Matplotlib Basics¶

Load Font¶

In [ ]:

!wget "https://github.com/web-fonts/ttf/raw/refs/heads/master/arial-geo-webfont.ttf"
# Arial GEO

!wget "https://github.com/web-fonts/ttf/raw/refs/heads/master/arial-geo-webfont.ttf" # Arial GEO

In [18]:

import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from pathlib import Path

# Path to the Arial font file
fpath = Path("./arial-geo-webfont.ttf")

# Load and register the Arial font
arial_font = fm.FontProperties(fname=fpath)
fm.fontManager.addfont(fpath)  # Add the font to Matplotlib's font manager

# Set the global font to Arial
plt.rcParams['font.family'] = arial_font.get_name()

import matplotlib.pyplot as plt import matplotlib.font_manager as fm from pathlib import Path # Path to the Arial font file fpath = Path("./arial-geo-webfont.ttf") # Load and register the Arial font arial_font = fm.FontProperties(fname=fpath) fm.fontManager.addfont(fpath) # Add the font to Matplotlib's font manager # Set the global font to Arial plt.rcParams['font.family'] = arial_font.get_name()

Imports¶

In [4]:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

from sklearn.datasets import load_breast_cancer, load_iris

import seaborn as sns

plt.style.use('Solarize_Light2')

import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.datasets import load_breast_cancer, load_iris import seaborn as sns plt.style.use('Solarize_Light2')

Import and Generate Data¶

In [8]:

# pandas
# M(0) = malignant, B(1) = benign
bc_X, bc_y = load_breast_cancer(return_X_y=True, as_frame=True)
bc_X.head(2)

# pandas # M(0) = malignant, B(1) = benign bc_X, bc_y = load_breast_cancer(return_X_y=True, as_frame=True) bc_X.head(2)

Out[8]:

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	17.99	10.38	122.8	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.9	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902

2 rows × 30 columns

In [9]:

# numpy
iris_X, iris_y = load_iris(return_X_y=True)
iris_X[:2, :]

# numpy iris_X, iris_y = load_iris(return_X_y=True) iris_X[:2, :]

Out[9]:

array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2]])

In [10]:

# generated
rand_X = np.random.random(50) * 100
rand_y = np.random.random(50) * 100

# generated rand_X = np.random.random(50) * 100 rand_y = np.random.random(50) * 100

Scatter Plot¶

In [17]:

sns.scatterplot(x=rand_X, y=rand_y)
plt.title("sns", fontdict={"fontsize": 20, "fontname": "Arial GEO"})
plt.xlabel("rand X")
plt.ylabel("rand y")
plt.yticks(rand_y[::10], [f"{y:.0f}k" for y in rand_y[::10]])

plt.show()

sns.scatterplot(x=rand_X, y=rand_y) plt.title("sns", fontdict={"fontsize": 20, "fontname": "Arial GEO"}) plt.xlabel("rand X") plt.ylabel("rand y") plt.yticks(rand_y[::10], [f"{y:.0f}k" for y in rand_y[::10]]) plt.show()

In [33]:

plt.scatter(x=rand_X, y=rand_y, c="purple", marker="^", s=50, alpha=0.5)
plt.title("plt")
plt.show()

plt.scatter(x=rand_X, y=rand_y, c="purple", marker="^", s=50, alpha=0.5) plt.title("plt") plt.show()

In [6]:

sns.scatterplot(x=iris_X[:, 2], y=iris_X[:, 3], hue=iris_y, palette="tab10", s=50)

sns.scatterplot(x=iris_X[:, 2], y=iris_X[:, 3], hue=iris_y, palette="tab10", s=50)

Out[6]:

<Axes: >

Line Chart¶

In [19]:

years = np.array([2001 + x for x in range(24)])
profitsSaj = np.random.randint(5_000, 20_000, (24, ))
profitsRez = np.random.randint(5_000, 20_000, (24, ))

years = np.array([2001 + x for x in range(24)]) profitsSaj = np.random.randint(5_000, 20_000, (24, )) profitsRez = np.random.randint(5_000, 20_000, (24, ))

In [21]:

# supported values are '-', '--', '-.', ':', 'None', ' ', '', 'solid', 'dashed', 'dashdot', 'dotted'
plt.plot(years, profitsSaj, c="g", lw=1, linestyle="-.", label="Sajjad")

plt.show()

