DEV Community: TechPlygrnd

Matplotlib Tutorial #7: Plot Color Customization

TechPlygrnd — Fri, 08 Sep 2023 11:00:00 +0000

Hi everyone, Today I will show you how to color your plot.

Marker Edge Color

You can use the keyword argument markeredgecolor or the shorter mec to set the color of the edge of the markers

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 5, 6])
ypoints = np.array([0, 1, 2, 3, 5, 6])

plt.plot(xpoints, ypoints, marker='o', markeredgecolor='r')
plt.show()

That will generate the following output

For the value of color, you can use basic color code such as:

b: blue
g: green
r: red
c: cyan
m: magenta
y: yellow
k: black
w: white

Other than basic value, you can use hex code and color codes from the color names supported by all browsers https://www.w3schools.com/colors/colors_names.asp

Marker Face Color

You can use the keyword argument markerfacecolor or the shorter mfc to set the color inside the edge of the markers

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 5, 6])
ypoints = np.array([0, 1, 2, 3, 5, 6])

plt.plot(xpoints, ypoints, marker='o', markerfacecolor='r')
plt.show()

Which will produces the following output

For color value you can refer to the available color value for the Marker Edge Color above.

Line Color

You can use the keyword argument color or the shorter c to set the color of the line

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.arange(10)

plt.plot(ypoints, color='r')
plt.show()

Which will produce the following output

That is how you can customize the color of your plot. Thank you for reading and have a nice day!

Matplotlib Tutorial #6: Plot Line Customization

TechPlygrnd — Thu, 07 Sep 2023 09:18:49 +0000

In previous blog I have explained how to customize a marker in Matplotlib plot, in today's blog I will show you how to customize a line in plot.

Line Style

You can use the keyword argument linestyle, or shorter ls, to change the style of the plotted line. linestyle can have values of:

- or solid to have a solid line, this is the default style of line of you don't specify it.
: or dotted to have a dotted line.
-- or dashed to have a dashed line.
-. or dashdot to have a dashdot line.
'' or None to have a plot with no line.

The following is the implementation of Matplotlib linestyle customization

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.arange(10)

plt.plot(ypoints, linestyle='--')
plt.show()

The following is the output of the above code

Line Width

You can use the keyword argument linewidth or the shorter lw to change the width of the line

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.arange(10)

plt.plot(ypoints, linewidth=20)
plt.show()

You will get the following output

There you go, that is how you can customize line in Matplotlib plot. Thank you for reading this blog, and have a good day!

Matplotlib Tutorial #5: Plot Marker Customization

TechPlygrnd — Wed, 06 Sep 2023 14:37:17 +0000

In this blog, I will show you how to make your plot more interesting with plot marker customization.

Marker Style

In Matplotlib, the marker refers to the points that are being plotted in the plot. The default plot method does not include a marker in the plot, therefore the output will look like this

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 5, 6])
ypoints = np.array([0, 1, 2, 3, 5, 6])

plt.plot(xpoints, ypoints)
plt.show()

That produces the following output

By passing optional argument marker with certain value, you can make mark each of your point like the following

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 5, 6])
ypoints = np.array([0, 1, 2, 3, 5, 6])

plt.plot(xpoints, ypoints, marker='o')
plt.show()

That produces the following output

As I mention previously, optional argument marker will hold a certain value, and you can reference that value from here

Marker Size

You can use the keyword argument markersize or the shorter version, ms to set the size of the markers

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 5, 6])
ypoints = np.array([0, 1, 2, 3, 5, 6])

plt.plot(xpoints, ypoints, marker='o', markersize=15)
plt.show()

Which produces the following output

There you go, that is how you can customize your marker in plot. Thank you for reading this blog and have a nice day!

Matplotlib Tutorial #4: Plot With Default X-Points

TechPlygrnd — Tue, 05 Sep 2023 12:57:55 +0000

You actually don't have to pass x-points as an argument in the plot method. If the value of x-points is a sequential increment starting from 0 for example 1, 2, 3, .. and you just don't care about the values, you just need to only pass the y-points.

Default X-Points

Let's say that you want to plot the following points: (0, 1), (1, 5), (2, 10), (3, 8), (4, 11). You can do that by typing the following code

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 1, 2, 3, 4])
ypoints = np.array([1, 5, 10, 8, 11])

plt.plot(xpoints, ypoints)
plt.show()

Which produces the following output

But notice that xpoints values are a sequential of 1 through 5 with the increment of 1 each step. In this case, you actually don't have to pass xpoints, therefore the code will look like this

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([1, 5, 10, 8, 11])

plt.plot(ypoints)
plt.show()

The output will be the same as the previous code output

There you go, that is how you can create a plot by only passing the y-points. Thank you for reading this article and have a great day!

