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Matplotlib Articles
Page 6 of 91
How to Annotate Matplotlib Scatter Plots?
Introduction Scatter plots are an essential tool for illustrating the connection between two continuous variables. They help us identify potential anomalies, patterns, and trends in the data. Yet, scatter charts can also be hard to interpret when there are numerous data points. If comments are made, some points of interest in a scatter plot could be easier to observe and understand. In order to make Matplotlib scatter plots more understandable, this article will examine how to annotate them. Syntax ax.annotate(text, xy, xytext=None, arrowprops=None, **kwargs) text − Text to be displayed in the annotation. xy − (x, y) coordinates ...
Read MoreHow to animate an object using the Arcade module?
Introduction Python's Arcade module allows users to build interactive animations. It has simple and straightforward documentation for making interactive games, and its object-oriented architecture makes working with animated objects simple. Wonderful Animations with Arcade Module The Arcade module in Python is a Python library for creating 2D video games and can be easily installed by pip installing the arcade package. In order to use Arcade in your Python project, you need to install the Arcade external dependency by running the command "pip install arcade" in the terminal. Let's look at two fantastic uses for this Python package. Create a ...
Read MoreHow to Adjust the Number of Ticks in Seaborn Plots?
Introduction Ticks are tiny symbols that Matplotlib uses to represent the positions of data points on both axes of a plot. They may be positioned to best fit the data range and are used to highlight certain locations on the x and y axes. Usually, ticks may be labeled to indicate the precise values they stand for. In the python package Seaborn, there are two functions, namely, xticks() and yticks() that can be used for adjusting the ticks of a given graph. Syntax To adjust the number of ticks in Seaborn plots, we can use the following syntax − ...
Read MoreHow to Adjust Marker Size in Matplotlib?
Introduction In a plot, a marker is a symbol that designates a single data point. Size, color, and shape are just a few of the attributes that may be changed. Markers are commonly used in conjunction with other charting methods to enhance the readability and comprehension of data. With Matplotlib, a wide variety of marker shapes are provided, including circles, squares, triangles, diamonds, and more. It is possible to alter the marker size to draw attention to crucial details or to develop more aesthetically pleasing plots. We'll show you how to alter the marker size in Matplotlib using examples of ...
Read MoreHow to save a Librosa spectrogram plot as a specific sized image?
Librosa is a Python package that helps to analyse audio and music files. This package also helps to create music retrieval information systems. In this article, we will see how to save a Librosa spectrogram plot as an image of specific size.StepsSet the figure size and adjust the padding between and around the subplots..Create a figure and a set of subplots.Initialize three different variables, hl, hi, wi, to store samples per time in the spectrogram, height and width of the images.Load a demo track.Create a window, i.e., a list for audio time series..Compute a mel-scaled spectrogram, using melspectrogram() with window ...
Read MoreHow to change the attributes of a networkx / matplotlib graph drawing?
To change the attributes of a netwrokx/matplotlib graph drawing, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add the graph's attributes. Add an edge between u and v.Get the edge attributes from the graph.Position the nodes with circles.Draw the graph G with Matplotlib.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.Graph() G.add_edge(0, 1, color='r', weight=2) G.add_edge(1, 2, color='g', weight=4) G.add_edge(2, 3, color='b', weight=6) G.add_edge(3, 4, ...
Read MoreHow to fill an area within a polygon in Python using matplotlib?
To fill an area within a polygon in Python using matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Get an instance of a polygon.Get the generic collection of patches with iterable polygons.Add a 'collection' to the axes' collections; return the collection.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots(1) polygon = Polygon(np.random.rand(6, 2), closed=True, alpha=1) ...
Read MoreHow to get data labels on a Seaborn pointplot?
To get data labels on a Seaborn pointplot, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a dataframe, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Create a pointplot.Get the axes patches and label; annotate with respective labels.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd import seaborn as sns plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'a': [1, 3, 1, 2, 3, 1]}) ax = sns.pointplot(df["a"], order=df["a"].value_counts().index) for p, label in zip(ax.patches, df["a"].value_counts().index): ax.annotate(label, ...
Read MoreHow to draw a precision-recall curve with interpolation in Python Matplotlib?
To draw a precision-recall curve with interpolation in Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create r, p and duplicate recall, i data points using numpy.Create a figure and a set of subplots.Plot the recall matrix in the range of r.shape.Plot the r and dup_r data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True r = np.linspace(0.0, 1.0, num=10) p = np.random.rand(10) * (1. - r) dup_p = p.copy() i ...
Read MoreHow to plot additional points on the top of a scatter plot in Matplotlib?
To plot additional points on the top of a scatter plot in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Make a list of x and y data points.Create a scatter plot with x and y data points.Plot the additional points with marker='*'To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # List of data points x = [1, 2, 6, 4] y = [1, 5, 2, 3] # Scatter plot ...
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