<h5 id="4-1-introduction">4. 1 Introduction</h5> <ul> <li>Data visualization is the graphical or pictorial representation of data using graphs, charts, etc.</li> <li>It helps to understand variations in data and show relationships between variables, aiding in the communication of information.</li> <li>Examples of visualization include traffic symbols, ultrasound reports, and speedometers.</li> <li>It is effectively used in various fields such as health, finance, science, mathematics, and engineering.</li> <li>With Python’s Matplotlib library, one can visualize data through line, bar, and scatter charts.</li> </ul> <p>======</p> <h5 id="42-plotting-using-matplotlib">4.2 Plotting using Matplotlib</h5> <ul> <li>The <code>plot()</code> function is used to create a line plot or marker plot of x versus y.</li> <li>The <code>bar()</code> function is used to create a bar plot with the parameter ‘height’ being the height of the bars and optional parameters for width, bottom position, alignment, and data.</li> <li>The <code>boxplot()</code> function is used to create a box and whisker plot with optional parameters for notch, symmetry, vertical orientation, and more.</li> <li>The <code>hist()</code> function is used to create a histogram with optional parameters for number of bins, range, density, weights, etc.</li> <li>The <code>pie()</code> function is used to create a pie chart with optional parameters for explode, labels, colors, and more.</li> <li>The <code>scatter()</code> function is used to create a scatter plot of x versus y with optional parameters for size, color, marker, colormap, and normalization.</li> </ul> <p>======</p> <h5 id="43-customisation-of-plots">4.3 Customisation of Plots</h5> <ul> <li>The text provides a summary of Program 4-2, which uses the matplotlib library to plot a line chart of date versus temperature.</li> <li>The date and temperature data are stored in the date and temp lists, respectively.</li> <li>The plt.plot(date, temp) function is used to generate the line chart.</li> <li>The x-axis label, y-axis label, and chart title are added using the plt.xlabel(), plt.ylabel(), and plt.title() functions, respectively.</li> <li>The plt.grid(True) function is used to add gridlines to the chart.</li> <li>The plt.yticks(temp) function is used to set the tick labels for the y-axis to the temperature values.</li> <li>The chart is displayed using the plt.show() function.</li> <li>The use of xlabel, ylabel, title, and yticks functions in the example provides more meaning to the chart compared to Figure 4.2.</li> </ul> <p>======</p> <h5 id="431-marker">4.3.1 Marker</h5> <ul> <li>In matplotlib, markers are used to represent data values in a line chart or scatter plot.</li> <li>A list of markers along with their corresponding symbols and descriptions is provided in Table 4.3.</li> <li>Markers can be specified in the plot() function to represent each point in the line.</li> <li>The use of markers can make plots more informative and visually appealing.</li> <li>The choice of marker depends on the type of data and the desired visual representation.</li> </ul> <p>======</p> <h5 id="432-colour">4.3.2 Colour</h5> <ul> <li>The text introduces basic color terminology.</li> <li>The color names are: blue (b), green (g), red (r), cyan (c), magenta (m), yellow (k), black (w), and white.</li> <li>Combinations of these colors can produce other colors, such as white (w = k + y) and black (w = b + g + r).</li> <li>The color white can be created by combining all colors, while black can be created by not using any color (w = c + m + y + k = 0).</li> <li>The color white can also be represented as the absence of color (w = 1), and black can be represented as the presence of all colors (w = 0).</li> </ul> <p>======</p> <h5 id="433-linewidth-and-line-style">4.3.3 Linewidth and Line Style</h5> <ul> <li>The linewidth and linestyle property can be used to change the width and style of a line chart.</li> <li>Linewidth is specified in pixels, with a default of 1 pixel (thin line). A number greater than 1 will output a thicker line depending on the value provided.</li> <li>The linestyle parameter can take a string such as “solid”, “dotted”, “dashed” or “dashdot” to set the line style of a line chart.</li> <li>An example of changing linewidth and linestyle is provided in Program 4-3, which plots a line chart with a dashed line style and a linewidth of 2 pixels.</li> <li>The program uses the matplotlib and pandas libraries to create a line chart of average weight against average height, with customizations such as green color, marker style, and labels.