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To cycle through both colors and linestyles on a matplotlib figure, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Set the current rcParams, withcolors and linestyle.Plot the data points using plot() method.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as plt from cycler import cycler # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Set the rcParams with color or linestyle plt.rc('axes', prop_cycle=(cycler('color', ['r', 'g', 'b', 'y']) + cycler('linestyle', [':', '-.', '-', '--']))) # Plot the data points plt.plot([0, 5, 2, ... Read More
To rearrange levels using level name in MultiIndex, use the MultiIndex.reorder_levels() method in Pandas. Pass the levels (level names) to be rearranged as arguments.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob'], [50, 30, 40, 70]] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('rank', 'student', 'points'))Reorder levels of MultiIndex. The "order" parameter is used to set the level name in a ... Read More
To rasterize a plot in a bettery way without blurring the labels in 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.Axis 0 – Fill the area between the curve with alpha and rasterized=False.Add text to the axes.Axis 1 – Fill the area between the curve with alpha and rasterized=True.Add text to the axes.Axes 2 and 3 – Fill the area between the curve without alpha and rasterized=True and False, respectively.Add text to the axes.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as ... Read More
To get the properties of picked objects in matplotlib 3d, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Make a scatter plot of random data points.Bind the function *pick_event_method* to the event *pick_event*.Print x, y and z coordinates of the event.To display the figure, use Show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ... Read More
To swap levels of a MultiIndex, use the swaplevel() method in Pandas. The levels to be swapped should be mentioned as arguments.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob'], [50, 30, 40, 70]] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('rank', 'student', 'points'))Swap levels of MultiIndex using swaplevel(). The 1st parameter is the first level of index to be swapped. ... Read More
To return MultiIndex with multiple levels removed using the level names, use the MultiIndex.droplevel() method and set the multiple levels (level name) to be removed as arguments.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob'], [50, 30, 40, 70]] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('rank', 'student', 'points'))Drop a specific level from MultiIndex. The levels to be dropped is set ... Read More
To return MultiIndex with requested level removed using the level name, use the MultiIndex.droplevel() method and set the level (level name) to be removed as an argument.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob']] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student'))Drop a specific level from MultiIndex. The level to be dropped is set as the level name in ... Read More
To return MultiIndex with requested level removed, use the MultiIndex.droplevel() method in Pandas. Set the level to be removed as an argument.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob']] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student'))Drop a specific level. The level is 1 i.e. level 1 gets dropped” −print("Multi-index after dropping a level...", multiIndex.droplevel(1)) ExampleFollowing is the code ... Read More
To create a MultiIndex, use the from_arrays() method. However, to sort MultiIndex at a specific level, use the multiIndex.sortlevel() method in Pandas. Set the level as an argument. To sort in descending order, use the ascending parameter and set to False.At first, import the required libraries −import pandas as pdMultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob']] The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student'))Sort MultiIndex. The specific ... Read More
In a profile histogram, each bin contains the mean of its entries. To plot profile histograms in Python, we can use the regplot method from Seaborn.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Use seaborn.regplot to plot the data and a linear regress model fit. Use the parameter x_bins to bin the x variable into discrete bins. Use fit_reg=True to plot the regression model relating the x and y variables.To display the figure, use Show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt ... Read More