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MRITUNJAY SRIVASTAVA
MRITUNJAY SRIVASTAVA

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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners

#python #tutorial #datascience #programming

Data visualization is about viewing or visualizing data in the form of graphical plots, charts, figures, and animations.

Data Visualization Libraries in Python?:

  • Altair - Declarative statistical visualization library for Python.
  • Bokeh - Interactive Web Plotting for Python.
  • bqplot - Interactive Plotting Library for the Jupyter Notebook.
  • Cartopy - A cartographic Python library with matplotlib support.
  • Dash - Built on top of Flask, React, and Plotly aimed at analytical web applications.
  • diagrams - Diagram as Code.
  • Matplotlib - A Python 2D plotting library.
  • plotnine - A grammar of graphics for Python based on ggplot2.
  • Pygal - A Python SVG Charts Creator.
  • PyGraphviz - Python interface to Graphviz.
  • PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
  • Seaborn - Statistical data visualization using Matplotlib.
  • VisPy - High-performance scientific visualization based on OpenGL.

What is Matplotlib?

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.

  • Organized in hierarchy
  • top-level matplotlib.pyplot module is present
  • pyplot is used for a few activities such as figure creation,
  • Through the created figure, one or more axes/subplot objects are created
  • Axes objects can further be used for doing many plotting actions. 
#install matplotlib !pip install matplotlib
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 Requirement already satisfied: matplotlib in c:\users\mritu\anaconda3\lib\site-packages (3.8.0) Requirement already satisfied: contourpy>=1.0.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.0.5) Requirement already satisfied: cycler>=0.10 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (0.11.0) Requirement already satisfied: fonttools>=4.22.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (4.25.0) Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.4.4) Requirement already satisfied: numpy<2,>=1.21 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.24.3) Requirement already satisfied: packaging>=20.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (23.0) Requirement already satisfied: pillow>=6.2.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (9.4.0) Requirement already satisfied: pyparsing>=2.3.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (3.0.9) Requirement already satisfied: python-dateutil>=2.7 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (2.8.2) Requirement already satisfied: six>=1.5 in c:\users\mritu\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
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#import library import numpy as np import matplotlib import matplotlib.pyplot as plt %matplotlib inline
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#get matplotlib version matplotlib.__version__ >>> '3.8.0'
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#upgrade matplotlib !pip install --upgrade matplotlib
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 Requirement already satisfied: matplotlib in c:\users\mritu\anaconda3\lib\site-packages (3.8.0) Requirement already satisfied: contourpy>=1.0.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.0.5) Requirement already satisfied: cycler>=0.10 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (0.11.0) Requirement already satisfied: fonttools>=4.22.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (4.25.0) Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.4.4) Requirement already satisfied: numpy<2,>=1.21 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (1.24.3) Requirement already satisfied: packaging>=20.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (23.0) Requirement already satisfied: pillow>=6.2.0 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (9.4.0) Requirement already satisfied: pyparsing>=2.3.1 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (3.0.9) Requirement already satisfied: python-dateutil>=2.7 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (2.8.2) Requirement already satisfied: six>=1.5 in c:\users\mritu\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
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#get matplotlib version after upgrade matplotlib.__version__ >>> '3.8.0'
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Parts of Matplotlib

  • Figure: Whole area chosen for plotting
  • Axes: Area where data is plotted
  • Axis: Number-line link objects, which define graph limits (x-axis, y-axis)
  • Artist: Every element on the figure is an artist (Major and Minor tick table)

Figure

  • It refers to the whole area or page on which everything is drawn.
  • It includes Axes, Axis and other Artist element
  • The figure is created using the figure function of the pyplot module 
#create figure using plt.figure() return figure object fig = plt.figure() # Viewing figure to display figure needs to tell explicitly pyplot to display it # The below command will return the object to display the figure it requires at least one axe.  plt.show() >>> <Figure size 640x480 with 0 Axes>
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Axes

  • Axes are the region of the figure, available for plotting data
  • Axes object is associated with only one Figure
  • A Figure can contain one or more number of Axes element
  • Axes contain two Axis objects in case of 2D plots and three objects in case of 3D plots 
#Axes can be added by using methods add_subplot(nrows, ncols, index) return axes object fig = plt.figure() ax = fig.add_subplot() plt.show()
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Matplotlib_12_0 

