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Articles by Rishikesh Kumar Rishi
Page 27 of 102
Group-by and Sum in Python Pandas
To find group-by and sum in Python Pandas, we can use groupby(columns).sum().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Find the groupby sum using df.groupby().sum(). This function takes a given column and sorts its values. After that, based on the sorted values, it also sorts the values of other columns.Print the groupby sum.Exampleimport pandas as pd df = pd.DataFrame( { "Apple": [5, 2, 7, 0], "Banana": [4, 7, 5, 1], "Carrot": [9, 3, 5, 1] } ) print "Input DataFrame 1 ...
Read MoreHow to get column index from column name in Python Pandas?
To get column index from column name in Python Pandas, we can use the get_loc() method.Steps −Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Find the columns of DataFrame, using df.columns.Print the columns from Step 3.Initialize a variable column_name.Get the location, i.e., of index for column_name.Print the index of the column_name.Example −import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print"Input DataFrame 1 is:", df columns = ...
Read MoreHow to find the common elements in a Pandas DataFrame?
To find the common elements in a Pandas DataFrame, we can use the merge() method with a list of columnsStepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df1.Print the input DataFrame, df1.Create another two-dimensional tabular data, df2.Print the input DataFrame, df2.Find the common elements using merge() method.Print the common DataFrame.Exampleimport pandas as pd df1 = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) df2 = ...
Read MoreHow to properly enable ffmpeg for matplotlib.animation?
To enable ffmpeg for matplotlib.animation, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set the ffmpeg directory.Create a new figure or activate an existing figure, using figure() method.Add an 'ax1' to the figure as part of a subplot arrangement.Plot the divider based on the pre-existing axes.Create random data to be plotted, to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, cb.Set the title as the current frame.Make a list of colormaps.Make an animation by repeatedly calling a function, animate. The ...
Read MoreHow to make multipartite graphs using networkx and Matplotlib?
To make multipartite graph in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of subset sizes and colors.Define a method for multilayered graph that could return a multilayered graph object.Set the color of the nodes.Position the nodes in layers of straight lines.Draw the graph G with Matplotlib.Set equal axis properties.To display the figure, use show() method.Exampleimport itertools import matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] subset_color = ...
Read MoreHow to plot the difference of two distributions in Matplotlib?
To plot the difference of two distributions in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a and b datasets using Numpy.Get kdea and kdeb, i.e., representation of a kernel-density estimate using Gaussian kernels.Create a grid using Numpy.Plot the gird with kdea(grid), kdeb(grid) and kdea(grid)-kdeb(grid), using plot() method.Place the legend at the upper-left corner.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import scipy.stats plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True a = np.random.gumbel(50, 28, 100) b = np.random.gumbel(60, 37, 100) ...
Read MoreHow to visualize 95% confidence interval in Matplotlib?
To visualize 95% confidence interval in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data sets.Get the confidence interval dataset.Plot the x and y data points using plot() method.Fill the area within the confidence interval range.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.arange(0, 10, 0.05) y = np.sin(x) # Define the confidence interval ci = 0.1 * np.std(y) / np.mean(y) plt.plot(x, y, color='black', ...
Read MorePlotting an imshow() image in 3d in Matplotlib
To plot an imshow() image in 3D in Matplotlib, we can take the following steps −Create xx and yy data points using numpy.Get the data (2D) using X, Y and Z.Create a new figure or activate an existing figure using figure() method.Add an 'ax1' to the figure as part of a subplot arrangement.Display the data as an image, i.e., on a 2D regular raster with data.Add an 'ax2' to the figure as part of a subplot arrangement.Create and store a set of contour lines or filled regions.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np ...
Read MoreAdding extra contour lines using Matplotlib 2D contour plotting
To add extra contour lines using Matplotlib 2D contour plotting, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create e a function f(x, y) to get the z data points from x and y.Create x and y data points using numpy.Make a list of levels using Numpy.Make a contour plot using contour() method.Label the contour plot and set the title of the plot.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 def f(x, y): return ...
Read MoreHow to remove the digits after the decimal point in axis ticks in Matplotlib?
To remove the digits after the decimal point in axis ticks in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.To set the xtick labels only in digits, we can use x.astype(int) method.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 x = np.array([1.110, 2.110, 4.110, 5.901, 6.00, 7.90, 8.90]) y = np.array([2.110, 1.110, 3.110, 9.00, 4.001, 2.095, 5.890]) fig, ...
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