# supported values are '-', '--', '-.', ':', 'None', ' ', '', 'solid', 'dashed', 'dashdot', 'dotted' plt.plot(years, profitsSaj, c="g", lw=1, linestyle="-.", label="Sajjad") plt.show()

Bar Plot¶

Different from Histogram (In bar charts there is a gab)

In [9]:

x = ["Python", "C#", "Rust", "RUby", "R", "Golang"]
y = [200, 50, 100, 70, 90, 120]

x = ["Python", "C#", "Rust", "RUby", "R", "Golang"] y = [200, 50, 100, 70, 90, 120]

In [10]:

# supported values are 'center', 'edge'
plt.bar(x, y, align="edge", width=0.5, edgecolor="black", lw=3)

# supported values are 'center', 'edge' plt.bar(x, y, align="edge", width=0.5, edgecolor="black", lw=3)

Out[10]:

<BarContainer object of 6 artists>

Histogram¶

In [11]:

ages = np.random.normal(20, 1, 1000)

ages = np.random.normal(20, 1, 1000)

In [12]:

# plt.hist(ages, bins=[18, 20, 23]) # you can specify bin ranges
plt.hist(ages, bins=100)
plt.show()

# plt.hist(ages, bins=[18, 20, 23]) # you can specify bin ranges plt.hist(ages, bins=100) plt.show()

In [13]:

plt.hist(ages, bins=20, cumulative=True)
plt.show()

plt.hist(ages, bins=20, cumulative=True) plt.show()

Pie Chart¶

In [14]:

unique, counts = np.unique(iris_y, return_counts=True)
plt.pie(x=counts, labels=["Type 1", "Type 2", "Type 3"], explode=[0.05, 0, 0], autopct="%.2f%%")
plt.show()

counts = bc_y.value_counts().to_numpy()
plt.pie(x=counts, labels=["benign", "malignant"], autopct="%.2f%%", pctdistance=1.3)
plt.show()

unique, counts = np.unique(iris_y, return_counts=True) plt.pie(x=counts, labels=["Type 1", "Type 2", "Type 3"], explode=[0.05, 0, 0], autopct="%.2f%%") plt.show() counts = bc_y.value_counts().to_numpy() plt.pie(x=counts, labels=["benign", "malignant"], autopct="%.2f%%", pctdistance=1.3) plt.show()

In [14]:

Box Plot¶

Statistical Plot

In [15]:

heights = np.random.normal(172, 8, 500)

heights = np.random.normal(172, 8, 500)

In [23]:

plt.boxplot(heights)
plt.show()
"""
Min is 150,
Max is 195,
Median is green line in square
circles are out layers
"""

plt.boxplot(heights) plt.show() """ Min is 150, Max is 195, Median is green line in square circles are out layers """

Out[23]:

'\nMin is 150,\nMax is 195,\nMedian is green line in square\ncircles are out layers\n'

Heatmap Plot¶

In [27]:

vegetables = np.array(["cucumber", "tomato", "lettuce", "asparagus",
              "potato", "wheat", "barley"])
farmers = np.array(["Farmer Joe", "Upland Bros.", "Smith Gardening",
           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."])

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

vegetables = np.array(["cucumber", "tomato", "lettuce", "asparagus", "potato", "wheat", "barley"]) farmers = np.array(["Farmer Joe", "Upland Bros.", "Smith Gardening", "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]) harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0], [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0], [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0], [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0], [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0], [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1], [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

In [30]:

sns.heatmap(harvest, xticklabels=farmers, yticklabels=vegetables, annot=True)

sns.heatmap(harvest, xticklabels=farmers, yticklabels=vegetables, annot=True)

Out[30]:

<Axes: >

Legend¶

In [24]:

plt.plot(years, profitsSaj, c="g", lw=1, linestyle="-.", label="Sajjad")
plt.plot(years, profitsRez, c="r", lw=1, linestyle="--", label="Reza")

# supported values are 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center'
plt.legend(loc="lower left") # To show the labels
plt.show()

plt.plot(years, profitsSaj, c="g", lw=1, linestyle="-.", label="Sajjad") plt.plot(years, profitsRez, c="r", lw=1, linestyle="--", label="Reza") # supported values are 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center' plt.legend(loc="lower left") # To show the labels plt.show()

