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What is Matplotlib ?
Matplotlib is a data visualization package for Python with NumPy, the Python numerical mathematics extension. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. It provides an object-oriented API for embedding charts into applications utilising GUI toolkits such as Tkinter, wxPython, Qt, or GTK. A procedural "pylab" interface based on a state machine, similar to MATLAB, is also available, however its use is discouraged. Matplotlib is used by SciPy.
A User Interface (UI) and menu structure are developed when matplotlib is used to build a plot. The user interface allows you to personalise the plot, pan/zoom, and toggle other features.
Matplotlib 2.0.x is compatible with Python versions 2.7 through 3.10. Matplotlib 1.2 was the first version to support Python 3. Pyplot is a Matplotlib package that emulates the MATLAB interface. Matplotlib is intended to be as easy to use as MATLAB, but with the added benefit of being free and open-source.
Pandas is a data manipulation and analysis toolkit used by matplotlib. A Data Frame is an in-memory 2D data table object provided by Pandas. Pandas is not a mandatory requirement of matplotlib, unlike numpy.
Numpy is a scientific computing software. Matplotlib requires Numpy, which employs numpy functions for numerical data and multi-dimensional arrays.
What is matplotlib used for?
Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy. As such, it offers a viable open source alternative to MATLAB. Developers can also use matplotlib's APIs (Application Programming Interfaces) to embed plots in GUI applications.
Types of plots in Matplotlib we provide help are given below
Basic
Basic plot types, usually y versus x.
Plots of arrays and fields
Plotting for arrays of data Z(x, y) and fields U(x, y), V(x, y).
Statistics plots
Plots for statistical analysis.
Unstructured coordinates
Sometimes we collect data z at coordinates (x,y) and want to visualize as a contour. Instead of gridding the data and then using contour, we can use a triangulation algorithm and fill the triangles.
Sample examples of Matplotlib Assignment help as given below
How to Plot the coherence of two signals
An example showing how to plot the coherence of two signals.
import numpy as np
import matplotlib.pyplot as plt # importing matplotlib package
# Fixing random state for reproducibility
np.random.seed(19680801)
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t)) # white noise 1
nse2 = np.random.randn(len(t)) # white noise 2
# Two signals with a coherent part at 10Hz and a random part
s1 = np.sin(2 * np.pi * 10 * t) + nse1
s2 = np.sin(2 * np.pi * 10 * t) + nse2
fig, axs = plt.subplots(2, 1)
axs[0].plot(t, s1, t, s2)
axs[0].set_xlim(0, 2)
axs[0].set_xlabel('time')
axs[0].set_ylabel('s1 and s2')
axs[0].grid(True)
cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
axs[1].set_ylabel('coherence')
fig.tight_layout()
plt.show()How to dram Grouped bar chart with labels
This example shows a how to create a grouped bar chart and how to annotate bars with labels.
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
men_means = [20, 34, 30, 35, 27]
women_means = [25, 32, 34, 20, 25]
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, men_means, width, label='Men')
rects2 = ax.bar(x + width/2, women_means, width, label='Women')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
fig.tight_layout()
plt.show()
How to draw Nan Test
Example: simple line plots with NaNs inserted.
import numpy as np
import matplotlib.pyplot as plt
t = np.arange(0.0, 1.0 + 0.01, 0.01)
s = np.cos(2 * 2*np.pi * t)
t[41:60] = np.nan
plt.subplot(2, 1, 1)
plt.plot(t, s, '-', lw=2)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('A sine wave with a gap of NaNs between 0.4 and 0.6')
plt.grid(True)
plt.subplot(2, 1, 2)
t[0] = np.nan
t[-1] = np.nan
plt.plot(t, s, '-', lw=2)
plt.title('Also with NaN in first and last point')
plt.xlabel('time (s)')
plt.ylabel('more nans')
plt.grid(True)
plt.tight_layout()
plt.show()
Code explanation:
import matplotlib.pyplot as plt: syntax of including / importing Matplotlib package.
plt.xlabel('time (s)') : Set the x-axis label text.
plt.ylabel('voltage (mV)'): Set the y-axis label text.
plt.title('A sine wave with a gap of NaNs between 0.4 and 0.6'): Set the title of the plot.
plt.grid(True): Show grid in plot
plt.subplot(2, 1, 2): Subplot function is used to combined two plot on graph
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