Plotting with Pyplot

Both A and R are true and R is the correct explanation of A.

Explanation
The Matplotlib library is used for data visualization in Python, providing a variety of tools and functionalities for creating different types of plots, charts, and graphs. The Pyplot module, which is a collection of methods within the Matplotlib library, allows users to construct 2D plots easily and interactively.

Both A and R are true but R is not the correct explanation of A.

Explanation
The scatter chart is a graph of plotted points on two axes that show the relationship between two sets of data. On the other hand, a line chart, or line graph, is a type of chart that displays information as a series of data points called 'markers' connected by straight line segments.

Both A and R are true and R is the correct explanation of A.

Explanation
Both the scatter() and plot() functions in Pyplot can create scatter charts. The plot() function in Pyplot can create both line charts and scatter charts. When specifying marker styles without providing a linestyle argument, the plot() function will create a scatter chart.

A is false but R is true.

Explanation
We can create various charts using plot(), scatter(), bar(), and barh() for the same datasets, but not using pie(). The pie() function specifically works with a single set of data, whereas the other functions can handle multiple datasets or series.

Both A and R are true and R is the correct explanation of A.

Explanation
The five-point statistical summary of a dataset can be visually represented through a box plot. A box plot is used to display the range and middle half of ranked data. It uses five important numbers from the data range: the extremes (highest and lowest numbers), the median, and the upper and lower quartiles, comprising the five-number statistical summary.

A is true but R is false.

Explanation
A line graph is a tool for comparison, created by plotting a series of data points called 'markers' and connecting them with straight lines. This makes it easier to compare different data points and observe patterns. Line charts are suitable for continuous data sets, displaying information as a series of data and not restricted to discontinuous data sets.

Data visualization basically refers to the graphical or visual representation of information and data using visual elements like charts, graphs, maps and so forth.

The Python library generally used for data visualization is Matplotlib library.

Yes, Pyplot is a Python library. Pyplot is a collection of methods within matplotlib library which allows user to construct 2D plots easily and interactively.

The barh() function is used to create a horizontal bar chart.

(i) For changing the width of the line, we use the linewidth argument with the plot() function as: <matplotlib.pyplot>.plot(<data1>, [,data2], linewidth = <width> )

(ii) For changing the color of the line, we use the color argument with the plot() function as: <matplotlib.pyplot>.plot(<data1>, [,data2], <color code>)

The data points being plotted on a graph/chart are called markers. To change the marker type and color, we use following additional optional arguments in plot function : marker = <valid marker type>, markeredgecolor = <valid color>.

With Pyplot, a histogram is created using hist() function.

No, bar charts and histograms are not same. 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. On the other hand, a histogram is a type of graph that provides a visual interpretation of numerical data by indicating the number of data points that lie within a range of values.

The types of histogram plots that we can create using Pyplot are cumulative histogram, step type histogram, stacked bar type histogram, horizontal histogram.

A frequency polygon is a type of frequency distribution graph. In a frequency polygon, the number of observations is marked with a single point at the midpoint of an interval.

The box plot is used to show the range and the middle half of the ranked data.

Pyplot module's boxplot() function allows to create box plots.

matplotlib

Reason — The library imported to draw charts in Python is Matplotlib.

matplotlib

Reason — PyPlot is an interface of Python's Matplotlib library that allows users to construct 2D plots easily and interactively.

matplotlib.pyplot

Reason — The matplotlib.pyplot interface is commonly used for 2D plotting in Python using the Matplotlib library.

statistical, box

Reason — Pie charts and histograms are valid chart types used for data visualization. Statistical and box plots are not valid chart types.

line()

Reason — The line() function is not a valid plotting function of Pyplot. In Matplotlib's Pyplot module, the correct function for creating line plots is plot().

pie()

Reason — The pie() function in Matplotlib's Pyplot module can plot only one data sequence. On the other hand, functions like plot(), bar(), and barh() can plot multiple data series in a single chart.

line

Reason — A line chart or line graph is a type of chart which displays information as a series of data points called "markers" connected by straight line segments.

scatter

Reason — A scatter chart is a graph of plotted points on two axes that show the relationship between two sets of data.

histogram

Reason — A histogram is a summarization tool for discrete or continuous data. A histogram provides a visual interpretation of numerical data by indicating the number of data points that lie within a range of values.

box plot

Reason — A box plot provides a visual representation of the statistical five-number summary of a given dataset. It includes the highest and lowest numbers, the median, and the upper and lower quartiles.

plot()

Reason — The plot() function is used to create a line chart or line graph.

bar(), barh()

Reason — With Pyplot, a bar chart is created using bar() and barh() functions.

bar()

Reason — The bar() function will create a vertical bar chart.

barh()

Reason — The barh() function will create a horizontal bar chart.