SubPlots + Save¶

In [51]:

fig, axs= plt.subplots(nrows=2, ncols=2, figsize=(12, 8))

col_names = bc_X.columns
col_count = len(col_names)
col_index = 0
for i in range(2):
    for j in range(2):
        if col_index == col_count - 1:
          break
        axs[i][j].scatter(x=bc_X.iloc[:, col_index], y=bc_X.iloc[:, col_index + 1], c="purple", marker="o", s=50, alpha=0.5)
        axs[i][j].set_title("Breast Cancer")
        axs[i][j].set_xlabel(col_names[col_index])
        axs[i][j].set_ylabel(col_names[col_index + 1])
        col_index += 1

fig.suptitle("Breast Cancer", fontsize=30)
plt.tight_layout() # prevent overlap

plt.savefig("bc.png", dpi=300)

plt.show()

fig, axs= plt.subplots(nrows=2, ncols=2, figsize=(12, 8)) col_names = bc_X.columns col_count = len(col_names) col_index = 0 for i in range(2): for j in range(2): if col_index == col_count - 1: break axs[i][j].scatter(x=bc_X.iloc[:, col_index], y=bc_X.iloc[:, col_index + 1], c="purple", marker="o", s=50, alpha=0.5) axs[i][j].set_title("Breast Cancer") axs[i][j].set_xlabel(col_names[col_index]) axs[i][j].set_ylabel(col_names[col_index + 1]) col_index += 1 fig.suptitle("Breast Cancer", fontsize=30) plt.tight_layout() # prevent overlap plt.savefig("bc.png", dpi=300) plt.show()

Showing Saved Fig¶

In [52]:

# Imports PIL module
from PIL import Image

# open method used to open different extension image file
im = Image.open("bc.png")

# This method will show image in any image viewer
# im.show()
im

# Imports PIL module from PIL import Image # open method used to open different extension image file im = Image.open("bc.png") # This method will show image in any image viewer # im.show() im

Out[52]:

3D Plot¶

Scatter Plot¶

In [54]:

ax = plt.axes(projection="3d")

x = np.random.random(100)
y = np.random.random(100)
z = np.random.random(100)

ax.scatter(x, y, z)

ax = plt.axes(projection="3d") x = np.random.random(100) y = np.random.random(100) z = np.random.random(100) ax.scatter(x, y, z)

Out[54]:

<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7b8bacf24390>

Line Plot¶

In [56]:

ax = plt.axes(projection="3d")

x = np.arange(0, 50, 0.1)
y =  np.arange(0, 50, 0.1)
z = np.cos(x + y)

ax.plot(x, y, z)

ax = plt.axes(projection="3d") x = np.arange(0, 50, 0.1) y = np.arange(0, 50, 0.1) z = np.cos(x + y) ax.plot(x, y, z)

Out[56]:

[<mpl_toolkits.mplot3d.art3d.Line3D at 0x7b8bac705410>]

Surface Plot¶

In [67]:

ax = plt.axes(projection="3d")

x = np.arange(-5, 5, 0.1)
y =  np.arange(-5, 5, 0.1)

X, Y = np.meshgrid(x, y)

Z = np.sin(X) * np.cos(Y)

ax.plot_surface(X, Y, Z, cmap="Spectral")

# ax.view_init(azim = 0, elev = 90) # change view

ax = plt.axes(projection="3d") x = np.arange(-5, 5, 0.1) y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(x, y) Z = np.sin(X) * np.cos(Y) ax.plot_surface(X, Y, Z, cmap="Spectral") # ax.view_init(azim = 0, elev = 90) # change view

Out[67]:

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7b8ba7d41cd0>

Animation Plot¶

not working in ipynb

In [71]:

import random

heads_tails = [0, 0]

for _ in range(20):
    heads_tails[random.randint(0, 1)] += 10
    plt.bar(["Heads", "Tails"], heads_tails, color=["skyblue", "purple"])
    plt.pause(0.001)

plt.show()

import random heads_tails = [0, 0] for _ in range(20): heads_tails[random.randint(0, 1)] += 10 plt.bar(["Heads", "Tails"], heads_tails, color=["skyblue", "purple"]) plt.pause(0.001) plt.show()

In [ ]: