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Matplotlib Articles
Page 2 of 91
How To Visualize Sparse Matrix in Python using Matplotlib?
Sparse matrices are a specialized type of matrix that contain mostly zero values. These matrices are commonly encountered in applications such as graph theory, machine learning, and network analysis. Visualizing sparse matrices can provide valuable insights into the distribution and patterns of non-zero values. In this article, we will understand how to visualize sparse matrices in Python using the popular data visualization library, Matplotlib. Understanding Sparse Matrices A sparse matrix is a matrix in which most of the elements are zero. These matrices are typically large and inefficient to store in memory if all the zeros are explicitly represented. ...
Read MoreDraw a Unstructured Triangular Grid as Lines or Markers in Python using Matplotlib
Python is a popularly used programming language. It offers a wide range of tools and libraries which can be used for solving different problems, one of them is Matplotlib. This library provides various functions for data visualization and creating different plots. In this article, we will be using Matplotlib for drawing an unstructured triangular grid as liners or markers in python. What is Matplotlib and How to Install it? Matplotlib is one of the libraries of python. This library is very strong tool for serving the purpose of plotting graphs for visualizing data. It has a module named “pyplot” ...
Read MoreHow to Calculate and Plot the Derivative of a Function Using Python – Matplotlib?
The Derivative of a function is one of the key concepts used in calculus. It is a measure of how much the function changes as we change the output. Whereas Matplotlib is a plotting library for python, since it does not provide a direct method to calculate the derivative of a function you need to use NumPy, which is also one of the python libraries and you can use it to calculate the derivative of a function and Matplotlib for visualizing the results. In this article, we will be calculating the derivative of a function using the NumPy ...
Read MoreHow to Change the Line Width of a Graph Plot in Matplotlib?
Matplotlib one of the libraries of python, which plays an important role in beautifying plots and making the data analysis and data visualization an easier task. You can use Matplotlib for experimenting, by using different options available in it and creating a more appealing, informative plot. One common customization in Matplotlib is changing the line width of a graph plot. Since, line width controls the thickness of the lines, which are used in the plots at various points such as in connecting the plot points, etc. In this article, we will be learning how to change the line ...
Read MoreHow to change the color of the axis, ticks and labels for a plot in matplotlib?
We can change the color of the axis, ticks and labels, using ax.spines['left'].set_color('red') and ax.spines['top'].set_color('red') statements. To change the color of the axis, ticks, and labels for a plot in matplotlib, we can take the following steps −Create a new figure, or activate an existing figure, using plt.figure().Add an axis to the figure as part of a subplot arrangement, using plt.add_subplot(xyz) where x is nrows, y is ncols and z is the index. Here taking x = 1(rows), y = 2(columns) and z = 1(position).Set up X-axis and Y-axis labels using set_xlabel and set_ylabel method for creating ax using add_subplot().To ...
Read MoreData Pre-Processing with Sklearn using Standard and Minmax scaler
Introduction Data pre-processing is required for producing trustworthy analytical results. Data preparation includes eliminating duplicates, identifying and fixing outliers, normalizing measurements, and filing away categories of information. Popular for its ability to scale features, handle missing data, and encode categorical variables, the Python-based Sklearn toolkit is an essential resource for pre-processing data. With Sklearn, preprocessing data is a breeze, and you have access to trustworthy methodologies for effective data analysis. Data Pre-Processing Techniques Standard Scaling Data can be transformed using standard scaling so that it is normally distributed around zero and one. It ensures that everything is uniform in size. This ...
Read MoreHow to write text in subscript in the axis labels and the legend using Matplotlib?
To write text in subscript in the axis labels and the legend, we can take the following steps −Create x and y data points using NumPy.Plot x and y data points with a super subscript texts label.Use xlabel and ylabel with subscripts in the text.Use the legend() method to place a legend in the plot.Adjust the padding between and around subplots.To display the figure, use the show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 1000) y = np.exp(x) plt.plot(x, y, label=r'$e^x$', c="red", lw=2) plt.xlabel("$X_{axis}$") plt.ylabel("$Y_{axis}$") plt.legend(loc='upper left') plt.show()Output
Read MoreHow to clear the memory completely of all Matplotlib plots?
Using the following methods, we can clear the memory occupied by Matplotlib plots.plt.figure() - Create a new figure or activate an existing figure.plt.figure().close() - Close a figure window.close() by itself closes the current figureclose(h), where h is a Figure instance, closes that figureclose(num) closes the figure number, numclose(name), where name is a string, closes figure with that labelclose('all') closes all the figure windowsplt.figure().clear() - It is the same as clf.plt.cla() - Clear the current axes.plt.clf() - Clear the current figure.Examplefrom matplotlib import pyplot as plt fig = plt.figure() plt.figure().clear() plt.close() plt.cla() plt.clf()OutputWhen we execute the code, it will clear all the plots from ...
Read MorePlot a bar using matplotlib using a dictionary
First, we can define our dictionary and then, convert that dictionary into keys and values. Finally, we can use the data to plot a bar chart.StepsCreate a dictionary, i.e., data, where milk and water are the keys.Get the list of keys of the dictionary.Get the list of values of the dictionary.Plot the bar using plt.bar().Using plt.show(), show the figure.Exampleimport matplotlib.pyplot as plt data = {'milk': 60, 'water': 10} names = list(data.keys()) values = list(data.values()) plt.bar(range(len(data)), values, tick_label=names) plt.show()Output
Read MorePlot multiple boxplots in one graph in Pandas or Matplotlib
To plot multiple boxplots in one graph in Pandas or Matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Make a Pandas data frame with two columns.Plot the data frame using plot() method, with kind='boxplot'.To display the figure, use show() method.Exampleimport pandas as pd import numpy as np from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Pandas dataframe data = pd.DataFrame({"Box1": np.random.rand(10), "Box2": np.random.rand(10)}) # Plot the dataframe ax = data[['Box1', 'Box2']].plot(kind='box', title='boxplot') # Display ...
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