{"id":23093048,"url":"https://github.com/crispengari/numpy","last_synced_at":"2026-05-03T16:31:32.615Z","repository":{"id":141094825,"uuid":"341022900","full_name":"CrispenGari/Numpy","owner":"CrispenGari","description":"💎 The core concept of numpy arrays","archived":false,"fork":false,"pushed_at":"2023-10-06T13:13:25.000Z","size":1352,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-03T18:52:02.516Z","etag":null,"topics":["ai","artificial-intelligence","datascience","jupyter-notebook","machine-learning","numpy","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CrispenGari.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-02-21T22:59:39.000Z","updated_at":"2023-09-06T09:28:12.000Z","dependencies_parsed_at":null,"dependency_job_id":"a74c370a-319d-410f-b1d4-0e719bd22d6e","html_url":"https://github.com/CrispenGari/Numpy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/CrispenGari/Numpy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CrispenGari%2FNumpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CrispenGari%2FNumpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CrispenGari%2FNumpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CrispenGari%2FNumpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CrispenGari","download_url":"https://codeload.github.com/CrispenGari/Numpy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CrispenGari%2FNumpy/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32577119,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-03T06:36:36.687Z","status":"ssl_error","status_checked_at":"2026-05-03T06:36:09.306Z","response_time":103,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","artificial-intelligence","datascience","jupyter-notebook","machine-learning","numpy","python"],"created_at":"2024-12-16T21:46:22.416Z","updated_at":"2026-05-03T16:31:32.593Z","avatar_url":"https://github.com/CrispenGari.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numpy\n\nThe core concept of numpy arrays\n\n\u003cp align=\"center\"\u003e\n \u003cimg src=\"https://img.shields.io/static/v1?label=language\u0026message=python\u0026color=green\"/\u003e\n\u003cimg src=\"https://img.shields.io/static/v1?label=liked-most\u0026message=jupyter-notebook\u0026color=brightgreen\"/\u003e\n\u003cimg src=\"https://img.shields.io/static/v1?label=liked-most\u0026message=numpy\u0026color=blueviolet\"/\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/CrispenGari/Numpy/blob/main/download.png\"/\u003e\n\u003c/p\u003e\n\n## Why numpy over python list\n\n- numpy are faster because the used fixed type\n- List requires more memory than numpy arrays\n\n## Applications\n\n- Mathematics (MATLAB)\n- Backend\n- Plotting\n- Machine Learning\n\n## Getting started\n\nInstall numpy using pip by running the following command\n\n```shell\n$ pip install numpy\nOR\n$ python -m pip install numpy\n\n```\n\n## Importing numpy to our project\n\n```python\nimport numpy as np\n\n```\n\n## Creating arrays\n\n```python\n# Createing a 1d array\n\na = np.array([2,3,4,5,6])\n\nprint(a)\n\n# Creating a 2d array\n\nb = np.array([[1,2,3,4], [5,6,7,8]])\nprint(b)\n\n```\n\n## Getting dimensions of arrays\n\n```python\n# Getting the dimension of the array\nprint(a.ndim)\nprint(b.ndim)\n```\n\n## Getting the shape of the array\n\n```python\n# Getting the shape of numpy arrays\nprint(a.shape)\nprint(b.shape) # returns (2 rows, 4 colms)\n```\n\n## Getting the data type of the array\n\n```python\n# Getting the datatype of the array in numpy\nprint(a.dtype) # int32\n```\n\n### Specifying the data type of the array during initialization\n\n```python\n# Specifing the datatype in a numpy array\n\nc = np.array([2, 3,4,5,6,8,9], dtype='int16')\n\n# getting the datatype of numpy array c\n\nprint(c.dtype) # int16\n```\n\n## Getting the size of the array\n\n```python\n# Getting the size of the array\n# int32 type\nprint(a.itemsize) # 4\n# the int16 type\nprint(c.itemsize) # 2\n```\n\n## Getting the total number of elements in an array\n\n```python\n#  Getting the total number