linestyle

Reason — To specify the style of the line as dashed in Matplotlib's plot() function, we need to set the linestyle argument to 'dashed'.

markers

Reason — The data points being plotted on a graph/chart are called markers.

line

Reason — A line chart or line graph is a type of chart which displays information as a series of data points called "markers" connected by straight line segments.

linestyle

Reason — When creating scatter charts using Matplotlib's plot() function, the linestyle argument is skipped because scatter plots do not use line styles.

s

Reason — The s argument in scatter() is used to specify the size of data points.

width

Reason — The width argument allows to control the thickness of the bars in a bar chart created using the bar() function in Matplotlib.

width

Reason — The width argument with a float value is used to change the width of bars in a bar chart created using the bar() function in Matplotlib.

title()

Reason — The title() function sets the title of the plot in Matplotlib.

plt.title()

Reason — The plt.title() command is used in Matplotlib's Pyplot module to give a heading or title to a graph.

xlim()

Reason — The xlim() function is used to set the limits for the x-axis of a plot in Matplotlib.

legend()

Reason — The legend() function in Matplotlib's Pyplot module is used to show legends in a plot.

label

Reason — The label argument must be set with plotting functions for the legend() function to display the legends correctly.

hist()

Reason — The hist() function is used to create a histogram.

histtype

Reason — The histtype argument in Matplotlib's hist() function is used to create a stacked bar type histogram. Setting histtype = 'barstacked' creates a histogram where bars for each bin are stacked on top of each other, representing different categories or subgroups within the data.

pie()

Reason — The pie() function in Matplotlib's Pyplot module can plot only one data series. On the other hand, functions like plot(), bar(), and boxplot() can plot multiple data series in a single chart.

explode

Reason — The explode argument is used in pie charts to visually separate one or more wedges from the rest of the pie chart.

autopct

Reason — To view the percentage of share in a pie chart, we need to add an argument autopct with a format string, such as "%1.1F%%".

boxplot()

Reason — The boxplot() function is used to create box plots in Matplotlib.

patch_artist

Reason — The patch_artist argument in the boxplot() function is used to create a filled box plot. When set to True, it fills the boxes of the box plot with a color, making them more visually distinct.

True

Reason — PyPlot is a sub-library of Matplotlib library. It allows users to construct 2D plots easily and interactively. Pyplot essentially reproduces plotting functions and behavior of MATLAB.

False

Reason — The statement import matplotlib.pyplot is the valid and commonly used way to import the PyPlot submodule from the Matplotlib library.

True

Reason — By default, a pie chart is printed in an elliptical or oval shape.

False

Reason — By default, a pie chart is printed in an elliptical or oval shape. It can be changed to a circle by using the axis() function of Pyplot and passing the 'equal' argument to it.

False

Reason — In Matplotlib's Pyplot library, the function used to plot a line chart is plot().

True

Reason — In Matplotlib's Pyplot library, the function used to plot a line chart is plot().

True

Reason — In Matplotlib's Pyplot library, the function used to plot a bar chart is bar().

True

Reason — The barh() function in Matplotlib's Pyplot library is used to create horizontal bar charts.

False

Reason — It is possible to plot multiple series of values in the same bar graph using Matplotlib's Pyplot library because the bar() and barh() functions support handling multiple datasets.

True

Reason — A standard marker for representing missing or non-number data in Python libraries is NaN (Not a Number).

True

Reason — When both the linestyle argument and the marker argument (markerstyle-string) are not specified in the plot() function, the resulting chart can resemble a scatter plot. In this case, the points will be plotted without connected lines, similar to how a scatter plot displays data points.

False

Reason — The bar() function in Matplotlib's Pyplot library can create vertical bar charts, while the barh() function creates horizontal bar charts.

False

Reason — The pie() function in Matplotlib's Pyplot module can plot only one data series.

False

Reason — The plot appearance can be affected by the data series being plotted, but it can also be influenced by functions such as xlim() which determine the range of values shown on the x-axis.

True

Reason — When a frequency polygon is drawn manually, it is based on the data that would be used to create a histogram.

No library needs to be imported to create charts in Python language.

Reason — To create charts and visualizations in Python, we need to import libraries such as Matplotlib.

PyPlot is an interface provided by the Matplotlib library.

The statement to import PyPlot in our script is as follows:

import matplotlib.pyplot as plt

(a) line chart: plot() function

(b) bar chart: bar() function

(c) horizontal bar chart: barh() function

(d) histogram: hist() function

(e) scatter chart: scatter() function

(j) boxplot: boxplot() function

(g) pie chart: pie() function

A line chart, or line graph, is a type of chart that displays information as a series of data points called 'markers' connected by straight line segments.

