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Data Visualization Articles
Page 13 of 68
Plot a Line Graph for Pandas Dataframe with Matplotlib?
We will plot a line grapg for Pandas DataFrame using the plot(). At first, import the required libraries −import pandas as pd import matplotlib.pyplot as pltCreate a DataFrame −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Reg_Price": [2000, 2500, 2800, 3000, 3200, 3500], "Units": [100, 120, 150, 170, 180, 200] } )Plot a line graph with both the columns −plt.plot(dataFrame["Reg_Price"], dataFrame["Units"])ExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as plt # creating a DataFrame with 2 columns dataFrame = pd.DataFrame( { "Car": ['BMW', ...
Read MoreHow to curve text in a polar plot in matplotlib?
To curve text in a polar plot in matplotlib we can take the following stepsStepsSet 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.Plot the line with some degree, color='green' and linewidth=2.Create x and y data points, with some curve and plot them using plot() method.To display the figure, use Show() method.Examplefrom matplotlib import pyplot as plt from scipy.interpolate import interp1d import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax ...
Read MoreHow to put xtick labels in a box matplotlib?
To put xtick labels in a box, we can take the following stepsStepsCreate a new figure or activate an existing figure.Get the current axis of the figure.Set the left and bottom position of the axes.Set the position of the spines, i.e., bottom and left.To put xtick labels in a box, iterate the ticklabels and use set_bbox() method.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 plt.figure() ax = plt.gca() ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') ax.spines['bottom'].set_position(('data', 0)) ax.spines['left'].set_position(('data', 0)) for label in ax.get_xticklabels(): label.set_fontsize(12) label.set_bbox(dict(facecolor='red', edgecolor='black', alpha=0.7)) ...
Read MoreHow to plot a time as an index value in a Pandas dataframe in Matplotlib?
To plot a time as an index value in a Pandas dataframe in matplotlib, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe with two columns, time and speed.Set the DataFrame index using existing columns.To display the figure, use Show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Pandas dataframe df = pd.DataFrame(dict(time=list(pd.date_range("2021-01-01 12:00:00", periods=10)), speed=np.linspace(1, 10, 10))) # Set the dataframe index df.set_index('time').plot() # ...
Read MoreHow to remove the axis tick marks on a Seaborn heatmap?
To remove the axis tick marks on a Seaborn heatmap, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create random data points with 4×4 dimension.Plot the rectangular data as a color-encoded matrix.Use tick_params() for changing the appearance of ticks and tick labels. Use left=false and bottom=false to remove the tick marks.To display the figure, use Show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) ax = sns.heatmap(data, vmax=1) ax.tick_params(left=False, bottom=False) ...
Read MoreMake logically shading region for a curve in matplotlib
To make logically shading region for a curve in matplotlib, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create t, s1 and s2 data points using numpy.Create a figure and a set of subplots.Plot t and s1 data points; add a horizontal line across the axis.Create a collection of horizontal bars spanning *yrange* with a sequence of xranges.Add a '~.Collection' to the axes' collections; return the collection.To display the figure, use Show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.collections as collections plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ...
Read MoreHow to increase the line thickness of a Seaborn Line?
To increase the line thickness of a Seaborn line, we can take the following stepsStepsSet 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 Seaborn line plot with linewidth value in the argument. Here we have set linewidth=7.Rotate the tick params, i.e., labels by 45 degrees.To display the figure, use Show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt import pandas as pd import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame( dict( ...
Read MoreHow to adjust the space between Matplotlib/Seaborn subplots for multi-plot layouts?
To adjust the space between matplotlib/seaborn subplots for multi-plot layouts, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Adjust the subplot layout parameters.Create Seaborn's box plot for all the subplots.To display the figure, use Show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, axes = plt.subplots(2, 2) # Adjust the subplot layout parameters fig.subplots_adjust(hspace=0.125, wspace=0.125) # Create Seaborn boxplot for all the subplots sns.boxplot(ax=axes[0, 0]) sns.boxplot(ax=axes[0, 1]) sns.boxplot(ax=axes[1, 0]) sns.boxplot(ax=axes[1, ...
Read MoreHow to sort a boxplot by the median values in Pandas?
To sort a boxplot by the median values in Pandas, we can take the following stepsStepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe of two-dimensional, size-mutable, potentially heterogeneous tabular data, with three columns.Group the dataframe elements by marks and dob.Find the median of the dataframe.Get the sorted values of the median.Create a box plot from the DataFrame columns.To display the figure, use Show() method.Exampleimport pandas as pd import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame([ [23, 'James', 12], [39, 'Jimmy', ...
Read MoreHow to make a grouped boxplot graph in matplotlib?
To make a grouped boxplot graph in matplotlib, we can take the following steps −Import matplotlib.pyplot and seaborn.Set the figure size and adjust the padding between and around the subplots.Load an example Seaborn dataset from the online repository.Make a boxplot with male and female group in a single day.To display the figure, use show() method.Exampleimport seaborn as sns import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Import a Seaborn dataset data = sns.load_dataset('tips') # Create a grouped boxplot sns.boxplot(x=data['day'], y=data['total_bill'], hue=data['sex']) plt.show()OutputIt will produce the following ...
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