Matplotlib Tutorial #3: Plot Without Line

TechPlygrnd — Mon, 04 Sep 2023 13:13:19 +0000

In the last blog we already created a plot, but perhaps you only want the figure to show points not with lines, how can you do that? In this blog, I will show you how to do just that.

Plot Without Line

In the Matplotlib plot, you can create a plot without the line. Let me show you how to do it.

First, let us create a normal plot with a line



import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])

plt.plot(xpoints, ypoints)
plt.show()

In order to make a plot without the line, you just need to pass o as the third argument to the plot method



import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])

plt.plot(xpoints, ypoints, 'o')
plt.show()

If you run the above code, you will get the following output result

That is it! that is how you can create a plot without the line. Thank you for reading this blog and have a nice day!

Matplotlib Tutorial #2: Create a Plot

TechPlygrnd — Sun, 03 Sep 2023 16:03:23 +0000

In this blog, I will show you how to create a simple plot in Matplotlib.

Plot

Matplotlib has provided a method called plot from pyplot. This method requires two arguments, which are the collection of x-points and y-points respectively. Let me show you how to use it.

First of all, you have to import the required packages

import matplotlib.pyplot as plt
import numpy as np

Next, create two arrays that define the points in the x and y coordinates

xpoints = np.array([0, 6])
ypoints = np.array([0, 255])

I have defined two points from the above code: (0, 0) and (6, 255).
Then, plot xpoints and ypoints into the Matplotlib

plt.plot(xpoints, ypoints)
plt.show()

The above code will show the following output

Congratulations, you have just created a plot in Matplotlib. Thank you for reading this blog, and have a great day!

Matplotlib Tutorial #1: Introduction

TechPlygrnd — Sun, 03 Sep 2023 15:46:20 +0000

Hi everyone, welcome my Matplotlib Tutorial Series! I would like to start this series with a little introduction to Matplotlib.

What is Matplotlib

According to the official documentation, Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

The following are the things that you can create with Matplotlob:

Prerequisites

To follow this series, you should know:

Basic of Python Programming Language
Basic of Mathematics
Basic of NumPy, you can see my NumPy tutorial series at https://dev.to/tech_plygrnd/series/24331

Tools

You can use Kaggle to follow along with this series. Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals. Provide many tools to run data science project, including a tool to run NumPy. Kaggle is free and you can sign up at https://www.kaggle.com/

Thank you for reading this post and I hope you have a nice day!

NumPy Tutorial #14: Random

TechPlygrnd — Thu, 31 Aug 2023 15:18:23 +0000

Hi everyone, in this blog I will show you how to generate value in NumPy.

Random

To generate value, you can use random from NumPy. It is very easy to use it. Let me show you how to do it.

First, you have to import random

from numpy import random

Then use randint method from random to generate random value. This method requires one argument, which is the maximum constraint that will be generated. In this example, I will set 70 as the maximum constraint, so that the value generated will in between 0 - 70

x = random.randint(70)

If you print x, you will get value around 0 - 70 as the output, in this case the following is my output result (your result might be different)

If you want to generate an array of values instead of an individual array, you can add the second optional argument size that requires the tuple of array size you want

arr = random.randint(70, size=(10))

If you print arr, you will get an array that have 10 random values. Below are my output result (your result might be different)

[58 41 55  0 13 21 62  6 38 43]

That is how you can generate value in NumPy. Thank you for reading this blog and have a nice day!

How to Generate Fake Data in Python With Faker

TechPlygrnd — Wed, 30 Aug 2023 15:55:40 +0000

Do you often feel annoyed when trying to make made up data for testing your Python application such as names and addresses? Well, worry no more, now you can generate those data automatically using Faker package.

Install Faker Dependency

To use the package, you have to install it first using the following command

pip install Faker

Create fake data

After installing the package into your machine, you can start to use it by importing it into your code

from faker import Faker

After that create an object from the Faker class

fake = Faker()

Then you can start to use it, for example in here I will try to generate a fake name

fake_name = fake.name()
print(fake_name)

From the output, you will see that the code will generate a fake name (your output might be different)

Tony Leon

I will use loop to generate 10 fake names

for _ in range(10):
  print(fake.name())

You can see from the output that 10 fake names are generated

Jessica Griffin
Richard Carson
Alyssa Wilson
Jonathan Chandler
Clinton Riley
Jeffrey Moore
Lisa Coffey
Pamela Swanson
Carl Jones
Todd Guzman
Timothy Kim
Collin Dillon
Lisa Hall
Adam Robinson
Michael Richardson
Brandy Webb MD
Anthony Tate
Tyler Alvarado
Alyssa Lee
Heather Robbins

Not only generating a fake name, you can also generate a fake address and a fake text.

