Matplot in python

What is Matplot?

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is widely used in data science, machine learning, and general scientific computing for plotting data in various formats, including line plots, bar charts, histograms, scatter plots, and more. Matplotlib is particularly powerful because it can produce publication-quality figures in various formats and interactive environments across platforms.

Key Features of Matplotlib

Versatility:

• Matplotlib supports a wide range of plots and charts, including line plots, scatter plots, bar charts, histograms, pie charts, and 3D plots.
• It can be used in different environments like Jupyter notebooks, Python scripts, web application servers, and more.

Customization:

• You can customize almost every aspect of your plots, such as labels, legends, titles, tick marks, colors, line styles, and more.
• Matplotlib provides a vast number of parameters that allow you to fine-tune the appearance of your plots.

Interactivity:

• With Matplotlib, you can create interactive plots that allow zooming, panning, and updating in real-time.
• It integrates with IPython and Jupyter notebooks, making it easy to visualize data interactively in these environments.

Integration with NumPy:

• Matplotlib works seamlessly with NumPy, the foundational package for numerical computing in Python, making it easy to plot mathematical functions and numerical data.

Support for Multiple Backends:

• Matplotlib can output plots to several formats, including PNG, PDF, SVG, EPS, and PGF, making it easy to integrate into different workflows.
• It supports various GUI toolkits such as Tkinter, wxPython, and PyQt, which allows the creation of interactive applications.

Basic Components of Matplotlib

Figures and Axes:

• Figure: The entire window or page where the plot is drawn. It can contain multiple plots (axes).
• Axes: The area where the data is plotted. A figure can have multiple axes, which allows for multiple plots within the same figure.

Plot Elements:

• Lines: Represented by Line2D objects, used in line plots.
• Markers: Symbols used to mark points in scatter plots.
• Text: Titles, axis labels, and annotations can be added to plots.

Plot Styles:

• Matplotlib provides a variety of built-in styles that can be applied to your plots using plt.style.use('style_name').
• You can also create your custom styles to standardize the appearance of plots.

Matplot is a library for creating visualization with python.

How do plot lines bars and markers?

The grouped bar, stacked bar and horizontal bar chart examples and how to use bar_label

import matplotlib.pyplot as plt

import numpy as np

N=5

menMeans=(20, 35, 30, 35, -27)

womenMeans = (25, 32, 34, 20, -25)

menStd = (2, 3, 4, 1, 2)

womenStd = (3, 5, 2, 3, 3)

ind = np.arange(N) # the x locations for the groups

width = 0.35  # the width of the bars: can also be len(x) sequence

ind

array([0, 1, 2, 3, 4])

fig, ax = plt.subplots()

p1=ax.bar(ind, menMeans, width, yerr=menStd, label='Men')

p2=ax.bar(ind,womenMeans,width,

          bottom=womenStd,label='Women')

ax.axhline(0,color='gray',linewidth=0.8)

ax.set_ylabel('Scores')

ax.set_title('Scores by group and gender')

ax.set_xticks(ind)

ax.set_xticklabels(('G1','G2','G3','G4','G5'))

ax.legend()

plt.show()

Horizontal bar chart

# Fixing random state for reproducibility

np.random.seed(19680801)

# Example data

people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')

y_pos = np.arange(len(people))

performance = 3 + 10 * np.random.rand(len(people))

error = np.random.rand(len(people))

fig, ax = plt.subplots()

hbars = ax.barh(y_pos, performance, xerr=error, align='center')

ax.set_yticks(y_pos)

ax.set_yticklabels(people)

ax.invert_yaxis()  # labels read top-to-bottom

ax.set_xlabel('Performance')

ax.set_title('How fast do you want to go today?')

# Label with specially formatted floats

ax.bar_label(hbars, fmt='%.2f')

ax.set_xlim(right=15)  # adjust xlim to fit labels

plt.show()

%matplotlib inline

import matplotlib as mpl

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d.axes3d import Axes3D

 import numpy as np

 import sympy

x=np.linspace(-5,2,100)

y1=x**3+5*x**2+10

y2 = 3*x**2 + 10*x

y3 = 6*x + 10

fig,ax=plt.subplots()

ax.plot(x,y1,color="blue",label="y(x)")

ax.plot(x,y2,color="red",label="y'(x)")

ax.plot(x, y3, color="green", label="y”(x)")

ax.set_xlabel("x")

ax.set_ylabel("y")

ax.legend()

import matplotlib as mpl

import matplotlib.pyplot as plt

%matplotlib inline

%config InlineBackend.figure_format='svg'

fig=plt.figure(figsize=(8,2.5),facecolor="#f1f1f1")

 left, bottom, width, height = 0.1, 0.1, 0.8, 0.8

ax=fig.add_axes((left,bottom,width,height),facecolor="#e1e1e1")

x=np.linspace(-2,2,1000)

y1=np.cos(40*x)

y2 = np.exp(-x**2)

ax.plot(x, y1 * y2)

ax.plot(x, y2, 'g')

ax.plot(x, -y2, 'g')

ax.set_xlabel("x")

ax.set_ylabel("y")

fig.savefig("graph.png", dpi=100, facecolor="#f1f1f1")

fig, axes = plt.subplots(nrows=3, ncols=2)

x = np.linspace(-5, 5, 5)

y = np.ones_like(x)

def axes_settings(fig,ax,title,ymax):

   ax.set_xticks([])

   ax.set_yticks([])

   ax.set_ylim(0, ymax+1)

   ax.set_title(title)

fig, axes = plt.subplots(1, 4, figsize=(16,3))

More MATPLOT WORK

Python