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    Data Visualization

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    vsy9926
    Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It has various applications like analyzing large datasets faster, aiding decision making, and making sense of complicated data. Popular Python libraries for data visualization include Matplotlib, Seaborn, and Bokeh. Common chart types that can be created using these libraries are line charts, bar charts, pie charts, scatter plots, and histograms. Various parameters can be customized to control aspects like colors, labels, legends, and scales.

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    Data Visualization

    Uploaded by

    vsy9926
    18 views28 pages
    Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It has various applications like analyzing large datasets faster, aiding decision making, and making sense of complicated data. Popular Python libraries for data visualization include Matplotlib, Seaborn, and Bokeh. Common chart types that can be created using these libraries are line charts, bar charts, pie charts, scatter plots, and histograms. Various parameters can be customized to control aspects like colors, labels, legends, and scales.

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    Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It has various applications like analyzing large datasets faster, aiding decision making, and making sense of complicated data. Popular Python libraries for data visualization include Matplotlib, Seaborn, and Bokeh. Common chart types that can be created using these libraries are line charts, bar charts, pie charts, scatter plots, and histograms. Various parameters can be customized to control aspects like colors, labels, legends, and scales.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    18 views28 pages

    Data Visualization

    Uploaded by

    vsy9926
    Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It has various applications like analyzing large datasets faster, aiding decision making, and making sense of complicated data. Popular Python libraries for data visualization include Matplotlib, Seaborn, and Bokeh. Common chart types that can be created using these libraries are line charts, bar charts, pie charts, scatter plots, and histograms. Various parameters can be customized to control aspects like colors, labels, legends, and scales.

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    Download as PDF, TXT or read online from Scribd
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    of 28

    DATA VISUALIZATION

    DATA VISUALIZATION IS THE GRAPHICAL REPRESENTATION OF INFORMATION AND DATA. BY


    USING VISUAL ELEMENTS LIKE CHARTS, GRAPHS, AND MAPS.
    DATA VISUALIZATION
    APPLICATIONS:
    THE MAIN GOAL OF DATA VISUALIZATION IS TO MAKE IT EASIER TO IDENTIFY PATTERNS,
    TRENDS AND OUTLIERS IN
    1. Analyzing
    LARGE the Data in a Better Way
    DATA SETS
    2. Faster Decision Making
    3. Making Sense of Complicated Data

    Fields:
    Sales and marketing.
    Politics.
    Healthcare
    Scientists
    Finance

    Examples:
    sales analysis, market research analysis, customer analysis, defect analysis,
    cost analysis, and Weather forecasting
    DATA VISUALIZATION
    data visualization libraries in Python:
    Matplotlib
    Matplotlib is a Python plotting library that allows you to construct
    static, dynamic, and interactive visualizations
    Matplotlib is a python library which provides many interfaces and
    function to present data in 2D graphics.
    Matplotlib library offers many different collections of sub modules
    Pyplot is one such sub module.
    Pyplot is a collection of methods within Matplotlib library which
    allows user to construct 2D plots easily.
    Other libraries
    Seaborn , Ggplot, Bokeh ,pygal etc
    DATA VISUALIZATION

    To use Pyplot for data visualization, we have


    to first import it in our python environment.
    import matplotlib.pyplot
    or
    import matplotlib.pyplot as plt
    DATA VISUALIZATION
    Line charts are used to represent the relation between two data X and Y on a different axis .
    A line chart is a type of chart which displays information as a series of data points connected by
    straight line .
    To create a line chart plot() function is used .
    DATA VISUALIZATION
    Create multiple line chart on a single plot : Multiple charts can be made using plot()
    DATA VISUALIZATION
    Various parameters of the plot() function :
    1. Marker – different types of marker
    2. Markersize e or ms
    DATA VISUALIZATION
    Various parameters of the plot() function :
    3. Markerfacecolor or mfc - ’b’,’r’,’g’ etc
    4. Markeredgecolor or mec - ’b’,’r’,’g’
    DATA VISUALIZATION
    Note : Marker type and marker color can be combined :
    For Ex- ‘r+’ gives you line color and marker color red
    and marker type +
    DATA VISUALIZATION
    Various parameters of the plot() function :
    1. Color –’r’,’g’,’b’
    2. Linestyle or ls
    3. Linewidth : specify the width in points
    DATA VISUALIZATION
    The legend is a side section of the chart that gives a small text description of each series. You can specify the text
    associated with each series in this legend, and specify where on the chart it should appear.
    Matplotlib.pyplot.legend() function is used to create the
    legend