I will generate a fake address

fake_address = fake.address()
print(fake_address)

The output shows a fake address

0084 Patel Crossing
New Eric, CT 79853

Below code will generate fake text

fake_text = fake.text()
print(fake_text)

Below are the output of the generated fake text

Into side begin compare affect. Like fall generation.
Consider TV sell something method most.
Message personal fund attack sea social.

Providers

Provider refer to a faker generator. You can use it like this

from faker import Faker
from faker.providers import internet

fake = Faker()
fake.add_provider(internet)

for _ in range(10):
  print(fake.ipv4_private())

You can see the list of providers provided by the faker package built-in by this link https://faker.readthedocs.io/en/stable/providers.html#standard-providers and also the providers that created by the community https://faker.readthedocs.io/en/stable/communityproviders.html#community-providers

Localization

You can set the localization according to your preference. For example, if you want to generate fake Japanese names, you can set it by the following code

from faker import Faker

fake = Faker('ja_JP')

for _ in range(10):
  print(fake.name())

Below are the result of the code

鈴木 幹
藤井 さゆり
鈴木 幹
渡辺 結衣
長谷川 洋介
山崎 浩
山本 和也
渡辺 結衣
長谷川 美加子
中村 智也

Faker v3.0.0 supports multiple locales, so if you want to generate the combination of Japanese and US names

from faker import Faker

fake = Faker(['en_US', 'ja_JP'])

for _ in range(10):
  print(fake.name())

The output is the following

Jerry Chavez
伊藤 真綾
Phillip Williams
Elizabeth Wood
Michelle Villarreal
森 康弘
Melanie James
Mr. Andrew Glenn
山田 結衣
田中 七夏

You can read the following page for the supported locales https://faker.readthedocs.io/en/master/locales.html

There you go, that is how to can utilize Faker package in Python to generate fake data. I hope this blog helps you, see you next time!

NumPy Tutorial #13: Array Filtering

TechPlygrnd — Wed, 30 Aug 2023 15:16:58 +0000

In this blog, I will show you how to filter an array in NumPy.

Filter

NumPy has provided us a functionality to perform a filtration. Let me show you how to use it.

First, let us create an array

import numpy as np

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

Then, perform filtration. In this example, I want to filter out the odd numbers from the array. To achieve this, First I will create an expression of even number as the following

even_arr_index = arr % 2 == 0

If you print the even_arr_index variable, you will get the following output

[False  True False  True False  True False  True False  True]

Then you can assign even_arr_index to the arr to perform the filtration

even_arr = arr[even_arr_index]

If you print even_arr, you will get the following result

array([ 2,  4,  6,  8, 10])

There you go, that is how you can filter a NumPy array. Thank you for reading this blog and see you next time!

NumPy Tutorial #12: Array Sorting

TechPlygrnd — Tue, 29 Aug 2023 10:43:16 +0000

In this blog, I will show you how to sort an array in NumPy.

Sort

NumPy has provided a method called sort to sort an array. This method requires one argument which is the original array that you want to sort. Let see how that method works in code.

Let us create an array

import numpy as np

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

Then, use the method sort to sort the unsorted_arr

sorted_arr = np.sort(unsorted_arr)

Finally, print the sorted_arr to see the output

print(sorted_arr)

You will see the following output

[1 2 3 4 5 6 7 7 8 9]

That is how you can sort an array in NumPy. Thank you for reading, and have a nice day!

NumPy Tutorial #11: Array Search

TechPlygrnd — Mon, 28 Aug 2023 12:48:30 +0000

In this blog, I will show you how to search a specific item in an array.

Search

NumPy has a method called where to find an item in array. This method requires an expression for its parameter. Let me show you how to implement it.

First, let us create an array

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 2])

Then, use method where to find the index of an item in array and store it in a variable x

x = np.where(arr == 2)

Let us print the value of x to see its output

print(x)

The following is the output

(array([1, 5]),)

From the output above, you can see that 2 is occurs two times in index 1 and 5.

Congratulations, that is how you can search an item in NumPy array. Thank you for reading, and have a nice day!