of elements in the array\nprint(a.size) # 4\nprint(b.size) # 8\n```\n\n## Getting the total size of an array\n\n```python\n## Getting the total size of an array\n\n## ------------ METHOD 1\nprint(a.itemsize * a.size)\n##------------- METHOD 2\nprint(a.nbytes)\n```\n\n## Array Indexing\n\n### Getting the a specific element in a a given position in a N-d array\n\n```python\nnpArray = np.array([[5, 10, 15, 20, 25, 30], [2, 4,6, 8, 10, 12]])\n# The shape\nprint(npArray.shape)\n\n# The number of elements\nprint(npArray.size)\n\n# The total memory in an array\nprint(npArray.size * npArray.itemsize)\n# OR\nprint(npArray.nbytes)\n\n\n# GETTING THE ELEMENT 10 IN OUR ARRAY\n\nprint(npArray[1, -2])\n# OR\nprint(npArray[1, 4])\n\n```\n\n### Getting a specific column in an n-d array\n\n```python\n# GETTING A SPECIFIC COLUMN (last column)\nprint(npArray[:, -1])\nprint(npArray[:, 5])\n\n```\n\n### Getting a specific row in an n-d array\n\n```python\n# GETTING A SPECIFIC ROW\nprint(npArray[1, :])\n# OR\nprint(npArray[-1: :][0])\n```\n\n### Numpy array indexing using\n\n`array[index, startindex, endindex, step]`\n\n```python\n### Numpy array indexing using `array[index, startindex, endindex, step]`\n\n# Let's say we want to get all the numbers that ends with 5\n\nprint(npArray[0, 0: -1: 2])\n```\n\n### Changing the item value in an array\n\n`array[index, index] = value`\n\n```python\n## Changing the item value in an array `array[index, index] = value`\n\n# Let's say we ant to update 10 in the npArray to be 100\n\nnpArray[1, -2] = 100\nprint(npArray[1,-2]) # 100\n\n# Let's say now we want to change all the numbers that ends with 5 to have a value of 99\nnpArray[0, 0: -1:2] = 99\n\nnpArray\n```\n\n### 3 Dimensional Array\n\n```python\n\n# 3 Dimensional Array\n\nnp3D = np.array([\n    [\n        [2,4,6,8,10],\n        [4,8,12,16,20]\n    ],\n    [\n        [3,6,9, 12, 15],\n        [6,12,18, 24, 30]\n    ]\n])\n\n#  Getting the dimension\n\nprint(np3D.ndim) # 3\n\n# Getting the total elements\nprint(np3D.size) # 20\n\n# Getting the total memory\nprint(np3D.nbytes) # 80\n\n# Getting the element 18 from the array\nprint(np3D[-1,-1, -3]) # 18\n\n```\n\n## Initializing Different types of arrays Matrix\n\n### np.zeros(shape, type)\n\n```python\n### np.zeros(shape, type)\nzeros = np.zeros([2, 4, 6])\nzeros\n```\n\n### np.ones(shape, type)\n\n```python\n### np.ones(shape, type)\nones = np.ones((2, 3,5), dtype=np.uint32)\nones\n```\n\n### np.full(shape, number, dtype)\n\n```python\n### any number 255\nwhiteImage = np.full((4,4), 255, dtype='uint32')\nwhiteImage\n```\n\n### np.full_like(shape_of_the_existing_array, number)\n\n```python\n### full_like\n# Let's say we want to generate the array of 100s that has the same shape with whiteImage array\n\narray100s = np.full_like(whiteImage, 9)\narray100s\n\n```\n\n## Generating array from random numbers in numpy\n\n### np.random.rand(shape)\n\n```python\nrandomFloat = np.random.rand(2,3,6)\nrandomFloat\n\n```\n\n### np.random.randint(start, end_exclusive, size)\n\n```python\n\n# Generating an array of integers\nrandomInt = np.random.randint(-7, 15, size=(7, 9,7))\nrandomInt\n\n```\n\n### np.identity(number, dtype)\n\nGenerates the identity matrix\n\n```python\nidentity = np.identity(4, dtype='int8')\nidentity\n```\n\n### Generating array of integers using `np.arange(n).reshape(shape)`\n\n\u003e Note that the product of the shape (a, b) must give us n which means `n = a * b` otherwise an error will occur the following is an example of how we can generate array of integers using the two methods `arange` and `reshape`\n\n```python\n## Generating element's using the np.arange(15).reshape(3, 5) methods\narr = np.arange(15).reshape(3,5)\narr\n```\n\n## Repeating an array\n\n### Repeating an array vertically\n\n```python\narr1 = np.array([[2,4,6]])\narr2 = np.repeat(arr1, 3, axis=0) #  repeats vertically\narr2\n```\n\n### Repeating an array horizontally\n\n```python\narr3 = np.repeat(arr1, 3, axis=1) # Repeats horizontally\narr3\n```\n\n### Universal Functions\n\n- arange(n)\n\n```python\n# the arange(n) function that generates 1-d array of integers from 0 to n-1\na = np.arange(10)\na # array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n\n```\n\n\u003e There are a lot of functions that are used in numpy arrays some of them are\n\n- all\n  - Test whether all array elements along a given axis evaluate to True.\n  ```python\n  print(np.all(np.array([True, False, True, True, False]))) # False\n  print(np.all(np.array([True, True]))) # True\n  print(np.all(np.array([1,2, 3,-10]))) # True\n  ```\n- any\n  - Test whether any array element along a given axis evaluates to True.\n  ```python\n  print(np.any(np.array([True, False, True, True, False]))) # True\n  print(np.any(np.array([not True, not True]))) # False\n  print(np.any(np.array([1,2, 3,-10]))) # True\n  ```\n- apply_along_axis\n  - Read more [here](https://numpy.org/doc/1.14/reference/generated/numpy.apply_along_axis.html#numpy.apply_along_axis)\n- argmax\n\n  - Returns the indices of the maximum values along an axis.\n\n  ```python\n  print(np.argmax(np.array([1,10,-19,9, 0,56,108,-76]))) # 6\n  ```\n\n- argmin\n\n  - Returns the indices of the minimum values along an axis.\n\n  ```python\n  print(np.argmax(np.array([1,10,-19,9, 0,56,108,-76]))) # 7\n  ```\n\n- argsort\n  - Returns the indices that would sort an array.\n  ```python\n  print(np.argmax(np.array([1,10,-19,9, 0,56,108,-76]))) # 7\n  ```\n- average\n  - Compute the weighted average along the specified axis.\n  ```python\n  print(np.average(np.array([1,10,-19,9, 0,56,108,-76]))) # 11.125\n  ```\n- bincount\n  - Count number of occurrences of each value in array of non-negative ints.\n\n```python\n# bincount\nprint(np.bincount(np.array([1,1,2,6,7,0]))) # [1 2 1 0 0 0 1 1]\n```\n\n- ceil\n  - Return the ceiling of the input, element-wise from floats values\n\n```python\n# ceil function\n\nprint(np.ceil(np.array([1.2, 2.8, 6.9, -6,10,9]))) # [ 2.  3.  7. -6. 10.  9.]\n```\n\n- clip\n  - Clip (limit) the values in an array.\n\n```python\n# clip function\nprint(np.clip(np.arange(10), 2, 8)) # [2 2 2 3 4 5 6 7 8 8]\n```\n\n- conj\n  - Return the complex conjugate, element-wise.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.conj.html#numpy.conj)\n- corrcoef\n  - Return Pearson product-moment correlation coefficients.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.corrcoef.html#numpy.corrcoef)\n- cov\n  - Estimate a covariance matrix, given data and weights.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.cov.html#numpy.cov)\n- cross\n  - Return the cross product of two (arrays of) vectors.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.cross.html#numpy.cross)\n- cumprod\n  - Return the cumulative product of elements along a given axis.\n\n```python\nprint(np.cumprod(np.array([2,3,5]))) # [ 2  6 30]\n```\n\n- cumsum\n  - Return the cumulative sum of elements along a given axis.\n\n```python\nprint(np.cumsum(np.array([2,3,5]))) # [ 2  5 10]\n```\n\n- diff\n\n  - Calculate the n-th discrete difference along the given axis.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.diff.html#numpy.diff)\n\n- dot\n  - Dot product of two arrays. Specifically\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.dot.html#numpy.dot)\n- floor\n\n```python\n# floor function\nprint(np.floor(np.array([1.2, 2.8, 6.9, -6,10,9]))) # [ 1.  2.  6. -6. 10.  9.]\n```\n\n- inner\n\n  - Inner product of two arrays.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.inner.html#numpy.inner)\n\n- lexsort\n  - Perform an indirect sort using a sequence of keys.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.lexsort.html#numpy.lexsort)\n- max\n  - Return the largest item in an iterable or the largest of two or more arguments.\n\n```python\n# max function\nprint(np.max(np.random.randint(1, 100, 5))) # a random numbaer\n```\n\n- maximum\n  - Element-wise maximum of array elements.\n  ```python\n  print (np.maximum([2, 3, 4], [1, 5, 2])) # [2 5 4]\n  ```\n- mean\n  - Compute the arithmetic mean along the specified axis.\n\n```python\n# mean\nprint(np.mean(np.ones((5,)))) # 1.0\n```\n\n- median\n  - Compute the median along the specified axis.\n\n```python\nprint(np.median(np.array([2, 5, -6, 8, 9]))) # 5\n```\n\n- min\n  - Return the smallest item in an iterable or the largest of two or more arguments.\n\n```python\n# max function\nprint(np.min(np.random.randint(1, 100, 5))) # a random number\n```\n\n- ## minimum\n\n```python\nprint (np.minimum([2, 3, 4], [1, 5, 2])) # [1 3 2]\n```\n\n- nonzero\n  - Return the indices of the elements that are non-zero.\n\n```python\n# nonzero function\nprint(np.nonzero(np.round(np.array(np.random.rand(10))))) # (array([2, 3, 4, 6, 7, 8], dtype=int64),)\n```\n\n- outer\n  - Compute the outer product of two vectors.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.outer.html#numpy.outer)\n- prod\n  - Return the product of array elements over a given axis.\n\n```python\n# prod function\nprint(np.prod(np.array([2,2,2,3]))) # 24\n```\n\n- re\n  - This module provides regular expression matching operations similar to those found in Perl.\n    [Example](https://docs.python.org/dev/library/re.html#module-re)\n- round\n  - Return number rounded to ndigits precision after the decimal point. If ndigits is omitted or is None, it returns the nearest integer to its input.\n\n```python\nnp.round(np.array(np.random.rand(10))) # a random array\n```\n\n- sort\n  - Return a sorted copy of an array.\n\n```python\nnp.sort(np.array([1, 2,10, 0,7,-6])) # array([-6,  0,  1,  2,  7, 10])\n```\n\n- std\n  - Compute the standard deviation along the specified axis.\n\n```python\nnp.std(np.array([5, 9, 9, 0])) # 3.6996621467371855\n```\n\n- sum\n  - Sum of array elements over a given axis\n\n```python\nnp.sum(np.ones(5)) # 5\n```\n\n- trace\n  - Return the sum along diagonals of the array.\n  ```python\n  A = np.array([\n   [2, 3, 4.],\n   [4, 5, 9]\n  ])\n  np.trace(A) # 7\n  ```\n- transpose\n  - Permute the dimensions of an array.\n  ```python\n  A = np.array([\n   [2, 3, 4.],\n   [4, 5, 9]])\n   print(np.transpose(A))\n  ```\n- var\n  - Compute the variance along the specified axis.\n\n```python\n# var function the varience of the array\nnp.var(np.array([5, 9, 9, 0])) # 13.6875\n```\n\n- vdot\n  - Return the dot product of two vectors.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.vdot.html#numpy.vdot)\n- vectorize\n  - Generalized function class.\n    [Example](https://numpy.org/doc/1.14/reference/generated/numpy.vectorize.html#numpy.vectorize)\n- where\n  Return elements, either from x or y, depending on condition.\n  [Example](https://numpy.org/doc/1.14/reference/generated/numpy.where.html#numpy.where)\n\n## Copying numpy arrays\n\n### Direct copying\n\n\u003e This method just assigns the first array to the second array. So when the value of the first second array changes then the value of the first array also changes.\n\n```python\n\n## Copying arrays\n\narray1 = np.array([1,2,3,4,5])\narray2 = array1\nprint(array2) # [1 2 3 4 5]\narray2[-1]= 500\nprint(array2) # [1 2 3 4 500]\n\n# Array1 also changes\n\nprint(array1) # [1 2 3 4 500]\n\n```\n\n### using the copy() method\n\n\u003e This method copies the array content not the original array. So the content of the copied array when changed doesn't affects the content of the original array.\n\n```python\n\narray1 = np.array([1,2,3,4,5])\narray2 = array1.copy()\nprint(array2) # [1 2 3 4 5]\narray2[-1]= 500\nprint(array2) # [1 2 3 4 500]\n\n# Array1 also changes\n\nprint(array1) # [1 2 3 4 5]\n\n```\n\n### Mathematics on numpy arrays\n\nSuppose we have the following arrays\n\n```python\n\na = np.array([2,3,4,5,9,90])\nb = np.array([1,2,3,4,5,6])\n\n```\n\n### Trigonometry\n\n```python\n\n### Trigonometry\n\nprint(np.sin(a))\nprint(np.cos(a))\nprint(np.tan(a))\n\n# There are more\n\n```\n\n### Maths on two arrays\n\n```python\n\n### Maths on two arrays\n\nprint(a * b)\nprint(a ** b)\nprint(a / b)\nprint(a - b)\nprint(a + b)\n\n# There are more\n\n```\n\n### Math on one array\n\n```python\n\n### Math on one array\n\nprint(a \\* 3)\nprint(a \\*\\* 3)\nprint(a / 3)\nprint(a - 3)\nprint(a + 3)\n\n# There are more\n\n```\n\n## Linear Algebra `dot products`\n\n\u003e In Linear Algebra, we are more focusing on multiplying arrays. So the rule is simple, THE NUMBER OF ONE COLUMNS OF MATRIX a SHOULD BE EQUAL TO THE NUMBER OF ROWS OF MATRIX B.