</li> <li>Height and weight are examples of continuous data, which can be measured in decimals, while discrete data (e.g., total number of students in a class) can never be in decimals.</li> </ul> <p>======</p> <h5 id="44-the-pandas-plot-function-pandas-visualisation">4.4 The Pandas Plot function (Pandas Visualisation)</h5> <ul> <li>The <code>plot()</code> function is used to create various types of charts in Python with respect to the data stored in DataFrames.</li> <li>The different types of charts that can be created include line plot, bar plot (vertical), bar plot (horizontal), histogram, box plot, area plot, pie plot, and scatter plot.</li> <li>Line plot is the default plot and is used to display data as a series of data points connected by straight line segments.</li> <li>Bar plot (vertical) is used to display data in the form of vertical bars where the length of each bar is proportional to the value it represents.</li> <li>Bar plot (horizontal) is used to display data in the form of horizontal bars where the length of each bar is proportional to the value it represents.</li> <li>Histogram is used to display the distribution of numerical data.</li> <li>Box plot is used to show the distribution of quantitative data in a way that facilitates comparisons between variables or across levels of a categorical variable.</li> <li>Area plot is similar to line plot, but the area under the line is filled in.</li> <li>Pie plot is used to display data in the form of a pie chart, which shows the proportion of whole categories.</li> <li>Scatter plot is used to display data as a collection of points.</li> </ul> <p>======</p> <h5 id="441-plotting-a-line-chart">4.4.1 Plotting a Line chart</h5> <ul> <li>The text provides instructions to create a line chart depicting sales for three weeks.</li> <li>The chart title should be “Mela Sales Report”.</li> <li>The x-axis label should be “Days” and the y-axis label should be “Sales in Rs”.</li> <li>The line colors should be red for week 1, blue for week 2, and brown for week 3.</li> <li>No equations or specific sales data is provided in the text.</li> <li>The required libraries for this task are pandas and matplotlib.</li> </ul> <p>======</p> <h5 id="reads-melasalescsv-to-df-by-giving-path-to-the-file">reads “MelaSales.csv” to df by giving path to the file</h5> <ul> <li>The text discusses reading data from a CSV file into a DataFrame using pandas.</li> <li>The DataFrame, named “df”, contains data from a file named “MelaSales.csv”.</li> <li>A line plot is created using the DataFrame, with a different color for each week.</li> <li>The line plot is created using the “df.plot()” function, with “kind” set to “line” and “color” set to a list of three colors.</li> <li>The colors assigned to each week in the line plot are red, blue, and brown.</li> </ul> <p>======</p> <h5 id="set-title-to-mela-sales-report">Set title to “Mela Sales Report”</h5> <ul> <li>The text provided is related to setting the title of a plot for a sales report.</li> <li>The title chosen for the sales report is “Mela Sales Report”.</li> <li>The plot title is set using the ‘plt.title()’ function.</li> <li>The title is enclosed in single quotes within the function.</li> <li>No equations or formulae are provided in the text.</li> <li>The text does not contain any examples or their solutions.</li> </ul> <p>======</p> <h5 id="label-x-axis-as-days">Label x axis as “Days”</h5> <ul> <li>The text provides instructions for customizing a line plot using the matplotlib and pandas libraries in Python.</li> <li>The x-axis is labeled as “Days” and the y-axis as “Sales in Rs” using the plt.xlabel() and plt.ylabel() functions.</li> <li>The plt.xticks() function is used to substitute the x-axis ticks with a list of values of the user’s choice.</li> <li>The data for the plot is read from a CSV file named “MelaSales.csv” using the pd.read_csv() function.</li> <li>The plot is customized with a marker, marker size, linestyle, and linewidth using the df.plot() function and the plt.title() function is used to add a title to the plot.</li> <li>The x-axis ticks are displayed as day names by converting the index of the DataFrame to a list and using the plt.xticks() function.</li> </ul> <p>======</p> <h5 id="442-plotting-bar-chart">4.4.2 Plotting Bar Chart</h5> <ul> <li>The text provides information about labeling the x-axis in a bar plot.</li> <li>If no column is specified for the x parameter in the plot(), it plots all DataFrame columns with the index (row label) of the DataFrame at the x-axis, starting from 0.