#Adjusting Figure Size => plt.figure(figsize=(x, y)) fig = plt.figure(figsize=(3, 3)) ax = fig.add_subplot(1, 1, 1) plt.show()
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Matplotlib_13_0

#setting title and axis label as a parameter fig = plt.figure(figsize=(10,3)) ax = fig.add_subplot(1,1,1) ax.set(title="Figure" , xlabel='x-axis' , ylabel='y-axis' , xlim=(0,5) , ylim=(0,10) , xticks=[0.1, 0.9, 2, 3, 4, 5] , xticklabels=['pointone, 'pointnine', 'two', 'three', 'four', 'five']) plt.show()
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Matplotlib_14_0

#setting title and axis label as a method fig = plt.figure(figsize=(3,3)) ax = fig.add_subplot(111) ax.set_title("Figure") ax.set_xlabel("X-Axis") ax.set_ylabel('Y-Axis') ax.set_xlim([0,5]) ax.set_ylim([0,10]) plt.show()
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Matplotlib_15_0

#setting title and axis label explicitly fig = plt.figure(figsize=(3,3)) ax = fig.add_subplot(1,1,1) plt.title('Figure') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') plt.xlim(0,5) plt.ylim(0,10) plt.show()
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Matplotlib_16_0

#plot data points in graph => plt.plot(x, y) will use x = list(range(0,5)) y = list(range(0,10, 2)) fig = plt.figure(figsize=(3,3)) ax = fig.add_subplot(1,1,1) ax.set(title="Figure" , xlabel='x-asix' , ylabel='y-axis' , xlim=(0,5) , ylim=(0,10)) plt.plot(x,y) plt.show()
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Matplotlib_17_0

#adding legend in graph => plt.legend(x, y, label='legend'); plt.legend() will use x = list(range(0,5)) y = list(range(0,10, 2)) fig = plt.figure(figsize=(3,3)) ax = fig.add_subplot(1,1,1) ax.set(title="Figure" , xlabel='x-asix' , ylabel='y-axis' , xlim=(0,5) , ylim=(0,10)) plt.plot(x,y, label='legend') plt.legend() plt.show()
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Matplotlib_18_0

Types Of Plot

Line Plot

  • Line Plot is used to visualize a trend in data.
  • A Line Plot is also used to compare two variables.
  • Line Plots are simple and effective in communicating.
  • plot function is used for drawing Line plots.
  • Syntax: plt.plot(x,y) 
x = [1, 5, 8, 12, 15, 19, 22, 26, 29] y = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7] fig = plt.figure(figsize=(8,6)) ax = fig.add_subplot(1,1,1) ax.set(title='Line Plot Graph' , xlabel='X-Axis' , ylabel='Y-Axis' , xlim=(0, 30) , ylim=(25, 35)) ax.plot(x, y) plt.show()
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Matplotlib_21_0

Parameter for Plot

  • color - Sets the color of the line.
  • linestyle - Sets the line style, e.g., solid, dashed, etc.
  • linewidth - Sets the thickness of a line.
  • marker - Chooses a marker for data points, e.g., circle, triangle, etc.
  • markersize - Sets the size of the chosen marker.
  • label - Names the line, which will come in legend. 
fig = plt.figure(figsize=(6,3)) ax = fig.add_subplot(1,1,1) ax.set(title='Line Plot Graph' , xlabel='X-Axis' , ylabel='Y-Axis' , xlim=(0, 30) , ylim=(25, 35)) ax.plot(x , y , color='blue' , linestyle='dotted' , linewidth=1 , marker='*' , markersize=10 , label='label') plt.legend() plt.show()
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Matplotlib_23_0

