How To Change Plot Size In Python

In this Python Matplotlib tutorial, we'll talk over the Matplotlib increment plot size in python. Here nosotros will cover dissimilar examples related to the increase of the plot size using matplotlib. Moreover, we'll also comprehend the post-obit topics:

• Matplotlib increment plot size
• Matplotlib increment plot size jupyter
• Matplotlib increase plot size subplots
• Matplotlib pyplot set_size_inches
• Pandas matplotlib increase plot size
• Matplotlib change figure size and dpi
• Matplotlib plot change point size
• Matplotlib ready plot size in pixels
• Matplotlib change default plot size
• Matplotlib change bar plot size
• Matplotlib change besprinkle plot size
• Matplotlib set plot window size
• Matplotlib alter effigy size fig ax
• Matplotlib set plot size in centimeter

Matplotlib increase plot size

Plots are a corking method to graphically depict information and summarise it in a visually attractive way. Even so, if non plotted properly, it appears to be complex.

In matplotlib, we have several libraries for the representation of information. While creating plots in matplotlib, it is important for us to correct their size, so that we properly visualize all the features of the plot.

Also, check: Matplotlib plot a line

Matplotlib increase plot size jupyter

In this department, we'll acquire to increase the size of the plot using matplotlib in a jupyter notebook.

The syntax is given beneath:

          matplotlib.pyplot.rcParams["effigy.figsize"]        

The to a higher place syntax is used to increase the width and height of the plot in inches. By default, the width is half dozen.4 and the height is 4.8.

Let'due south see examples:

Instance #one

Here we'll meet an case to increase the size of the plot in the jupyter notebook.

                      # Import Library                        import matplotlib.pyplot equally plt            # Increment size of plot in jupyter            plt.rcParams["figure.figsize"] = (viii,5.5)            # Define Information            x = [2, 4, 6, 8] y = [5, 10, 15, 20]            # Plot            plt.plot(10, y, '-.')            # Display            plt.show()        

• Firstly, import the matplotlib.pyplot library
• Next, to increase the size of the plot in the jupyter notebook use plt.rcParams["figure.figsize"] method and set width and height of the plot.
• And so, define the data coordinates used for plotting.
• To plot a graph, use the plot() function and also set the style of the line to dot-nuance.
• To display the plot, on the user's screen use the show() part.

Width=8, Top=5.5

Example #ii

The source lawmaking below elaborates the process of increasing size in a jupyter notebook using matplotlib.

                      # Import Library                        import matplotlib.pyplot as plt import numpy as np            # Increase size of plot in jupyter                        plt.rcParams["figure.figsize"] = (10,half-dozen)            # Define Data            x = np.linspace(0, 10, grand) y = np.sin(ten)            # Plot                        plt.plot(10, y)            # Display            plt.show()        

Here we define data coordinates by using linspace() function and sin() function of numpy module.

# Increment Size of Plot in Jupyter

Read: What is matplotlib inline

Matplotlib increase plot size subplots

Hither we'll learn to increase the plot size of subplots using matplotlib. There are two ways to increase the size of subplots.

• Set i size for all subplots
• Set individual sizes for subplots

Set i size for all subplots

Here nosotros'll run into examples where we fix one size for all the subplots.

The post-obit is the syntax:

          fig, ax = plt.subplots(nrows , ncols , figsize=(width,height))        

Example #1

Here is the example where we increase the same size for all the subplots.

                      # Import necessary libraries                        import matplotlib.pyplot equally plt import numpy equally np            # Set one size for all subplot            fig, ax = plt.subplots(2, ii, figsize=(10,8))            # Preparing the data to subplots                        x = np.linspace(0,10,100) y1 = 10 ** 2 y2 = x ** 4 y3 = x ** vi y4 = 10 ** 8            # Plot            ax[0, 0].plot(x, y1, color='r') ax[0, 1].plot(x, y2, colour='k', linestyle=':') ax[1, 0].plot(x, y3, color='y', linestyle='-.') ax[1, i].plot(x, y4, color='c',linestyle='--')            # Display            plt.bear witness()        

• Import necessary libraries, such equally matplotlib.pyplot, and numpy.
• And then create subplots with 2 rows and 2 columns, using the subplots() function.
• To set 1 size for all subplots, use the figsize() role with width and height parameters.
• Define information coordinates used for plotting.
• To plot the subplots, use the plot() function with axes.
• To set different linestyles of the plotted lines, employ linestyle parameter.
• To visualize the subplots, use the show() function.

