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Edits in the NumPy Basics Tutorial #458

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Edits in the NumPy Basics Tutorial #458

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dc364a1
Making the introduction more welcoming!
himahuja Apr 6, 2024
7280059
Removed pre=requisites, as a in-depth tutorial should not take the st…
himahuja Apr 6, 2024
5ffe0a3
A small edit because package imports are never discussed.
himahuja Apr 6, 2024
b24dee6
Edits on Section 1.3
himahuja Apr 6, 2024
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180 changes: 134 additions & 46 deletions core/numpy/numpy-basics.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -14,44 +14,30 @@
"metadata": {},
"source": [
"## Overview\n",
"NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n",
"Welcome to your first Python library - NumPy! NumPy is the fundamental package for numerical operations with Python. It contains among other things:\n",
"\n",
"- a powerful N-dimensional array object\n",
"- sophisticated (broadcasting) functions\n",
"- useful linear algebra, Fourier transform, and random number capabilities\n",
"\n",
"The NumPy array object is the common interface for working with typed arrays of data across a wide-variety of scientific Python packages. NumPy also features a C-API, which enables interfacing existing Fortran/C/C++ libraries with Python and NumPy. In this notebook we will cover\n",
"Let's get you started with the basics! In this notebook we will cover\n",
"\n",
"1. Creating an `array`\n",
"1. Math and calculations with arrays\n",
"1. Inspecting an array with slicing and indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"\n",
"| Concepts | Importance | Notes |\n",
"| --- | --- | --- |\n",
"| [Python Quickstart](../../foundations/quickstart) | Necessary | Lists, indexing, slicing, math |\n",
"\n",
"* **Time to learn**: 35 minutes\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports\n",
"A common convention you might encounter is to rename `numpy` to `np` on import to shorten it for the many times we will be calling on `numpy` for functionality."
"You need to import packages into your notebook to be able to use them. A common convention you might encounter is to rename `numpy` to `np` on import to shorten it for the many times we will be calling on `numpy` for functionality."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -64,14 +50,25 @@
"source": [
"## Create an array of 'data'\n",
"\n",
"The NumPy array represents a *contiguous* block of memory, holding entries of a given type (and hence fixed size). The entries are laid out in memory according to the shape, or list of dimension sizes. Let's start by creating an array from a list of integers and taking a look at it,"
"The NumPy array represents a *contiguous* block of memory, holding entries of a given type (and hence fixed size). Let's start by creating an array from a list of integers and taking a look at it,"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3])\n",
"a"
Expand All @@ -81,23 +78,45 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can inspect the number of dimensions our array is organized along with `ndim`, and how long each of these dimensions are with `shape`"
"The entries of the array are laid out in memory according to the shape, or list of dimension sizes. We can inspect the number of dimensions of our array with `ndim`, and how long each of these dimensions are with `shape`. "
]
},
{
"cell_type": "code",
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"execution_count": 3,
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{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ndim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3,)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
Expand All @@ -106,14 +125,25 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"So our 1-dimensional array has a shape of `3` along that dimension! Finally we can check out the underlying type of our underlying data,"
"So our 1-dimensional array has a shape of `3` along that dimension! The dot `(.)` notation helps you access various `properties` of the NumPy array. Finally, we can check out the underlying type of our underlying data,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.dtype"
]
Expand All @@ -127,37 +157,82 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 2., 3.],\n",
" [4., 5., 6.]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ndim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 3)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('float64')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.dtype"
]
Expand Down Expand Up @@ -1025,7 +1100,20 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.2"
"version": "3.7.15"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"toc-autonumbering": false
},
Expand Down
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