Multiple lines with a single plot function 

x=[1,4,6,8,2] y=[10,10,1,5,0] fig = plt.figure(figsize=(8,4)) ax = fig.add_subplot() ax.plot(x, y, 'g<', x,y, 'r--')
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``` [,
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 ![Matplotlib_25_1](https://github.com/mritunjaysri28/Matplotlib/assets/25318797/06a2a78f-acb7-4a3e-b3c2-c16d360af70f) ## Scatter Graph<a name="scatterplot"></a> * It similar to a line graph * Used to show how one variable is related to another * It consists of data points, if it is linear then it is highly correlated * It only marks the data point. * Syntax: plt.scatter(x,y) ### Parameter of Scatter Graph * c: Sets color of markers. * s: Sets the size of markers. * marker: Select a marker. e.g.: circle, triangle, etc * edgecolor: Sets the color of lines on the edges of markers. ```python x=[1,4,6,8,2] y=[10,10,1,5,0] fig = plt.figure(figsize=(8,4)) ax = fig.add_subplot() ax.scatter(x , y , c='red' , s=500 , marker='*' , edgecolor='blue' , label='scatter') ax.set_title('Scatter') ax.set_xlabel('X-Axis') ax.set_ylabel('Y-Axis') plt.legend()
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<matplotlib.legend.Legend at 0x24718801890>
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Matplotlib_28_1

Bar Chart

  • It is mostly used to compare categories
  • bar is used for vertical bar plots
  • barh is used for horizontal bar plots
  • Syntax: bar(x, height) or bar(y,width)

Parameter of Bar Graph

  • Color: Sets the color of bars.
  • edgecolor: Sets the color of the borderline of bars.
  • label: Sets label to a bar, appearing in legend.
  • color: the color of the bar
  • align: Aligns the bars w.r.t x-coordinates 
# width: Sets the width of bars bar(x,width)
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# height: Sets the height of bars barh(y,height)
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#vertical bar graph x = [1, 2, 3] y = [10,20,30] fig = plt.figure(figsize=(8,4)) ax = fig.add_subplot() ax.set(title='Bar Graph' , xlabel='X-Axis' , ylabel='Y-Axis' , xticks=x , label='label' , xticklabels=['first', 'second', 'third']) ax.bar(x , y , color='green' , edgecolor='red' , width=0.5 , align='edge' , label='label' , linewidth=5) plt.legend() plt.show()
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Matplotlib_31_0

#horizontal bar graph x = [1, 2, 3] y = [10,20,30] fig = plt.figure(figsize=(8,4)) ax = fig.add_subplot() ax.set(title='Bar Graph' , xlabel='X-Axis' , ylabel='Y-Axis' , xticks=x , label='label' , xticklabels=['first', 'second', 'third']) plt.barh(x , y , color='green' , edgecolor='red' , height=0.5 , align='edge' , label='label') plt.legend() plt.show()
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Matplotlib_32_0

Error Bar

It plots y versus x as lines and markers with attached error bars.

Parameter of Error Bar

  • ecolor: it is the color of the error bar lines.
  • elinewidth: it is the linewidth of the errorbar lines.
  • capsize: it is the length of the error bar caps in points.
  • barsabove: It contains a boolean value True for plotting error bars above the plot symbols. Its default value is False.
  • Syntax: errorbar() 
a = [1, 3, 5, 7] b = [11, 2, 4, 19] c = [1, 1, 1, 1] d = [4, 3, 2, 1] fig=plt.figure(figsize=(4,4)) ax=fig.add_subplot() plt.bar(a, b) plt.errorbar(a, b, xerr=c, yerr=d, fmt="^", color="r")
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<ErrorbarContainer object of 3 artists>
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Matplotlib_34_1

Pie Plot

  • It is effective in showing the proportion of categories.
  • It is best suited for comparing fewer categories.
  • It is used to highlight the proportion of one or a group of categories.
  • Syntax: pie(x), x: size of portions, passed as fraction or number

Parameter of Pie

  • colors: Sets the colors of portions.
  • labels: Sets the labels of portions.
  • startangle: Sets the start angle at which the portion drawing starts.
  • autopct: Sets the percentage display format of an area, covering portions. 
x=[1,2,3,4,5] fig=plt.figure(figsize=(4,4)) ax=fig.add_subplot() ax.set(title='Pie') plt.pie(x , colors=['brown', 'red', 'green', 'yellow', 'blue'] , labels=['first', 'second', 'third', 'fourth', 'fifth'] , startangle=0 , autopct='%1.1f%%')
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 ([<matplotlib.patches.Wedge at 0x2471900da10>, <matplotlib.patches.Wedge at 0x247189a1f10>, <matplotlib.patches.Wedge at 0x247189a26d0>, <matplotlib.patches.Wedge at 0x247189a2c10>, <matplotlib.patches.Wedge at 0x247189670d0>], [Text(1.075962358309037, 0.22870287165240302, 'first'), Text(0.7360436312779136, 0.817459340184711, 'second'), Text(-0.33991877217145816, 1.046162142464278, 'third'), Text(-1.0759623315431446, -0.2287029975759841, 'fourth'), Text(0.5500001932481627, -0.9526278325909777, 'fifth')], [Text(0.5868885590776565, 0.12474702090131072, '6.7%'), Text(0.4014783443334074, 0.4458869128280241, '13.3%'), Text(-0.18541023936624987, 0.5706338958896061, '20.0%'), Text(-0.5868885444780788, -0.12474708958690041, '26.7%'), Text(0.3000001054080887, -0.5196151814132605, '33.3%')])
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Matplotlib_37_1

Histogram Chart

  • It is used to visualize the spread of data in a distribution
  • Syntax: hist(x), x is the data values

Parameter of Histogram

  • Color: Sets the color of bars.
  • bins: Sets the number of bins to be used.
  • density: Sets to True where bins display fraction and not the count. 
x=[1,2,3,4,5] fig=plt.figure(figsize=(20,4)) ax=fig.add_subplot() ax.set(title='Histogram' , xlabel='X-Axis' , ylabel='Y-Axis') ax.hist(x , color='red' , bins=10 , density=True , orientation='vertical')
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 (array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]), array([1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]), <BarContainer object of 10 artists>)
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Matplotlib_39_1

Box Plot

It is a type of chart that depicts a group of numerical data through their quartiles. It is a simple way to visualize the shape of our data. It makes comparing characteristics of data between categories very easy.

  • Box plots are also used to visualize the spread of data.
  • Box plots are used to compare distributions.
  • Box plots can also be used to detect outliers.
  • Syntax: boxplot(x)

Parameter of Box Plot

  • labels: Sets the labels for box plots.
  • notch: Sets to True if notches need to be created around the median.
  • bootstrap: Number set to indicate that notches around the median are bootstrapped.
  • vert: Sets to False for plotting Box plots horizontally. 
x=[1,2,3,4,5] fig=plt.figure(figsize=(20,10)) ax=fig.add_subplot() ax.set(title='Histogram' , xlabel='X-Axis' , ylabel='Y-Axis') ax.boxplot(x , labels=['start'] , notch=False , bootstrap=10000 , vert=True)
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 {'whiskers': [<matplotlib.lines.Line2D at 0x24716eae810>, <matplotlib.lines.Line2D at 0x24716eadd10>], 'caps': [<matplotlib.lines.Line2D at 0x24716eadd50>, <matplotlib.lines.Line2D at 0x24716ed9790>], 'boxes': [<matplotlib.lines.Line2D at 0x24716eacb50>], 'medians': [<matplotlib.lines.Line2D at 0x24716edb390>], 'fliers': [<matplotlib.lines.Line2D at 0x247187d9d10>], 'means': []}
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Matplotlib_41_1

np.random.seed(100) x = 50 + 10*np.random.randn(1000) y = 70 + 25*np.random.randn(1000) z = 30 + 5*np.random.randn(1000) fig = plt.figure(figsize=(8,6)) ax = fig.add_subplot(111) ax.set(title="Box plot with outlier", xlabel='x-Axis', ylabel='Y-Axis') ax.boxplot([x, y, z] , labels=['A', 'B', 'C'] , notch=True , bootstrap=10000) plt.show()
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Matplotlib_42_0

Matplotlib Styles 

plt.style.available
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 '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
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plt.style.use('ggplot')
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plt.style.context('ggplot') >>> <contextlib._GeneratorContextManager at 0x24718952750>
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x = [1, 5, 8, 12, 15, 19, 22, 26, 29] y = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7] with plt.style.context(['dark_background', 'ggplot']): fig = plt.figure(figsize=(8,6)) ax = fig.add_subplot(111) ax.set(title='ggplot' , xlabel='X-Axis' , ylabel='Y-Axis' , xlim=(0, 30) , ylim=(25, 35)) ax.plot(x, y, color='green', linestyle='--', linewidth=3) plt.show()
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Matplotlib_47_0

