test

gistfile1.txt

{
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metadata: {
colab: {
name: "01-numpy-tutorial.ipynb",
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collapsed_sections: [ ],
include_colab_link: true
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cells: [
{
cell_type: "markdown",
metadata: {
id: "view-in-github",
colab_type: "text"
},
source: [
"<a href="https://colab.research.google.com/github/foobar8675/numpy-tutorials/blob/master/01_numpy_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>"
]
},
{
cell_type: "markdown",
metadata: {
id: "LJWL_Bbzs92k",
colab_type: "text"
},
source: [
"# A practical introduction to Numpy, Matplotlib and Pandas - Part 1 ",
" ",
"![alt text](https://miro.medium.com/max/950/1*00pL0zLnfI7y8d5G1aQrHA.jpeg) ",
" ",
"This tutorial is designed to introduce how to do operations in numpy in a practical way. It primarily uses images an an example since they are easy to visualize, but applies to all data in `numpy` as well as your future data engineering and machine learning endeavors! ",
" ",
"A few prerequisites: ",
" ",
" ",
"* An understanding of [RGB](https://en.wikipedia.org/wiki/RGB_color_model). ",
"* Python programming experience. ",
"* [jupyter notebooks](https://jupyter.org/) experience."
]
},
{
cell_type: "markdown",
metadata: {
id: "2afQUDp5s8rL",
colab_type: "text"
},
source: [
"Now that that's out of the way, let's introduce the designer ... ",
" ",
"![alt text](https://www.mediabistro.com/wp-content/uploads/2016/04/what-does-a-graphic-designer-do_860.jpg) ",
" ",
"For this exercise, imagine your designer came to you and said in her menacing voice, "I have a prototyped a photoshop file file where I add a background, border, and color manipulation. Now I have 30,000 images I need to apply this to and it also needs to happen every time a user uploads an image to the website" ",
" ",
"Wow, that's a lot. But we can take it step by step and learn how to use `numpy` operations along the way! ",
" ",
"We're going start by creating a black 2x2 1 byte RGB image, which in other words is a 2x2x3 matrix, keeping in mind - ",
" ",
" ",
"* there will be 2 rows and 2 columns to this matrix (2x2) ",
"* there will be 3 channels RGB for this matrix (3) ",
"* black in RGB has a value of 0"
]
},
{
cell_type: "code",
metadata: {
id: "beXud7vCCrnN",
colab_type: "code",
colab: { }
},
source: [
"import numpy as np"
],
execution_count: 0,
outputs: [ ]
},
{
cell_type: "code",
metadata: {
id: "7P1LOCj4pmDO",
colab_type: "code",
outputId: "52b49a36-33ea-4256-db42-bb70cba073e1",
colab: {
base_uri: "https://localhost:8080/",
height: 102
}
},
source: [
"img = np.zeros([2,2,3],dtype=np.uint8) ",
"img"
],
execution_count: 0,
outputs: [
{
output_type: "execute_result",
data: {
text/plain: [
"array([[[0, 0, 0], ",
" [0, 0, 0]], ",
" ",
" [[0, 0, 0], ",
" [0, 0, 0]]], dtype=uint8)"
]
},
metadata: {
tags: [ ]
},
execution_count: 27
}
]
},
{
cell_type: "markdown",
metadata: {
id: "zSHEwwfq0ntV",
colab_type: "text"
},
source: [
"And let's take a look at this 2x2 black image using `matplotlib`. For now, don't worry about the specifics of `matplotlib`, we'll dive deeper later on. Just know we can use it to view images and graphs."