Prepare one size for all subplots

Instance #2

Allow'southward see i more example to set the aforementioned size for all the subplots to empathise the concept more clearly.

                      # Import necessary libraries                        import matplotlib.pyplot as plt import numpy equally np            # Change the figure size            plt.effigy(figsize=[15,14])            # Preparing the data to subplots            x = np.linspace(0, 10, 1000) y1 = np.sin(x) y2 = np.cos(x)            # Plot the subplots                        # Plot 1            plt.subplot(ii, two, 1) plt.plot(10, y1, color='chiliad')            # Plot 2            plt.subplot(2, ii, 2) plt.plot(x, y2, color='m')            # Display                        plt.bear witness()        

• To ascertain information coordinates, we employ linspace(), sin() and cos() functions of numpy.
• Here we increase the size of all subplots, using the effigy() method and we pass the figsize() as a parameter and set the width and height of the subplots.

Matplotlib increment plot size subplots

Set individual sizes for subplots

Here we'll run across different examples where we ready individual sizes for subplots using matplotlib.

The following is the syntax:

          fig, ax = plt.subplots(nrows, ncols, gridspec_kw=                        {'width_ratios': [3, 1],                         'height_ratios': [3, 3]})        

Example:

The source code beneath elaborates the procedure of increasing the size of the individual subplots.

                      # Import Library            import matplotlib.pyplot as plt            # Define subplots                        fig, ax = plt.subplots(ane, two,                        gridspec_kw={'width_ratios': [eight,15]})            # Ascertain data            x = [1, 2, 3, iv, 5] y1 = [7, 13, 24, 26, 32] y2 = [2, four, vi, 8, ten]            # Create subplots            ax[0].plot(x, y1, colour='ruby', linestyle=':') ax[1].plot(x, y2, colour='blue', linestyle='--')            # Suit padding            plt.tight_layout()            # Display            plt.show()        

• Import matplotlib.pyplot library.
• Then create subplots with one row and 2 columns , using the subplots() function.
• To set the private sizes for subplots, utilise gridspec_kw() method. The gridpec_kw is a dictionary with keywords that tin can exist used to modify the size of each grid.
• Next, ascertain data coordinates to plot data.
• To plot a line chart, utilize plot() function.
• To auto adjust padding, use tight_layout() function.
• To visualize the subplots, use prove() function.

Matplotlib increase plot size subplots

Example #2

The process of increasing the size of specific subplots is explained in the source code below.

                      # Import Library            import matplotlib.pyplot equally plt            # Define subplots            fig, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios':                         [10,iv]})            # Ascertain data            x = np.arange(0, xxx, 0.2) y1 = np.cos(ten) y2 = np.sin(x)            # Create subplots            ax[0].plot(x, y1, colour='red', linestyle=':') ax[1].plot(x, y2, color='blue', linestyle='--')            # Conform padding            plt.tight_layout()            # Brandish                        plt.prove()        

To plot a data, we ascertain data coordinates, by using arange(), cos() and sin() functions of numpy.

gridspec_kw={'width_ratios': [10,4]}

Read: Matplotlib plot bar nautical chart

Matplotlib pyplot set_size_inches

In matplotlib, to set up the figure size in inches, use the set_size_inches() method of the effigy module.

The following is the syntax:

          matplotlib.figure.Effigy.set_size_inches(w, h)        

Hither w represents the width and h represents the height.

Let's see unlike examples:

Example #i

Here is an example to increase the plot size past using the set_size_inches method.

                      # Import Libraries            import matplotlib.pyplot as plt            # Create figure                        fig = plt.figure()            # Figure size                        fig.set_size_inches(6.five, 6)            # Define Data Coordinates            x = [10, 20, 30, 40, 50] y = [25, 25, 25, 25, 25]            # Plot                        plt.plot(x, y, '--') plt.plot(y, 10)            # Display            plt.show()                  

• Import matplotlib.pyplot library.
• Adjacent, create a new figure by using the figure() function.
• To set the figure size in inches, employ the set_size_inches() function and pass the width and the height as a parameter, and set their value to 6.5 and six respectively.
• Then, define the x and y data coordinates used for plotting.
• To plot the line chart, use the plot() office.

Matplotlib pyplot set_size_inches

Example #2

Here we create a line chart using 10 and y coordinates (define by arange and sin office). We set the width of the plot to 12 and the height to ten inches by using the set_size_inches function.

                      # Import Libraries            import matplotlib.pyplot every bit plt  import numpy every bit np            # Create figure            fig = plt.effigy()            # Figure size            fig.set_size_inches(12,10)            # Ascertain Data Coordinates            x = np.arange(0,4*np.pi,0.ane) y = np.sin(4*10)            # Plot                        plt.plot(x, y, '--')            # Brandish            plt.show()                  

Here we define x and y information coordinates by using arange(), pi, and sin() function of numpy.

fig.set_size_inches(12,10)

Read: Matplotlib subplots_adjust

Pandas matplotlib increase plot size

In Pandas, the figsize parameter of the plot() method of the matplotlib module tin be used to alter the size of a plot bypassing required dimensions equally a tuple. It is used to summate the size of a figure object.

The post-obit is the syntax:

          figsize=(width, height)        

Width and summit must exist in inches.

Let'south come across different examples:

Example #ane

In this example, nosotros'll create a pie chart using pandas dataframe and increase the size of the plot.

                      # Import Library                        import pandas as pd            # Creat pandas dataframe            df = pd.DataFrame({'Owner': [50, 15, viii, xx, 12]},                   index=['Dog', 'Cat', 'Rabbit',                            'Parrot','Fish'])            # Plot            df.plot.pie(y='Owner', figsize=(viii,8))            # Display            plt.bear witness()        

• Import the pandas module.
• After this, create a pandas dataframe with an index.
• To plot a pie nautical chart, employ df.plot.pie() function.
• To increase the size of the plot, laissez passer the figsize() parameter forth with dimensions.

Pandas Matplotlib Increase Plot Size

Example #2

We'll make a line nautical chart with a pandas dataframe and increase the plot size in this example.

                      # Import libraries            import pandas as pd import matplotlib.pyplot as plt            # Data                        data = {'Year': [2021, 2020, 2019, 2018, 2017, 2016, 2015,                  2014],         'Birth_Rate': [17.377, 22.807, 26.170, xviii.020, 34.532,                            xviii.636, 37.718, xix.252] }            # Create DataFrame                        df = pd.DataFrame(data,columns=['Year','Birth_Rate'])            # Line Plot            df.plot(ten ='Year', y='Birth_Rate', kind = 'line',          figsize=(eight,vi))            # Display            plt.show()        

• Create a dataframe in pandas using the DataFrame() method.
• Next, plot the DataFrame using df.plot() role.
• Here we set up the kind parameter to the line in order to plot the line chart.
• To increment the size of the plot, laissez passer the figsize() parameter along with dimensions.

figsize=()

Read: Matplotlib set y axis range

Matplotlib change figure size and dpi

Hither nosotros'll larn to modify the effigy size and the resolution of the figure using matplotlib module.

The following is the syntax:

          matplotlib.pyplot.figure(figsize=(w,h), dpi)        

Let's see examples related to this:

Instance #1

In this example, we plot a line chart and change its size and resolution.

                      # Import Libraries            import matplotlib.pyplot as plt from matplotlib.pyplot import figure            # Fix fig size and dpi            effigy(figsize=(viii,6), dpi=250)            # Define Data            ten = range(0,12)            # Plot                        plt.plot(x)            # Display            plt.show()        

• First use matplotlib.pyplot.figure to create a new figure or activate ane that already exists.
• The method takes a figsize argument, which is used to specify the effigy's width and height (in inches).
• Then, enter a dpi value that corresponds to the figure's resolution in dots-per-inch.

Matplotlib alter figure size and dpi

Instance #2

Here we'll run into one more than case related to the modify in size and resolution of the effigy to understand the concept more clearly.

                      # Import Libraries            import matplotlib.pyplot as plt from matplotlib.pyplot import figure import numpy as np            # Set fig size and dpi            figure(figsize=(half-dozen,iv), dpi=150)            # Define Data            10 = np.random.randint(450,size=(80))            # Plot            plt.plot(x)            # Display            plt.evidence()        

• To define data coordinates, here we apply random.randint() method of numpy.
• Here we set the width, height, and dpi of the effigy to half dozen, 4, and 150 respectively.

figure(figsize=(half dozen,4), dpi=150)

Read: Matplotlib update plot in loop

Matplotlib plot change point size

Hither we'll learn how to change marker size in matplotlib with dissimilar examples.

The post-obit is the syntax:

          matplotlib.pyplot.scatter(x , y , s)        

Here x and y specify the data coordinates and s specify the size of the marker.

Example #1

Here we'll meet an case where nosotros set a single size for all the points.

                      # Import Library            import matplotlib.pyplot as plt            # Ascertain Data            A = [6, 7, 2, 5, 4, 8] B = [12, xiv, 17, xx, 22, 27]            # Besprinkle Plot and set size                        plt.scatter(A, B, s=eighty)            # Display            plt.show()        

• Import matplotlib.pyplot library.
• Next, define A and B information coordinates.
• To plot a besprinkle graph, utilize the scatter() part.
• To set up the same size to all points, use s every bit an argument and set up the size.
• To visualize the pot, apply the show() part.

Set Single Size For All Points

Example #ii

Hither nosotros'll run across an example where nosotros prepare dissimilar sizes for each signal

                      # Import Library                        import matplotlib.pyplot as plt            # Ascertain Information                        10 = [6, 7, 2, 5, 4, 8] y = [12, 14, 17, xx, 22, 27]            # Define Size            sizes = [20, 55, 85, 150, 250, 9]            # Scatter Plot and set size                        plt.scatter(x, y, s=sizes)            # Display                        plt.testify()        

• Import matplotlib.pyplot library.
• Adjacent, define information coordinates.
• Then, ascertain unlike sizes for each indicate.
• To plot a scatter graph, apply the besprinkle() function and set up the size laissez passer s parameter.
• To display the plot, use the bear witness() part.

Set Different Size For Each Betoken

Example #iii

Here we'll meet an example where we define the bespeak size for each indicate in the plot.

                      # Import Library            import matplotlib.pyplot as plt            # Define Data            ten = [2, four, 6, 8, x, 12, xiv, 16] y = [iv, 8, 12, sixteen, twenty, 24, 28, 32]            # Define Size            sizes = [3**n for north in range(len(x))]            # Scatter Plot and fix size            plt.scatter(ten, y, s=sizes, color='red')            # Display            plt.show()        

• Import matplotlib.pyplot library for data visualization.
• Define data coordinates.
• Create a function to define the betoken sizes to use for each point in the plot.
• To plot a scatter graph, use the scatter() function.

Matplotlib Plot Alter Point Size

Read: Matplotlib Pie Chart Tutorial

Matplotlib set plot size in pixels

Here nosotros'll see examples where we convert inches to pixels and plot the graph using matplotlib module.

Example #1

In the below example we'll change the plot size by using the pixel conversion method.

                      # Import Libraries            import matplotlib.pyplot as plt from matplotlib.pyplot import figure import numpy equally np  px = 1/plt.rcParams["figure.dpi"]            # Set fig size in Pixel            figure(figsize=(600 * px , 450 * px))            # Define Information            x = np.random.randint(800,size=(120))            # Plot            plt.plot(x)            # Display                        plt.show()        

• Import necessary libraries such as matplotlib.pyplot, figure, and numpy.
• Then use, default pixels values, rcParams['figure.dpi'] to set values to px.
• Now, set figsize in pixels.
• Then, define the data coordinates for plotting.
• To plot the line chart, use the plot() function.
• To display the plot, use the show() office.

Matplotllib fix plot size in pixels

Instance #2

Here nosotros change the plot size in pixels past using the dpi argument.

                      # Import Libraries            import matplotlib.pyplot equally plt from matplotlib.pyplot import figure            # Set fig size and dpi            effigy(dpi=100)            # Ascertain Data Coordinates                        water_quantity = [17, 27, 14, 22, 16, 6]            # Add labels            water_uses = ['Shower', 'Toilet', 'Leaks', 'Clothes Wsher',                'Faucet', 'Other']            # Colors                        colors =['salmon','palegreen','skyblue','plum','pink','silver']            # Plot            plt.pie(water_quantity, colors=colors)            # Add legend            plt.legend(labels=water_uses, fontsize=18, loc='upper center',             bbox_to_anchor=(0.5, -0.04), ncol=two)            # Display            plt.prove()                  

• Here we utilize, the dpi parameter to set the plot in pixels with the figure() role.
• Adjacent, nosotros define data coordinates, labels, and colors for pie chart cosmos,
• To plot a pie chart, we utilise the pie() function.
• To add a legend to a plot, we apply the legend() function.

matplotlib set up plot size in pixels(dpi)

Read: Matplotlib scatter plot color

Matplotlib change default plot size

In Matplotlib, the dictionary object rcParams contains the properties. The figure size could be used equally the cardinal figure's value in figsize in rcParams, which represents the size of a figure.

