– William Lee
Nov 22 '18 at 2:58

add a comment |

import numpy as np  



old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3

new_data = np.zeros(int(np.size(old_data) / bin_size))

for ind, val in enumerate(new_data):

    leap = ind*bin_size

    new_data[ind] = 

np.sum(old_data[leap:leap+bin_size])

print(old_data, '->', bin_size, ':', new_data)



# [0 1 2 3 4 5 6 7 8] -> 3 : [ 3. 12. 21.]

asked Nov 22 '18 at 2:52

Captain Morgan

1406

maybe np.array([sum(old_data[bin_size*i: bin_size*(i+1)]) for i in range(old_data.size // bin_size)])

– William Lee
Nov 22 '18 at 2:58

add a comment |

import numpy as np  



old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3

new_data = np.zeros(int(np.size(old_data) / bin_size))

for ind, val in enumerate(new_data):

    leap = ind*bin_size

    new_data[ind] = 

np.sum(old_data[leap:leap+bin_size])

print(old_data, '->', bin_size, ':', new_data)



# [0 1 2 3 4 5 6 7 8] -> 3 : [ 3. 12. 21.]

asked Nov 22 '18 at 2:52

Captain Morgan

1406

import numpy as np  



old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3

new_data = np.zeros(int(np.size(old_data) / bin_size))

for ind, val in enumerate(new_data):

    leap = ind*bin_size

    new_data[ind] = 

np.sum(old_data[leap:leap+bin_size])

print(old_data, '->', bin_size, ':', new_data)



# [0 1 2 3 4 5 6 7 8] -> 3 : [ 3. 12. 21.]

python numpy scipy sum bin

asked Nov 22 '18 at 2:52

Captain Morgan

1406

asked Nov 22 '18 at 2:52

Captain Morgan

1406

asked Nov 22 '18 at 2:52

Captain Morgan

1406

asked Nov 22 '18 at 2:52

Captain Morgan

1406

asked Nov 22 '18 at 2:52

Captain Morgan

1406

maybe np.array([sum(old_data[bin_size*i: bin_size*(i+1)]) for i in range(old_data.size // bin_size)])

– William Lee
Nov 22 '18 at 2:58

add a comment |

maybe np.array([sum(old_data[bin_size*i: bin_size*(i+1)]) for i in range(old_data.size // bin_size)])

– William Lee
Nov 22 '18 at 2:58

maybe np.array([sum(old_data[bin_size*i: bin_size*(i+1)]) for i in range(old_data.size // bin_size)])

– William Lee
Nov 22 '18 at 2:58

add a comment |

1 Answer
1

active

oldest

votes

Assuming there's an integral number of bins, you can accomplish this with a reshape:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3



new_data = old_data.reshape(-1, bin_size).sum(axis=1)

new_data will then have the desired value of:

array([ 3, 12, 21])

If bin_size doesn't divide evenly into old_data.size, you can instead use resize:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

bin_size = 3



old_data.resize(old_data.size//bin_size + 1, bin_size)

new_data = old_data.sum(axis=1)

new_data will then have a value of:

array([ 3, 12, 21, 19])

Using resize has the downside of modifying old_data in place, so if you want to keep old_data around you should probably make a copy of it before you do the resize.

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

add a comment |

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1 Answer
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active

oldest

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1 Answer
1

active

oldest

votes

Assuming there's an integral number of bins, you can accomplish this with a reshape:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3



new_data = old_data.reshape(-1, bin_size).sum(axis=1)

new_data will then have the desired value of:

array([ 3, 12, 21])

If bin_size doesn't divide evenly into old_data.size, you can instead use resize:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

bin_size = 3



old_data.resize(old_data.size//bin_size + 1, bin_size)

new_data = old_data.sum(axis=1)

new_data will then have a value of:

array([ 3, 12, 21, 19])

Using resize has the downside of modifying old_data in place, so if you want to keep old_data around you should probably make a copy of it before you do the resize.

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

add a comment |

Assuming there's an integral number of bins, you can accomplish this with a reshape:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3



new_data = old_data.reshape(-1, bin_size).sum(axis=1)

new_data will then have the desired value of:

array([ 3, 12, 21])

If bin_size doesn't divide evenly into old_data.size, you can instead use resize:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

bin_size = 3



old_data.resize(old_data.size//bin_size + 1, bin_size)

new_data = old_data.sum(axis=1)

new_data will then have a value of:

array([ 3, 12, 21, 19])

Using resize has the downside of modifying old_data in place, so if you want to keep old_data around you should probably make a copy of it before you do the resize.

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

add a comment |

Assuming there's an integral number of bins, you can accomplish this with a reshape:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3



new_data = old_data.reshape(-1, bin_size).sum(axis=1)

new_data will then have the desired value of:

array([ 3, 12, 21])

If bin_size doesn't divide evenly into old_data.size, you can instead use resize:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

bin_size = 3



old_data.resize(old_data.size//bin_size + 1, bin_size)

new_data = old_data.sum(axis=1)

new_data will then have a value of:

array([ 3, 12, 21, 19])

Using resize has the downside of modifying old_data in place, so if you want to keep old_data around you should probably make a copy of it before you do the resize.

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

Assuming there's an integral number of bins, you can accomplish this with a reshape:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])

bin_size = 3



new_data = old_data.reshape(-1, bin_size).sum(axis=1)

new_data will then have the desired value of:

array([ 3, 12, 21])

If bin_size doesn't divide evenly into old_data.size, you can instead use resize:

old_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

bin_size = 3



old_data.resize(old_data.size//bin_size + 1, bin_size)

new_data = old_data.sum(axis=1)

new_data will then have a value of:

array([ 3, 12, 21, 19])

Using resize has the downside of modifying old_data in place, so if you want to keep old_data around you should probably make a copy of it before you do the resize.

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

edited Nov 22 '18 at 3:13

answered Nov 22 '18 at 3:01

tel

7,41621431

answered Nov 22 '18 at 3:01

tel

7,41621431

answered Nov 22 '18 at 3:01

tel

7,41621431

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

add a comment |

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

This is concise and does exactly as desired in a very legible manner. Thank you!

– Captain Morgan
Nov 22 '18 at 3:16

Is there a clean way to do this without altering the original array? One could simply np.copy it into a dummy array, but surely there's a slick way around this.

– Captain Morgan
Nov 22 '18 at 3:22

add a comment |

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