The scatter chart is a graph of plotted points on two axes that show the relationship between two sets of data. With a scatter plot, a mark or marker (usually a dot or small circle), represents a single data point. With one mark (point) for every data point a visual distribution of the data can be seen. Depending on how tightly the points cluster together, we may be able to discern a clear trend in the data.

The difference is that with a scatter plot, the decision is made from the data points such that the individual points should not be connected directly together with a line but, instead express a trend.

A pie chart is used to show parts in relation to the whole, often representing percentage shares and numerical proportions.

A bar graph / bar chart is a graphical display of data using bars of different heights.

Compared to a line chart, which connects data points with lines, a bar chart is useful for comparing discrete categories rather than showing continuous trends over time. Bar charts are effective for highlighting differences in values between categories and are particularly useful when dealing with categorical data or comparing data across different groups or time periods.

A histogram is a summarization tool for discrete or continuous data. A histogram provides a visual interpretation of numerical data by indicating the number of data points that lie within a range of values (called "bins"). Histograms are a great way to show results of continuous data, such as: weight, height, how much time, and so forth.

A boxplot is a graphical representation of the distribution of a dataset through five summary statistics: the extremes (the highest and the lowest numbers), the median, and the upper and lower quartiles.

Box plots are suitable for visualizing the spread of data, identifying outliers, comparing data distribution between different groups or categories, and assessing symmetry in a dataset.

A frequency polygon is a type of frequency distribution graph. In a frequency polygon, the number of observations is marked with a single point at the midpoint of an interval. A straight line then connects each set of points. Frequency polygons make it easy to compare two or more distributions on the same set of axes.

The functions to label axes in a plot using Matplotlib's Pyplot library are xlabel() for the x-axis and ylabel() for the y-axis.

The function title() in Matplotlib's Pyplot library is used to add a title to a plot.

The figure() function in Matplotlib's Pyplot library is used to set figure size of a plot.

The function to set limits for the axes in a plot using Matplotlib's Pyplot library is xlim() for the x-axis and ylim() for the y-axis.

The legend() function in Matplotlib's Pyplot library is used to display a legend on the plot.

The functions to add ticks on axes in a plot using Matplotlib's Pyplot library are xticks() for the x-axis and yticks() for the y-axis.

Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization techniques or tools such as line chart, bar chart, pie chart, histogram, scatter chart etc. Thus with data visualization tools, information can be processed in efficient manner and hence better decisions can be made.

Python supports data visualizations by providing some useful libraries for visualization. Most commonly used data visaulization library is matplotlib. Matplotlib is a Python library, also sometimes known as the plotting library. The matplotlib library offers very extensive range of 2D plot types and output formats. It offers complete 2D support along with limited 3D graphic support. It is useful in producing publication quality figures in interactive environment across platforms. It can also be used for animations as well. There are many other libraries of Python that can be used for data Visualization but matplotlib is very popular for 2D plotting.

For data visualization in Python, the matplotlib library's Pyplot interface is used. Matplotlib is a Python library that provides interfaces and functionalities for 2D graphics, similar to MATLAB's in various forms. It offers both a quick way to visualize data in Python and creates publication-quality figures in many formats. The Matplotlib library offers various named collections of methods. Pyplot, as one such interface, enables users to construct 2D plots easily and interactively.

The popular ways of plotting data include line charts, bar charts, histograms, scatter plots, pie charts, box plots.

In a chart/graph, there may be multiple datasets plotted. To distinguish among various datasets plotted in the same chart, legends are used. Legends can be different colors/patterns assigned to different specific datasets. The legends are shown in a corner of a chart/graph.

Using legend() function without labels results in default labels (e.g., "line 1," "line 2"). This can confuse viewers as it lacks meaningful information about the data series being plotted.

The xlimit and ylimit determine which data values are visible on the x-axis and y-axis in a plot or chart respectively. Only the data values that fall within these limits will be plotted. If no data value maps to the specified x-limits or y-limits, nothing will show on the plot for that particular axis range.

(i) Line Chart — Use a line chart to show trends or changes over time. It's suitable for displaying continuous data series and highlighting patterns or fluctuations.

(ii) Bar Chart — Use a bar chart to compare categories or groups. It's effective for displaying discrete data and showing differences or relationships between items.

(iii) Scatter Chart — Use a scatter chart to visualize relationships between two variables. It's helpful for identifying correlations or trends in data points.

(iv) Pie Chart — Use a pie chart to represent parts of a whole. It's useful for showing the proportion or distribution of different categories within a dataset.

(v) Boxplot — The box plot is used to show the range and the middle half of ranked data while identifying outliers or variability.

A line chart is the suitable choice for visualizing how the temperature changed over the last seven days. The line chart shows trends over time and displays continuous data, making it ideal for representing temperature values. The chart's ability to connect data points allows viewers to easily observe temperature trends and understand variations across the seven-day period.

