Plotting Data using Matplotlib Chapter Notes | Informatics Practices for Class 12 - Humanities/Arts PDF Download

Introduction

• Data visualisation involves graphical or pictorial representation of data using graphs, charts, etc., to visualise variation or show relationships between variables.
• Visualisation helps in better understanding of analysis results, especially when inferring from raw results is challenging.
• It facilitates effective communication of information to intended users.
• Examples of visualisation in daily life include traffic symbols, ultrasound reports, atlas maps, speedometers, and instrument tuners.
• Data visualisation is widely used in fields such as health, finance, science, mathematics, and engineering

Plotting using Matplotlib

• Matplotlib is a Python library used for creating static, animated, and interactive 2D plots or figures.
• It can be installed using the command: pip install matplotlib.
• The Pyplot module of Matplotlib is imported using: import matplotlib.pyplot as plt, where plt is an alias for matplotlib.pyplot.
• The Pyplot module contains functions to work on plots, such as creating figures, plotting areas, lines, and decorating plots with labels.
• A figure is the overall window where Pyplot outputs are plotted, containing components like the plotting area, legend, axis labels, ticks, and title.
• The plot() function creates a figure, and plt.plot(x, y) plots x versus y.
• The show() function displays the created figure.
• Charts should include a title, axis labels, and a legend (if multiple datasets are plotted) to ensure clarity.
• By default, plot() creates a line chart, but other plot types like bar, boxplot, histogram, pie, and scatter are supported, as listed in Table 4.1.
• Plots can be saved as images using the save button in the output window or the savefig() function, e.g., plt.savefig('x.png').

Customisation of Plots

• grid(): Configures grid lines.
• legend(): Places a legend on the axes.
• savefig(): Saves the current figure.
• show(): Displays all figures.
• title(): Sets a title for the axes.
• xlabel(): Sets the label for the x-axis.
• xticks(): Sets tick locations and labels for the x-axis.
• ylabel(): Sets the label for the y-axis.
• yticks(): Sets tick locations and labels for the y-axis.

Marker

• Markers can be specified in the plot() function to highlight data points.
• Table 4.3 lists markers, including:
  • .: Point
  • ,: Pixel
  • o: Circle
  • v: Triangle down
  • ^: Triangle up
  • 8: Octagon
  • s: Square
  • p: Pentagon
  • *: Star
  • h: Hexagon1
  • H: Hexagon2
  • +: Plus
  • x: X
  • D: Diamond

Colour

• b: Blue
• g: Green
• r: Red
• c: Cyan
• m: Magenta
• y: Yellow
• k: Black
• w: White

Linewidth and Line Style

• solid
• dotted
• dashed
• dashdot

The Pandas Plot Function (Pandas Visualisation)

• Starting from version 0.17.0, Pandas Series and DataFrame objects have a built-in plot() method, which wraps around Matplotlib’s plot() function.
• For a Series s or DataFrame df, the plot can be called using s.plot() or df.plot().
• The plot() method accepts arguments to customise plots, with the kindparameter specifying the plot type, as listed in Table 4.5:
  • line: Line plot (default)
  • bar: Vertical bar plot
  • barh: Horizontal bar plot
  • hist: Histogram
  • box: Boxplot
  • area: Area plot
  • pie: Pie plot
  • scatter: Scatter plot
• Matplotlib’s Pyplot methods and functions can be used alongside Pandas’ plot() method.

Plotting a Line Chart

• A line plot displays the frequency of data along a number line, suitable for continuous datasets.
• It is used to visualise growth or decline in data over a time interval.
• Pandas’ plot(kind='line') can plot line charts from DataFrame data, with customisations like colour, markers, and line styles.
• The x-axis typically uses the DataFrame’s index, and custom tick labels can be set using plt.xticks(ticks, labels).

Plotting Bar Chart

• Bar charts are used for comparing data across categories, capable of plotting strings on the x-axis.
• They are created using df.plot(kind='bar'), with the option to specify columns for x and y axes.
• Customisations include changing bar colours, edge colours, line styles, and line widths.

Plotting Histogram

• Histograms are column charts where each column represents a range of values, and the height corresponds to the number of values in that range.
• Data is sorted into bins, and the height of each column is proportional to the number of data points in the bin.
• The df.plot(kind='hist') function automatically selects bin sizes based on data spread.
• Customisations include setting bin numbers, edge colour, line style, line width, fill (boolean), and hatch patterns (e.g., -, +, x, o).

Using Open Data

• Open data refers to freely available datasets for public use, primarily for educational purposes, promoting transparency, accessibility, and innovation.
• The “Open Government Data (OGD) Platform India” (data.gov.in) provides large datasets on various projects and parameters.
• Histograms can be plotted for open datasets, such as temperature data, to observe frequency distributions.

Plotting Scatter Chart

• Scatter charts are two-dimensional plots using dots to represent values of two variables, one on the x-axis and one on the y-axis.
• They are used to show relationships or correlations between variables, sometimes called correlation plots.
• Dot size, shape, or colour can represent additional variables.
• Customisations include changing marker type, size, colour, edge colour, and line width.

Plotting Quartiles and Box Plot

• Quartiles divide data into four equal parts, each containing an equal number of observations, requiring median calculation.
• They are used in educational, sales, and survey data to divide populations into groups, e.g., identifying the top 25% of students.
• A box plot visualises a statistical summary, including minimum value, quartile 1, quartile 2 (median), quartile 4, maximum value, and whiskers extending to the highest and lowest values.
• Box plots identify outliers, which are observations numerically distant from the rest of the data.
• The distance between the box and whiskers indicates data variation; shorter distances suggest small variation, and longer distances suggest larger variation.

Plotting Pie Chart

• Pie charts are circular graphs divided into sectors, each representing a proportion of the whole, used for numerical data.
• They are created using df.plot(kind='pie', y='column'), with default labels from the DataFrame’s index.
• Customisations include:
  • explode: Specifies the fraction of the radius to offset each sector.
  • autopct: Displays the percentage of each sector as a label.
  • colors: Changes the colour of each sector.
  • legend: Can be set to False to hide the legend.

Summary

• A plot (also called a graph or chart) visually represents a dataset to show relationships between two or more variables.
• The Pyplot module is imported using import matplotlib.pyplot as plt, where plt is an alias.
• Pyplot functions create figures, plotting areas, lines, bars, histograms, etc., and decorate plots with labels.
• Plot components include title, legend, ticks, x-label, and y-label.
• plt.plot() builds a plot, and plt.show() displays it.
• plt.xlabel() and plt.ylabel() set axis labels, and plt.title() sets the plot title.
• Data can be plotted directly from a DataFrame using Pandas’ plot() function.
• The format for DataFrame plotting is df.plot(kind=''), where kind can be line, bar, hist, scatter, or box.