sepal_len=[6.01,6.94,7.59] sepal_wd=[4.42,3.77,3.97] petal_len=[2.46,5.26,6.55] petal_wd=[1.24,2.33,3.03] species=['setosa','versicolor','virginica'] species_index1=[0.8,1.8,2.8] species_index2=[1.0,2.0,3.0] species_index3=[1.2,2.2,3.2] species_index4=[1.4,2.4,3.4] with plt.style.context('ggplot'): fig = plt.figure(figsize=(9,7)) ax = fig.add_subplot() ax.bar(species_index1 , sepal_len , width=0.2 , label='Sepal Width' ) ax.bar(species_index2 , sepal_wd , width=0.2 , label='Sepal Width' ) ax.bar(species_index3 , petal_len , width=0.2 , label='Petal Length' ) ax.bar(species_index4 , petal_wd , width=0.2 , label='Petal Width' ) ax.set(xlabel='Species' , ylabel='Iris Measurements (cm)' , title='Mean Measurements of Iris Species' , xlim=(0.5,3.7) , ylim=(0,10) , xticks=species_index2 , xticklabels=species )
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Matplotlib_48_0

Custom Style

  • A style sheet is a text file having the extension .mplstyle.
  • All custom style sheets are placed in a folder, stylelib, present in the config directory of matplotlib.
  • Create a file mystyle.mplstyle with the below-shown contents and save it in the folder <matplotlib_configdir/stylelib/.
  • Reload the matplotlib library with the subsequent expression.
  • Use the below expression for knowing the Config folder. 
#Reload the matplotlib library with the subsequent expression. print(matplotlib.get_configdir()) >>> C:\Users\mritu\.matplotlib
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Create a file mystyle.mplstyle with the below-shown contents and save it in the folder <matplotlib_configdir/stylelib/.

  • axes.titlesize : 24
  • axes.labelsize : 20
  • lines.linewidth : 8
  • lines.markersize : 10
  • xtick.labelsize : 16
  • ytick.labelsize : 16

Reload the matplotlib library with the subsequent expression.

matplotlib.style.reload_library()
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print(plt.style.available)
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 ['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
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matplotlibrc file

  • matplotlib uses all the settings specified in matplotlibrc file.
  • These settings are known as rc settings or rc parameters.
  • For customization, rc settings can be altered in the file or interactively.
  • The location of the active matplotlibrc file used by matplotlib can be found below. 
import matplotlib matplotlib.matplotlib_fname() >>> C:\\Users\\mritu\\anaconda3\\Lib\\site-packages\\matplotlib\\mpl-data\\matplotlibrc'
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Matplotlib rcParams

  • All rc settings, present in matplotlibrc file are stored in a dictionary named matplotlib.rcParams.
  • Any settings can be changed by editing the values of this dictionary. 
import matplotlib as mpl mpl.rcParams['lines.linewidth'] = 2 mpl.rcParams['lines.color'] = 'r'
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plt.style.available
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['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
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Subplots

It provides a way to plot multiple plots on a single figure. Given the number of rows and columns, it returns a tuple (fig, ax), giving a single figure fig with an array of axes ax.

  • subplot creates the Axes object at index position and returns it. 
# 'index' is the position in a virtual grid with 'nrows' and 'ncols' # 'index' number varies from 1 to `nrows*ncols`. subplot(nrows, ncols, index)
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fig = plt.figure(figsize=(10,8)) ax1 = plt.subplot(2, 2, 1, title='Plot1') ax2 = plt.subplot(2, 2, 2, title='Plot2') ax3 = plt.subplot(2, 2, 3, title='Plot3') ax4 = plt.subplot(2, 2, 4, title='Plot4') plt.show()
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Matplotlib_59_0

fig = plt.figure(figsize=(10,8)) ax1 = plt.subplot(2, 2, (1,2), title='Plot1') ax1.set_xticks([]) ax1.set_yticks([]) ax2 = plt.subplot(2, 2, 3, title='Plot2') ax2.set_xticks([]) ax2.set_yticks([]) ax3 = plt.subplot(2, 2, 4, title='Plot3') ax3.set_xticks([]) ax3.set_yticks([]) plt.show()
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Matplotlib_60_0

Subplots Using 'GridSpec'

