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

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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#import library
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

#get matplotlib version
matplotlib.__version__
>>> '3.8.0'

#upgrade matplotlib
!pip install --upgrade matplotlib

    Requirement already satisfied: matplotlib in c:\users\mritu\anaconda3\lib\site-packages (3.8.0)
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    Requirement already satisfied: cycler>=0.10 in c:\users\mritu\anaconda3\lib\site-packages (from matplotlib) (0.11.0)
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    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)

#get matplotlib version after upgrade
matplotlib.__version__
>>> '3.8.0'

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>

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()

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

#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()

#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()

#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()

#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()

#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()

Types Of Plot

• Line plot
• Scatter plot
• Bar plot
• Pie plot
• Histogram
• Box 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()

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()

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--')

``` [,
]

![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()

<matplotlib.legend.Legend at 0x24718801890>

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) 

# height: Sets the height of bars
barh(y,height)

#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()

#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()

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")

<ErrorbarContainer object of 3 artists>

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%%')

    ([<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%')])

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')

    (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>)

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)

    {'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': []}

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()

Matplotlib Styles

plt.style.available

     '_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']

plt.style.use('ggplot')

plt.style.context('ggplot')
>>> <contextlib._GeneratorContextManager at 0x24718952750>

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()

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
          )

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

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()

print(plt.style.available)

 ['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']

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'

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'

plt.style.available

['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']

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)

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()

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()

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()

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()

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()

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()

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()

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^')

[<matplotlib.lines.Line2D at 0x24719563990>]