</li> <li>A bar plot is generated using the ‘MelaSales.csv’ file with the ‘Day’ column on the x-axis.</li> <li>The Python scripts for this are demonstrated in Program 4-6.</li> <li>The plot is shown in Figure 4.7, but it is not provided in the text.</li> </ul> <p>======</p> <h5 id="plots-a-bar-chart-with-the-column-days-as-x-axis">plots a bar chart with the column “Days” as x axis</h5> <ul> <li>The text pertains to data plotting using a bar chart.</li> <li>The ‘Day’ column is used for the x-axis of the bar chart.</li> <li>The title of the bar chart is ‘Mela Sales Report’ and it is colored with red, yellow, and purple.</li> <li>The y-axis represents ‘Sales in Rs’.</li> <li>The plot is displayed using matplotlib library in Python.</li> <li>There is no mention of bins or histogram in the text.</li> </ul> <p>======</p> <h5 id="443-plotting-histogram">4.4.3 Plotting Histogram</h5> <ul> <li> <p>A histogram is a type of column chart where each column represents a range of values, and the height of a column corresponds to the number of values in that range.</p> </li> <li> <p>To create a histogram, data is divided into “bins”, and the number of data points in each bin is counted. The height of each column in the histogram is proportional to the number of data points its bin contains.</p> </li> <li> <p>The df.plot(kind=‘hist’) function in Python automatically selects the size of the bins based on the spread of values in the data. However, it is also possible to set a value for the bins parameter.</p> </li> <li> <p>Histograms can be customized by changing properties such as edgecolor, line style, line width, fill, and hatch.</p> </li> <li> <p>Histograms can be used to plot open data, such as the minimum and maximum temperature data available on the “Open Government Data (OGD) Platform India” website, to observe the number of times a particular temperature has occurred.</p> </li> </ul> <p>======</p> <h5 id="extract-y-frequency-and-edges-bins">Extract y (frequency) and edges (bins)</h5> <ul> <li>The text uses the numpy library’s <code>np.histogram()</code> function to calculate the frequency (y) and edges (bins) of a data array.</li> <li>The midpoint for each bar on the histogram is calculated as the average of the edges.</li> <li>A histogram plot is created using matplotlib’s <code>plt.plot()</code> function with the ‘hist’ kind and the ‘ANNUAL - MIN’ variable.</li> <li>The calculated midpoints and frequency are then plotted on the histogram as well.</li> <li>A title, x-axis label, and show command are used to display the histogram plot.</li> </ul> <p>======</p> <h5 id="444-plotting-scatter-chart">4.4.4 Plotting Scatter Chart</h5> <ul> <li>A scatter chart is a two-dimensional data visualization method that uses dots to represent the values obtained for two different variables.</li> <li>It shows the relationship between two variables and can also represent a third or fourth variable through the size, shape, or color of the dots.</li> <li>Program 4-12 uses a scatter plot to show the relationship between the discount offered and sales made by Prayatna.</li> <li>The size of the bubble in a scatter plot can be used to reflect a value, as shown in Program 4-13 where the size of the bubble is 10 times the discount.</li> <li>Using df.plot(kind=‘scatter’) instead of plt.scatter() in Program 4-13 would create a scatter plot with the data in a dataframe, with the default markers and colors.</li> </ul> <p>======</p> <h5 id="445-plotting-quartiles-and-box-plot">4.4.5 Plotting Quartiles and Box plot</h5> <ul> <li>The text presents an example of using boxplot to analyze and compare the ratings of 3 resorts over the past 5 years.</li> <li>Program 4-15 is used to read the CSV file, convert it into a DataFrame, and plot a boxplot for the DataFrame with a title.</li> <li>The boxplot provides a visual representation of the distribution of ratings for each resort, including the median, quartiles, and potential outliers.</li> <li>The modified Program 4-15 displays the whisker in a horizontal direction, allowing for easier comparison of the range of ratings for each resort.</li> <li>Based on the boxplot, Resort 1 has the highest median rating and the smallest range of ratings, making it a potential candidate for the award.</li> </ul> <p>Note: The text does not provide enough information to definitively determine which resort should be awarded. Further analysis, such as considering the number of ratings for each resort, may be necessary.</p> <p>======</p>