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Articles by Rishikesh Kumar Rishi
Page 13 of 102
How to unset 'sharex' or 'sharey' from two axes in Matplotlib?
To inset sharex and sharey from two axes in matplotlib, we can use 'none', i.e., False or 'none'. Each subplot X- or Y-axis will be independent.StepsSet the figure size and adjust the padding between and around the subplots.Initialize two variables rows and cols.Create a figure and a set of subplots.Iterate the axes where rows=2 and cols=4.Plot the random data on the axis.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True rows = 2 cols = 4 fig, axes = plt.subplots(rows, cols, sharex='none', sharey='none', squeeze=False) ...
Read MoreHow to obtain 3D colored surface via Python?
To obtain 3D colored surface via Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Get 3D data, i.e., z.Create a new figure or activate an existing figure.Get the 3D axes.Create a surface plot.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 x = np.linspace(-3, 3, 100) y = np.cos(x) x, y = np.meshgrid(x, y) z = x ** 2 + y ** 2 - 2 ...
Read MoreScatter a 2D numpy array in matplotlib
To scatter a 2D numpy array in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create random data of 100×3 dimension.Use the scatter() method to plot 2D numpy array, i.e., data.To display the figure, use show() method.Exampleimport 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 # Random data of 100×3 dimension data = np.array(np.random.random((100, 3))) # Scatter plot plt.scatter(data[:, 0], data[:, 1], c=data[:, 2], cmap='hot') # Display the plot plt.show()OutputIt will produce ...
Read MoreHow to avoid overlapping error bars in matplotlib?
To avoid overlapping error bars in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a list of names.Get the data points for y1 and y2, and errors ye1, ye2.Create a figure and a set of subplots.Create a mutable 2D affine transformation, trans1 and trans2.Plot y versus x as lines and/or markers with attached errorbars.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt from matplotlib.transforms import Affine2D plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = ['Jack', 'James', 'Tom', 'Garry'] y1, ...
Read MoreHow do I remove the Y-axis from a Pylab-generated picture?
To remove the Y-axis from a Pylab-generated picture, we can get the current axis of the plot and use the set_visible(False) method.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points using plot() method.Get the current axis of the current figure.Set the visibility to False for the Y-axis.To display the figure, use show() method.Exampleimport numpy as np import pylab # Set the figure size pylab.rcParams["figure.figsize"] = [7.50, 3.50] pylab.rcParams["figure.autolayout"] = True # Random data points x = np.random.rand(10) y = np.random.rand(10) ...
Read MoreFlushing all current figures in matplotlib
To flush all current figures in matplotlib, use close('all') method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure with the title "First Figure".Create another figure with the title "Second Figure".To close all figures, use close('all').To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.figure("First Figure") plt.figure("Second Figure") # plt.close('all') plt.show()OutputNotice that we have commented the line −plt.close('all') Hence, it will display two figures −Uncomment the line plt.close('all') and run the code again. It will flush all the current figures.
Read MoreHow to create multiple series scatter plots with connected points using seaborn?
To create multiple series scatter plots with connected points using seaborn, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas data frame of two-dimensional, size-mutable, potentially heterogeneous tabular data.Multi-plot grid for plotting conditional relationships.Apply a plotting function to each facet's subset of the data.Plot the scatter and the data points with x and y data points.To display the figure, use show() method.Exampleimport pandas as pd import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({"x": [4, ...
Read MoreCombining two heatmaps in seaborn
To combine two heatmaps in seaborn, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create two Pandas data frames.Create a figure and a set of subplots, ax1 and ax2.Plot the rectangular data as a color-encoded matrix, on ax1 and ax2.Move ticks and ticklabels (if present) to the right of the axes.Keep the width of the padding between subplots minimum, as a fraction of the average axes width.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns plt.rcParams["figure.figsize"] ...
Read MoreBoxplot stratified by column in Python Pandas
To create a boxplot stratified by column in Python class, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas data frame of two-dimensional, size-mutable, potentially heterogeneous tabular data.Compute the histogram of a set of data.Create a boxplot startified by column.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 df = pd.DataFrame({"column1": [4, 6, 7, 1, 8], "column2": [1, 5, 7, ...
Read MoreHow to plot aggregated by date pandas dataframe?
To plot aggregated by date pandas dataframe, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a data frame, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Get the values of aggregated by date pandas dataframe.Plot the df (Step 3) with kind="bar".To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt, dates # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create a dataframe df = pd.DataFrame(dict(data=list(pd.date_range("2021-01-01", periods=10)), value=np.linspace(1, 10, 10))) df = df.groupby('data').agg(['sum']).reset_index() ...
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