  • GridSpec class of matplotlib.gridspec can also be used to create Subplots.
  • Initially, a grid with a given number of rows and columns is set up.
  • Later while creating a subplot, the number of rows and columns of the grid, spanned by the subplot are provided as inputs to the subplot function. 
import matplotlib.gridspec as gridspec fig = plt.figure(figsize=(10,8)) gd = gridspec.GridSpec(2,2) ax1 = plt.subplot(gd[0,:],title='Plot1') ax2 = plt.subplot(gd[1,0]) ax3 = plt.subplot(gd[1,-1]) plt.show()
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Matplotlib_62_0

fig = plt.figure(figsize=(10,8)) gd = gridspec.GridSpec(3,2) ax1 = plt.subplot(gd[0,:],title='Plot1') ax1.set_xticks([]) ax1.set_yticks([]) ax2 = plt.subplot(gd[1,0],title='Plot1') ax2.set_xticks([]) ax2.set_yticks([]) ax3 = plt.subplot(gd[2,0],title='Plot1') ax3.set_xticks([]) ax3.set_yticks([]) ax4 = plt.subplot(gd[1:,1],title='Plot1') ax4.set_xticks([]) ax4.set_yticks([]) plt.show()
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Matplotlib_63_0

np.random.seed(1500) x=np.random.rand(10) y=np.random.rand(10) z=np.sqrt(x**2+y**2) fig=plt.figure(figsize=(9,7)) fig.suptitle('Main title for all subplot') axes1=plt.subplot(2,2,1,title='Scatter plot with Diamond Markers') axes1.scatter(x, y, s=80, c=z, marker='d') axes1.set(xticks=(0.0,0.5,1.0,1.5), yticks=(-0.2,0.2,0.6,1.0)) axes2=plt.subplot(2,2,2,title='Scatter plot with Circle Markers') axes2.scatter(x, y, s=80, c=z, marker='o') axes2.set(xticks=(0.0,0.5,1.0,1.5), yticks=(-0.2,0.2,0.6,1.0)) axes3=plt.subplot(2,2,3,title='Scatter plot with Plus Markers') axes3.scatter(x, y, s=80, c=z, marker='*') axes3.set(xticks=(0.0,0.5,1.0,1.5), yticks=(-0.2,0.2,0.6,1.0)) axes4=plt.subplot(2,2,4,title='Scatter plot with Upper Triangle') axes4.scatter(x, y, s=80, c=z, marker='^') axes4.set(xticks=(0.0,0.5,1.0,1.5), yticks=(-0.2,0.2,0.6,1.0)) plt.tight_layout()
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Matplotlib_64_0

fig = plt.figure() gs = gridspec.GridSpec(3, 3) ax1 = plt.subplot(gs[:2, :2]) ax2 = plt.subplot(gs[0, 2]) ax3 = plt.subplot(gs[1, 2]) ax4 = plt.subplot(gs[-1, 0]) ax5 = plt.subplot(gs[-1, 1:]) plt.show()
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Matplotlib_65_0

import matplotlib.gridspec as gridspec fig = plt.figure() gs = gridspec.GridSpec(3, 3) ax1 = plt.subplot(gs[0, :]) ax2 = plt.subplot(gs[1, :-1]) ax3 = plt.subplot(gs[1:, -1]) ax4 = plt.subplot(gs[-1, 0]) ax5 = plt.subplot(gs[-1, -2]) plt.show()
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Matplotlib_66_0

Common Pitfalls in Data Visualization

Common pitfalls to be avoided for better Data Visualization are:

  • Creating unlabelled plots.
  • Using 3-dimensional charts. Don't prefer 3-D plots, unless they add any value over 2-D charts.
  • Portions of a pie plot do not sum up to a meaningful number.
  • Showing too many portions in a single pie chart.
  • Bar charts not starting at zero.
  • Failing to normalize the data.
  • Adding extra labels and fancy images.

Best Practices of Data Visualization

A few of the best practices of Data Visualization are:

  • Display the data points on the plot, whenever required.
  • Whenever correlation is plotted, clarify that you have not established any cause of the link between the variables.
  • Prefer labeling data objects directly inside the plot, rather than using legends.
  • Create a visualization, which stands by itself. Avoid adding extra text to tell more about visualization. 
fig=plt.figure() a=fig.add_subplot() a.plot(x, y, 'g^')
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[<matplotlib.lines.Line2D at 0x24719563990>]
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