Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Matplotlib Articles
Page 65 of 91
How can I place a table on a plot in Matplotlib?
StepsUsing the subplots() method, create a figure and a set of subplots with figure size (7, 7).Create a data frame with two keys, time and speed.Get the size of the array.Add a table to the current axis using the table method.Shrink the font size until the text fits into the cell width.Set the font size in the table.Set the face color, edge color, and text color by iterating the matplotlib table.Save and display the figure.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() df = pd.DataFrame(dict(time=list(pd.date_range("2021-01-01 12:00:00", periods=10)), ...
Read MoreHow to convert a .wav file to a spectrogram in Python3?
To convert a .wav file to a spectrogram in python3, we can take the following steps −Load a .wav file from local machine.Compute a spectrogram with consecutive Fourier transforms using spectrogram() method.Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.Use imshow() method with spectrogram.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from scipy import signal from scipy.io import wavfile plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True sample_rate, samples = wavfile.read('test.wav') frequencies, times, spectrogram = signal.spectrogram(samples, sample_rate) plt.pcolormesh(times, frequencies, spectrogram, shading='flat') plt.imshow(spectrogram) plt.show()Output
Read MoreDraw axis lines or the origin for Matplotlib contour plot.
To draw axis lines or the origin for matplotlib contour plot, we can use contourf(), axhline() y=0 and axvline() x=0.Create data points for x, y, and z using numpy.To set the axes properties, we can use plt.axis('off') method.Use contourf() method with x, y, and z data points.Plot x=0 and y=0 lines with red color.To display the figure, use 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(-1.0, 1.0, 10) x, y = np.meshgrid(x, x) z = -np.hypot(x, y) plt.axis('off') plt.contourf(x, y, z, 10) plt.axhline(0, color='red') plt.axvline(0, color='red') plt.show()Output
Read MoreHow to plot a line graph from histogram data in Matplotlib?
To plot a line graph from histogram data in matplotlib, we use numpy histogram method to compute the histogram of a set of data.StepsAdd a subplot to the current figure, nrows=2, ncols=1 and index=1.Use numpy histogram method to get the histogram of a set of data.Plot the histogram using hist() method with edgecolor=black.At index 2, use the computed data (from numpy histogram). To plot them, we can use plot() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True plt.subplot(211) data = np.array(np.random.rand(100)) y, binEdges = np.histogram(data, bins=100) plt.hist(data, bins=100, edgecolor='black') ...
Read MoreHow to plot a multi-colored line, like a rainbow using Matplotlib?
To plot multi-colored lines, like a rainbow, we can create a list of seven rainbow colors (VIBGYOR).StepsCreate x for data points using numpy.Create a list of colors (rainbow VIBGYOR).Iterate in the range of colors list length.Plot lines with x and y(x+i/20) using plot() method, with marker=o, linewidth=7 and colors[i] where i is the index.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-1, 1, 10) colors = ["red", "orange", "yellow", "green", "blue", "indigo", "violet"] for i in range(len(colors)): plt.plot(x, x+i/20, c=colors[i], lw=7, marker='o') plt.show()Output
Read MoreHow to remove the label on the left side in matplotlib.pyplot pie charts?
To remove the label on the left side in a matplotlib pie chart, we can take the following steps −Create lists of hours, activities, and colors.Plot a pie chart using pie() method.To hide the label on the left side in matplotlib, we can use plt.ylabel("") with ablank string.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True hours = [8, 1, 11, 4] activities = ['sleeping', 'exercise', 'studying', 'working'] colors = ["grey", "green", "orange", "blue"] plt.pie(hours, labels=activities, colors=colors, autopct="%.2f") plt.ylabel("") plt.show()Output
Read MoreHow can I display text over columns in a bar chart in Matplotlib?
To display text over columns in a bar chart, we can use text() method so that we could place text at a specific location (x and y) of the bars column.StepsCreate lists for x, y and percentage.Make a bar plot using bar() method.Iterate zipped x, y and percentage to place text for the bars column.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = ['A', 'B', 'C', 'D', 'E'] y = [1, 3, 2, 0, 4] percentage = [10, 30, 20, 0, 40] ax = plt.bar(x, y) for x, y, p in zip(x, y, percentage): ...
Read MoreHow to handle an asymptote/discontinuity with Matplotlib?
To handle an asymptote/discontinuity with matplotlib, we can take the following steps −Create x and y data points using numpy.Turn off the axes plot.Plot the line with x and y data points.Add a horizontal line across the axis, x=0.Add a vertical line across the axis, y=0.Place legend for the curve y=1/x.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-1, 1, 100) y = 1 / x plt.axis('off') plt.plot(x, y, label='y=1/x') plt.axhline(y=0, c='red') plt.axvline(x=0, c='red') plt.legend(loc='upper left') plt.show()Output
Read MoreWhat is the preferred way to set Matplotlib figure/axes properties?
To set the properties of a plot, we can get the current axis of the plot. After that, we can perform several set_* methods to set the properties of the plot.StepsCreate a figure and a set of subplots using subplots() method with figsize=(5, 5).Create x and y data points using numpy.Plot x and y using plot() method.Set the title and labels (for X and Y axis) using set_xlabel() and set_ylabel() methods.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.linspace(-1, 1, 10) y = ...
Read MoreHow to remove gaps between bars in Matplotlib bar chart?
To remove gaps between bars, we can change the align value to center in the argument of bar() method.StepsCreate a dictionary called data with two keys, milk and water.Get the list of keys and values in the dictionay.Using subplots() method, create a figure and add a set of two subplots.On axis 2, use bar method to plot bars without gaps. Set the width attribute as 1.0. Set the title using set_title() method.Use tight_layout() to adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = {'milk': 12, 'water': ...
Read More