A histogram is a summarization tool for discrete or continuous data, providing a visual interpretation of numerical data by showing the number of data points that fall within a specified range of values.

The hist() function of the Pyplot module is used to create and plot a histogram from a given sequence of numbers. The syntax for using the hist() function in Pyplot is as follows:

matplotlib.pyplot.hist(x, bins = None, cumulative = False, histtype = 'bar', align = 'mid', orientation = 'vertical', ).

The hist() function in Matplotlib's Pyplot module allows creating various types of histograms. These include the default bar histogram (histtype='bar'), step histogram (histtype='step'), stepfilled histogram (histtype='stepfilled'), barstacked histogram (histtype='barstacked').

Histograms are great for displaying specific ranges of values and are ideal for visualizing the results of continuous data, such as the ages of students in a class. Bar charts, on the other hand, are effective for comparing categorical or discrete data across different categories or groups, such as comparing the sales performance of different products.

A cumulative histogram is a graphical representation in which each bin displays the count of data points within that bin as well as the counts of all smaller bins. The final bin in this histogram indicates the total number of data points in the dataset.

In Matplotlib's hist function, we can create a cumulative histogram by setting the cumulative parameter to True. The syntax is as follows: matplotlib.pyplot.hist(x, bins = None, histtype='barstacked', cumulative=True).

A frequency polygon is a type of frequency distribution graph. In a frequency polygon, the number of observations is marked with a single point at the midpoint of an interval, and a straight line then connects each set of points.

We can create frequency polygon in following two ways:

1. Drawing Frequency Polygon Manually
2. Creating Frequency Polygon through a Line Chart

The five-point summary is a descriptive statistics tool that provides a concise summary of the distribution of a dataset. It consists of five important numbers of a data range:

1. the minimum range value
2. the maximum range value
3. the upper quartile
4. the lower quartile
5. the median

A boxplot is a visual representation of the statistical five number summary of a given data set, including the extremes (the highest and the lowest numbers), the median, the upper and lower quartiles.

With Pyplot, a boxplot is created using boxplot() function. The syntax is as follows : matplotlib.pyplot.boxplot(x, notch = None, vert = None, meanline = None, showmeans = None, showbox = None,).

Executing the provided code will not produce an error. It will generate a plot of the logarithm of A against A itself.

The line A = np.arange(2, 20, 2) creates an array A using NumPy's arange() function. It starts from 2, increments by 2, and includes values up to 20. This results in the array [2, 4, 6, 8, 10, 12, 14, 16, 18]. Next, the line B = np.log(A) calculates the natural logarithm of each element in array A using NumPy's log() function and stores the results in array B. Finally, plt.plot(A, B) plots the values in array A along the x-axis and the corresponding values in array B along the y-axis using Matplotlib's plot() function.

Executing the provided code will not produce an error. However, the resulting plot might not be as expected because the x-axis values are discrete and categorical, not continuous.

The line A = np.arange(2, 20, 2) creates an array A using NumPy's arange() function. It starts from 2, increments by 2, and includes values up to 20. This results in the array [2, 4, 6, 8, 10, 12, 14, 16, 18]. Next, the line B = np.log(A) calculates the natural logarithm of each element in array A using NumPy's log() function and stores the results in array B. Finally, plt.bar(A, B) creates a bar plot using Matplotlib's bar() function. It plots the values in array A along the x-axis and the corresponding values in array B along the y-axis.

The code will produce an error because the variable Y is not defined.

The corrected code is:

X = np.arange(1, 18, 2.655)
B = np.log(X)
plt.scatter(X, B)

The line X = np.arange(1, 18, 2.655) creates an array X using NumPy's arange() function. It starts from 1, increments by 2.655, and generates values less than 18. The resulting array will look like [1., 3.655, 6.31, 8.965, 11.62, 14.275, 16.93]. Next, the line B = np.log(X) calculates the natural logarithm of each element in array X using NumPy's log() function. Finally, the line plt.scatter(X, Y) attempts to use Matplotlib's scatter() function to create a scatter plot. However, Y is not defined in code, leading to a NameError.

This code snippet uses Matplotlib to create a bar chart. The list Months contains the names of the months ['Dec', 'Jan', 'Feb', 'Mar'], while the list Attendance holds corresponding attendance values [70, 90, 75, 95]. The plt.bar() function is then used to create a bar plot, where each bar represents a month and its height corresponds to the attendance value. Finally, plt.show() is called to display the plot.

Out of the above plotted histograms, none can be used for creating frequency polygons. We cannot draw frequency polygons from all the above histograms because to construct a frequency polygon, we need a step-type histogram. A frequency polygon is constructed by connecting the midpoints of the tops of the bars of a histogram. Step-type histograms provide a clear outline to draw these connections.