To change the size by default, we use plt.rcParams. It is acceptable when nosotros placed it earlier or afterward plt.plot. Any figure made using the same scripts will be assigned the same figure size.

Let's run across examples:

Example #one

Hither nosotros'll set the default, size of the figure to 7 past 7.

                      # Import Library            import matplotlib.pyplot as plt            # Default plot size            plt.rcParams["figure.figsize"] = (7,7)            # Plot                        plt.plot([[five, 6], [9, 10], [1, two], [i, two]])            # Show                        plt.show()        

• Import matplotlib.pyplot library.
• To set default plot size, use plt.rcParams["figure.figsize"].
• To plot a chart, use the plot() function.

Matplotlib alter default plot size

Read: Matplotlib Plot NumPy Array

Matplotlib change bar plot size

Here we'll learn the different ways to change the size of the bar plot using the matplotlib module with the aid of examples.

Example #one

Here we'll employ the figsize() method to change the bar plot size.

                      # Import Library            import matplotlib.pyplot equally plt            # Alter size            plt.figure(figsize=(9,7))            # Define Data            pets = ['Rabbit', 'Dog', 'Cat', 'Goldfish', 'Parrot'] no_of_people = [4, viii, eleven, 6, 5]            # Plot bar chart            plt.bar(pets, no_of_people)            # Display            plt.testify()        

• Import matplotlib.pyplot library.
• To change the effigy size, employ figsize argument and set up the width and the height of the plot.
• Side by side, we ascertain the data coordinates.
• To plot a bar chart, utilize the bar() office.
• To brandish the chart, utilize the show() function.

Matplotlib change bar plot size

Example #two

In this example, nosotros'll change the size of the bar nautical chart by using rcParams.

                      # Import Library                        import matplotlib.pyplot as plt            # Alter size                        plt.rcParams['effigy.figsize']=(8,6.5)            # Define Data                        school_suppiles = ['Pencil', 'Scale', 'Pen', 'Sharpner',                      'Eraser'] no_of_suppiles = [viii, 3, ii, v, four]            # Plot bar chart                        plt.bar(school_suppiles, no_of_suppiles)            # Display            plt.show()        

Hither we employ the default method, to alter the size of bar plots i.e. by using plt.rcParams['figure.figsize'].

plt.rcParams['figure.figsize']

Read: Matplotlib set_xticklabels

Matplotlib change scatter plot size

Hither we'll learn the different means to change the size of the scatter plot using the matplotlib module with the assistance of examples.

Instance #one

Here nosotros'll learn to change the size of scatter plot by using figsize.

                      # Import Library            import matplotlib.pyplot equally plt            # Prepare size            plt.figure(figsize=(8,5))            # Ascertain Dataset            x1 = [45, 58, 29, 83, 95, 20,        98, 27]    y1 = [31, 75, eight, 29, 79, 55,        43, 72]    x2 = [23, 41, 25, 64, iii, 15,       39, 66]    y2 = [26, 34, 38,        20, 56, two, 47, 15]            # Plot Besprinkle Graph            plt.scatter(x1, y1, c ="cyan",              marking ="due south",              edgecolor ="blackness",              s = 50)    plt.scatter(x2, y2, c ="yellow",             linewidths = 2,             marking ="^",              edgecolor ="red",              s = 200)            # Display                        plt.testify()        

• Import matplotlib.pyplot library.
• To set figure size, use the figsize parameter and ready the width and meridian of the figure.
• Define datasets to plot to scatter graph.
• To plot a scatter plot, use the scatter method.
• To add extra features to the plot, pass color, size, marker, edgecolor, mark equally a parameter.

Matplotlib change scatter plot size

Example #ii

In this case, we apply the set_size_inches method change the size of the scatter plot.