    To specify the position of the legend on the chart Loc parameter can be change
    DATA VISUALIZATION
    Matplotlib.pyplot.title() function can be used to display the title of the chart
    Matplotlib.pyplot.savefig() function can be used to save the chart in jpeg or pdf format after plotting data
    DATA VISUALIZATION
    matplotlib.pyplot.xticks() and yticks() Function: by default pylpot will automatically decide which
    data points will have ticks on the x axis or y axis but you can also decide which data points will
    have the tick marks on X and Y axis.
    DATA VISUALIZATION
    matplotlib.pyplot.xlim() and ylim() Function: using this function you can specify the ranges for X
    and Y
    Note axisthe data values mapping on xlimits and ylimits will get plotted . If no data value maps to the
    : only
    xlimits and ylimits nothing will print
    DATA VISUALIZATION
    A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with
    heights or lengths proportional to the values that they represent
    Python provides the bar() function to create bar chart
    DATA VISUALIZATION
    Multiple bar charts :
    DATA VISUALIZATION
    Customizing bar charts : Change color, width of charts
    All bars with same width
    DATA VISUALIZATION
    Customizing bar charts : All bars with different width : A list of width can be
    passed
    DATA VISUALIZATION
    Customizing bar charts : Change the color of bar

    Change the color of each bar pass a list of colors


    DATA VISUALIZATION
    Multiple bar separately
    DATA VISUALIZATION
    Multiple bar chart : Stacked Bar Chart
    DATA VISUALIZATION
    Horizontal bar chart: to create horizontal bar chart barh() function can be used
    DATA VISUALIZATION
    Histogram:
    1. Histogram was introduced by karl Pearson
    2. A histogram is a graphical representation that organizes a group of data points into user-specified ranges.
    3. A histogram is a bar graph-like representation of data that buckets a range of outcomes into
    columns along the x-axis.
    4. The y-axis represents the number count or percentage of occurrences in the data for each column and can
    be used to visualize data distributions.
    DATA VISUALIZATION
    hist() function is used to create histogram
    Default value of bin is 10
    DATA VISUALIZATION
    Bin=[1,2,3,4]
    Then it has 3 bins
    First bin is [1-2] excluding 2
    Second bin is [2-3] excluding 3
    Third bin is [3-4 ] including 4
    Hist( ) Function Syntax :
    <matplotlib.pyplot>.hist(x, bins = None, cumulative = Flase, histtype = 'bar', align = 'mid', orientation= 'vertical’)

    Different parameters can be changed to customize the Histogram


    X: Array or sequence of arrays to be plotted on Histogram

    bins: an integer or sequence, used to divide the range in the histogram

    cumulative: True or False, Default is False

    histtype: {'bar', 'barstacked', 'step', 'stepfilled'}, Default 'bar’


    align: {'left', 'mid', 'right'}, default is 'mid’

    orientation: {'horizontal', 'vertical'}, Default 'horizontal'


    DATA VISUALIZATION
    Cumulative =True/False by default it is
    False
    DATA VISUALIZATION
    Histtype = {'bar', 'barstacked', 'step', 'stepfilled'}, Default 'bar’
    DATA VISUALIZATION
    orientation: {'horizontal', 'vertical'}, Default ‘Vertical’

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