]
},
{
cell_type: "code",
metadata: {
id: "quMM-7cWzljA",
colab_type: "code",
colab: { }
},
source: [
"%matplotlib inline ",
"import matplotlib.pyplot as plt ",
"import matplotlib.image as mpimg"
],
execution_count: 0,
outputs: [ ]
},
{
cell_type: "code",
metadata: {
id: "63OD4nSYztFQ",
colab_type: "code",
outputId: "b83b4bea-55a3-4c6a-b4f4-02f0e82faa48",
colab: {
base_uri: "https://localhost:8080/",
height: 248
}
},
source: [
"plt.axis('off') ",
"imgplot = plt.imshow(img)"
],
execution_count: 0,
outputs: [
{
output_type: "display_data",
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text/plain: [
"<Figure size 432x288 with 1 Axes>"
]
},
metadata: {
tags: [ ]
}
}
]
},
{
cell_type: "markdown",
metadata: {
id: "9lk3cB8X0491",
colab_type: "text"
},
source: [
"Now in photoshop, the first thing our designer wants is to start with a solid red color. If we revisit the shape of our image, we see we have 3 2x2 images, where each of the three images represents one channel."
]
},
{
cell_type: "code",
metadata: {
id: "QBapW6CoFF-l",
colab_type: "code",
outputId: "863544da-7931-4967-bb82-e0f701a27e75",
colab: {
base_uri: "https://localhost:8080/",
height: 34
}
},
source: [
"img.shape"
],
execution_count: 0,
outputs: [
{
output_type: "execute_result",
data: {
text/plain: [
"(2, 2, 3)"
]
},
metadata: {
tags: [ ]
},
execution_count: 30
}
]
},
{
cell_type: "markdown",
metadata: {
id: "nAhuwy9XFKCf",
colab_type: "text"
},
source: [
"So if we want a red image, we should be able to change the first channel to `255` while leaving the others `0`. Let's try it out."
]
},
{
cell_type: "code",
metadata: {
id: "OLLKLWNGxkcn",
colab_type: "code",
outputId: "de162fae-6768-47b1-8bfa-1fa9cd8565e5",
colab: {
base_uri: "https://localhost:8080/",
height: 248
}
},
source: [
"img[:,:,0] = 255 ",
"plt.axis('off') ",
"imgplot = plt.imshow(img)"
],
execution_count: 0,
outputs: [
{
output_type: "display_data",
data: {
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text/plain: [
"<Figure size 432x288 with 1 Axes>"
]
},
metadata: {
tags: [ ]
}
}
]
},
{
cell_type: "markdown",
metadata: {
id: "rPyvcjri1ygb",
colab_type: "text"
},
source: [
"Oh, but our designer just came to us and said she wanted to start with a black image instead. Well, no problem, we could make a white image by changing all the channels to `255` "
]
},
{
cell_type: "code",
metadata: {
id: "Q_Pn1BIk1vlz",
colab_type: "code",
outputId: "6829d81c-5f64-4fb6-e199-3d07ea67528b",
colab: {
base_uri: "https://localhost:8080/",
height: 248
}
},
source: [
"img[:,:,:] = 0 ",
"plt.axis('off') ",
"imgplot = plt.imshow(img)"
],
execution_count: 0,
outputs: [
{
output_type: "display_data",
data: {
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text/plain: [
"<Figure size 432x288 with 1 Axes>"
]
},
metadata: {
tags: [ ]
}
}
]
},
{
cell_type: "markdown",
metadata: {
id: "m-XAJQna1nzo",
colab_type: "text"
},
source: [
"But now let's say she changes her mind, as designers often do. She says she wants an addition on the red channel of `100`. How do we do that? ",
" ",
"We can tell `numpy` to subtract a value, `100` on each element of the matrix, or each pixel on the channel of the image with `np.add`"
]
},
{
cell_type: "code",
metadata: {
id: "l8nAWIvWpuPM",
colab_type: "code",
outputId: "7c13c05e-3fbe-4db3-e3fa-5bae8db1e8aa",
colab: {
base_uri: "https://localhost:8080/",
height: 102
}
},
source: [
"red_channel_values = np.add(img[:,:, 0], 100) ",
"img[:,:,0] = red_channel_values ",
"img"
],
execution_count: 0,
outputs: [
{
output_type: "execute_result",
data: {
text/plain: [
"array([[[100, 0, 0], ",
" [100, 0, 0]], ",
" ",
" [[100, 0, 0], ",
" [100, 0, 0]]], dtype=uint8)"
]
},
metadata: {
tags: [ ]
},
execution_count: 33
}
]
},
{
cell_type: "code",
metadata: {
id: "oHhWH-egpyqD",
colab_type: "code",
outputId: "568aea24-9581-4bd0-e456-c843e5b6036a",
colab: {
base_uri: "https://localhost:8080/",
height: 269
}
},
source: [
"imgplot = plt.imshow(img)"
],
execution_count: 0,
outputs: [
{
output_type: "display_data",
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text/plain: [
"<Figure size 432x288 with 1 Axes>"
]
},
metadata: {
tags: [ ]
}
}
]
},
{
cell_type: "markdown",
metadata: {
id: "R_r9mcE-3pCi",
colab_type: "text"
},
source: [
"Now let's say she now wants the top half of the image to have an addition of 100 to the red channel. We can access the top row of the image by looking at the dimensions from before `[2,2,3]` and thinking of it in terms of `[rows, columns, channels]` So if we want to change the top row of the image, we need to access row `0` (top) which will leave row `1` (bottom) untouched."