                      # Import Libraries            import matplotlib.pyplot as plt import numpy as np            # Create figure            fig = plt.effigy()            # Figure size                        fig.set_size_inches(half-dozen,viii)            # Define Data                        x = np.linspace(0, 10, 100) y = np.sin(x)            # Plot            plt.besprinkle(10, y)            # Brandish                        plt.show()        

• Import necessary libraries such as matplotlib.pyplot and numpy.
• And then create a new effigy by using the figure() method.
• To set the size of the scatter plot, utilise the set_size_inches() method and laissez passer width and height.
• To define data coordinates, utilize linspace() and sin() methods.
• To plot a besprinkle graph, use the scatter() function.

fig.set_size_inches(6,viii)

Read: Matplotlib tight_layout – Helpful tutorial

Matplotlib change figure size fig ax

We tin can easily change the height and the width of the plot past using the set_figheight and the set_figwidth method of the matplotlib module.

Allow's see examples related to this concept:

Example #1

Here we ready the width of the plot, by using the set_figwidth method.

                      # Import Library            import numpy as np  import matplotlib.pyplot as plt            # Fix width            fig = plt.figure() fig.set_figwidth(8)            # Information Coordinates                        x = np.arange(2, 8)  y = np.array([5, 8, 6, twenty, 18, 30])            # Plot            plt.plot(x, y, linestyle='--')            # Brandish            plt.show()        

• First import necessary libraries, such every bit numpy and matplotlib.pyplot.
• Adjacent, use the set_figwidth() method to modify the width of the plot.
• To define data coordinates, utilize arange() and array() methods of numpy.
• To plot a line graph with a dashed line manner, utilize the plot() function with linestyle parameter.

Matplotlib change figure size fig ax

Case #2

Here nosotros set up the height of the plot, by using the set_figheight method.

                      # Import Library            import numpy as np  import matplotlib.pyplot as plt            # Set summit                        fig = plt.figure() fig.set_figheight(viii)            # Data Coordinates                        ten = np.arange(2, viii)  y = np.array([5, 8, 6, twenty, 18, xxx])            # Plot            plt.plot(ten, y, linestyle='--')            # Display                        plt.bear witness()        

• First import necessary libraries, such as numpy and matplotlib.pyplot.
• Adjacent, apply the set_figheight() method to modify the height of the plot.
• To ascertain data coordinates, use arange() and assortment() methods of numpy.

Matplotlib alter figure size

Read: Matplotlib 10-centrality label

Matplotlib fix plot size in centimeter

In matplotlib by default, the effigy size is in inches. Here we'll acquire to set plot size in centimeter with the help of an case.

Instance:

                      # Import Library            import numpy as np  import matplotlib.pyplot as plt            # Set Size            cm = 1/2.54  plt.figure(figsize=(fifteen*cm, 11*cm))            # Data Coordinates            x = np.arange(2, viii)  y = ten * 2 + 6            # Plot            plt.plot(10, y)            # Display                        plt.show()        

• Import numpy library.
• Adjacent, import the matplotlib library.
• Set figure size by conversion inches to centimeters.
• Define data coordinates x and y.
• To plot a line nautical chart, nosotros utilize the plot() function.

Matplotlib set up plot size in centimeter

You may too similar to read the post-obit Matplotlib tutorials.

• Matplotlib scatter plot legend
• Matplotlib multiple bar chart
• Stacked Bar Chart Matplotlib
• Matplotlib multiple plots
• Draw vertical line matplotlib

In this Python tutorial, nosotros have discussed the "Matplotlib increment plot size" and we accept also covered some examples related to it. These are the following topics that we accept discussed in this tutorial.

• Matplotlib increment plot size
• Matplotlib increase plot size jupyter
• Matplotlib increase plot size subplots
• Matplotlib pyplot set_size_inches
• Pandas matplotlib increment plot size
• Matplotlib change figure size and dpi
• Matplotlib plot change point size
• Matplotlib set plot size in pixels
• Matplotlib change default plot size
• Matplotlib change bar plot size
• Matplotlib alter besprinkle plot size
• Matplotlib set plot window size
• Matplotlib change figure size fig ax
• Matplotlib ready plot size in centimeter

How To Change Plot Size In Python,

Source: https://pythonguides.com/matplotlib-increase-plot-size/

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