\n\n```python\n\na = np.ones([2, 2], dtype='int32')\nb = np.full([2,2], 3, dtype= 'int32')\n\nprint(a \\* b)\n\n# The correct way\n\nprint(np.matmul(a, b))\n\n## Getting the deteminant of a\n\nprint(np.linalg.det(b))\n\n```\n\n\u003e For more of these on linear algebra go to the [Docs](https://docs.scipy.org/doc/)\n\n## Statistics\n\nSuppose we have the following array.\n\n```python\n\narr = np.array([[2, 3, 4, 5,-1,9],[100, 19, 100, 78,10,-90]])\n\n```\n\n### Finding the maximum element of the array\n\n```python\n\nprint(np.max(arr))\n\n```\n\n### Finding the minimum element of the array\n\n```python\n\nprint(np.min(arr))\n\n```\n\n### Summing the elements of the array\n\n```python\n\nprint(np.sum(arr))\n\n```\n\n## Reshaping arrays\n\n`arr.reshape(dimension)`\nSuppose we have an array of numbers. And we want to create another array from this array with different dimension. We can use the reshape method to reshape the array. Example:\n\n```python\n\narr = np.array([[2, 3, 4,5],[100, 19, 100, 7]])\nprint(arr.shape) # (2,4)\n\narr2 = arr.reshape([1, 8])\nprint(arr2.shape) # (1, 8)\n\n```\n\n\u003e When reshaping array all you need to take care of is the elements that are in the array meaning the number of elements in an array must be equal to the product of the dimension of the array. For example `1 * 8 = 8` elements which are the elements that are in the first array\n\n## Stacking arrays\n\nArrays can be stacked together to produce 1 array using two numpy methods\n\n- `np.vstack([arr1, arr2])` Stacks arrays vertically\n- `np.hstack([arr1, arr2])` Stacks array horizontally\n\n### horizontal Stack of arrays\n\n```python\n\narr1 = np.array([2,3,4,5,8])\narr2 = np.array([1,2,3,4,5])\nprint(np.hstack([arr1, arr2]))\n\n```\n\n### vertical stack of arrays\n\n```python\n\narr1 = np.array([2,3,4,5,8])\narr2 = np.array([1,2,3,4,5])\nprint(np.vstack([arr1, arr2]))\n\n```\n\n## Generating arrays from a file\n\n\u003e Suppose we have a file that has list of numbers seperated by a comma and we want to read this file and into a numpy array. The file name is 'data.txt'. This can be done as follows\n\n```python\n\ndata = np.genfromtxt('data.txt', delimiter=',')\ndata # the data in the file as float type\n\ndata = np.genfromtxt('data.txt', delimiter=',', dtype='int32')\ndata # the data in the file as int32\n\n# As integer datatype\n\ndata = np.genfromtxt('data.txt', delimiter=',', dtype='int32')\ndata\n\n# Or\n\ndata.astype('int8')\n\n```\n\n## Boolean Masking and Advanced indexing.\n\n\u003e Gives us the ability to mask the elements to boolean based on certain conditions\n\n\u003e Example: Checking if the element at that index is even or not.\n\n```python\n\ndata = np.genfromtxt('data.txt', delimiter=',', dtype='int32')\ndata % 2 == 0\n\n```\n\n\u003e Example: returning odd elements from the array\n\n```python\n\ndata = np.genfromtxt('data.txt', delimiter=',', dtype='int32')\ndata[data%2==1]\n\n```\n\n\u003e Example: Indexing multiple elements\n\u003e Let's say we want to index and return elements that are at index, `1, 2, 6, 9` and the last element in the array `data` where elements are `ODD` we can do it as follows:\n\n```python\ndata = np.genfromtxt('data.txt', delimiter=',', dtype='int32')\ndata[data%2==1][1, 2, 6, 9, -1]] # array([ 3, 5, 13, 19, 79])\n\n```\n\n## Where to find the documentation?\n\n- [Documentation](https://numpy.org/doc/)\n\n- [Documentation](https://numpy.org/doc/1.14/user/quickstart.html)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcrispengari%2Fnumpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcrispengari%2Fnumpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcrispengari%2Fnumpy/lists"}