]
},
{
cell_type: "code",
metadata: {
id: "Pt41-F8dp4aD",
colab_type: "code",
outputId: "dbcbbc25-65b1-4d32-b64b-470e4f67156f",
colab: {
base_uri: "https://localhost:8080/",
height: 102
}
},
source: [
"img[0,:,0] = np.add(img[0,:,0], 100) ",
"img"
],
execution_count: 0,
outputs: [
{
output_type: "execute_result",
data: {
text/plain: [
"array([[[200, 0, 0], ",
" [200, 0, 0]], ",
" ",
" [[100, 0, 0], ",
" [100, 0, 0]]], dtype=uint8)"
]
},
metadata: {
tags: [ ]
},
execution_count: 39
}
]
},
{
cell_type: "code",
metadata: {
id: "S1eaud_2p84D",
colab_type: "code",
outputId: "d8d239a5-a71e-4946-f085-1489a1be3398",
colab: {
base_uri: "https://localhost:8080/",
height: 269
}
},
source: [
"imgplot = plt.imshow(img)"
],
execution_count: 0,
outputs: [
{
output_type: "display_data",
data: {
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text/plain: [
"<Figure size 432x288 with 1 Axes>"
]
},
metadata: {
tags: [ ]
}
}
]
},
{
cell_type: "markdown",
metadata: {
id: "1WWipip65tGb",
colab_type: "text"
},
source: [
"So finally, she says she loves it, but just wants to increase the intensity of all values by 1. There are 2 ways to do this and we'll try both. ",
" ",
" ",
"* The first way is to create a matrix of the same shape and simply add it ",
"* The second way is to add the value `1` ",
" ",
"Then finally, we check the equality using `np.equal`"
]
},
{
cell_type: "code",
metadata: {
id: "ENZyEV0H4HmN",
colab_type: "code",
outputId: "55632633-ce17-4f8d-acad-cd884aa6471b",
colab: {
base_uri: "https://localhost:8080/",
height: 102
}
},
source: [
"one_matrix = np.ones([2,2,3],dtype=np.uint8) ",
"img_one_matrix = img + one_matrix ",
" ",
"img_one_scaler = img + 1 ",
"img_one_scaler ",
" ",
"np.equal(img_one_matrix, img_one_scaler)"
],
execution_count: 0,
outputs: [
{
output_type: "execute_result",
data: {
text/plain: [
"array([[[ True, True, True], ",
" [ True, True, True]], ",
" ",
" [[ True, True, True], ",
" [ True, True, True]]])"
]
},
metadata: {
tags: [ ]
},
execution_count: 46
}
]
},
{
cell_type: "markdown",
metadata: {
id: "8KtDqe93-yS-",
colab_type: "text"
},
source: [
"## Troubleshooting Advice ",
" ",
"Now let's say we have a typo and have a typo and try to add two matrixes of different shapes. When that happens, we have what is called a shape mismatch. If something like that happens, you need to print the `.shape` of each matrix and find out what is different. Try it for yourself. That is the best way to learn. ",
" "
]
}
]
}