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XII_Chapter_III_IP_Sumita_Arora_Comprehensive Guide to Data Visualization with Python Matplotlib Pyplot

Learn to create and customize line, bar, and histogram charts using Python's Matplotlib Pyplot. Understand visualization basics, installation, key methods, and real-world applications for effective data presentation.

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CHAPTER: 3 Plotting with Pyplot Data Visualization using Python • Matplotlib • Pyplot Python 3 Matplotlib Hands-on Coding
C H A P T E R R O A D MA P Learning Outcomes By the end of this chapter, you will be able to... EXPLAIN why data visualization matters and where it is used in real life INSTALL & IMPORT the Matplotlib library and use the Pyplot module correctly CREATE line charts, bar charts and horizontal bar charts using Python code CUSTOMIZE colours, markers, line width and chart size for clear presentation COMPARE different chart types and choose the right one for given data APPLY plotting skills to solve real classroom and board-exam style problems
R E A L - W O R L D R E L E V A N C E Why Should We Learn This Chapter? "A picture is worth a thousand numbers." Every industry today uses charts to make sense of data. Artificial Intelligence Visualizing model accuracy & training trends Business Sales dashboards & quarterly performance charts Healthcare Patient vitals, disease spread & recovery graphs Sports Analytics Player performance & match statistics Weather Forecast Temperature, rainfall & climate trend charts Finance Stock price movement & investment growth charts
C H A P T E R O V E R V I E W Your Journey Through Pyplot 1 Visualization Basics 2 Pyplot Setup & Methods 3 Line Chart 4 Bar Chart 5 Multiple & Horizontal Bars 6 Histogram 7 Customizing the Plot We will build each chart type step-by-step: syntax → code → output → customization → practice.
T O P I C 1 What is Data Visualization? DEFINITION Data Visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. "See the story behind the numbers" Classroom Example: Instead of reading 40 students' marks in a list, a bar chart instantly shows who scored highest! ▸ Turns raw numbers into pictures our brain understands instantly ▸ Helps us spot patterns, trends and comparisons quickly ▸ Makes reports and presentations easier to understand ▸ A single chart can replace pages of numeric tables Features:
T O P I C 2 . 3 Types of Charts in Matplotlib Different data needs different charts — here is a quick preview of what we will learn. Line Chart A graphical representation that connects data points with lines to show trends or changes over time. plot() Bar Chart Uses rectangular bars to compare quantities across different categories. bar() Histogram Displays the frequency distribution of continuous data using adjacent bars. hist() Scatter Plot Shows the relationship between two variables using dots on a coordinate plane. scatter() Pie Chart Represents data as slices of a circle, illustrating parts of a whole. pie() Box Plot Summarizes data distribution through quartiles, highlighting median and outliers. boxplot()
T O P I C 2 . 1 Installing & Importing Matplotlib STEP 1 — Install (one time only, in command prompt / terminal) pip install matplotlib STEP 2 — Import Pyplot in your Python program import matplotlib.pyplot as plt # "plt" is the standard short name used by all programmers REMEMBER We import Pyplot as "plt" — this short name (alias) is a worldwide convention. Always use it so your code matches textbooks and online examples. WHY "plt" AND NOT SOMETHING ELSE? ▸ Shorter to type again and again ▸ Every teacher, book and website uses "plt" ▸ You could technically use any name, but it breaks the convention ▸ CBSE exams expect standard aliasing matplotlib is Python's most popular plotting library!
T O P I C 2 . 2 Working with Pyplot Methods Pyplot gives ready-made functions (methods) to build and display a chart. Here are the ones you will use the most: METHOD WHAT IT DOES plt.plot() Draws a line chart plt.bar() / plt.barh() Draws a vertical / horizontal bar chart plt.hist() Draws a histogram plt.title() Adds a chart title plt.xlabel() / plt.ylabel() Labels the X-axis and Y-axis plt.legend() Shows a legend box for multiple data series plt.grid() Adds gridlines for easier reading plt.show() Displays the final chart on screen plt.savefig() Saves the chart as an image file
QUICK CHECK 1 MCQs 1. Which alias is conventionally used for matplotlib.pyplot? (a) mp (b) plt (c) pyp (d) mplt Answer: (b) plt 2. Which function actually displays the chart on screen? (a) plt.draw() (b) plt.view() (c) plt.show() (d) plt.display() Answer: (c) plt.show() SHORT QUESTION Name any two real-life fields where data visualization is used, with one example each. THINK & ANSWER Why do you think a chart communicates faster than a table of the same numbers? Answer: • Healthcare: Representing data such as age distribution of cases or vaccination progress. • Sports: Tracking passes, shots, and other key actions in games. Answer: The reason a chart communicates faster than a table of the same numbers is due to the visual nature of charts.
T O P I C 3 Line Chart — plot() Documentation A Matplotlib line chart links data points with lines to show trends over a sequence like time. It’s great for visualizing how values change and for comparing data points clearly. matplotlib.pyplot.plot(x, y, color=None, linestyle='-', linewidth=1, marker=None, label=None) Parameters: • x: Values to be displayed on the x-axis. • y: Values to be displayed on the y-axis. • color: Specifies the color of the line (for example, 'red', 'blue', 'green'). • linestyle: Defines the style of the line, such as solid ('-'), dashed ('--'), dotted (':'), or dash-dot ('-.'). • linewidth: Sets the thickness of the line. • marker: Displays a marker at each data point (such as 'o', 's', '^', '*'). • label: Specifies a label for the plotted line, which is displayed when using legend(). Syntax:
T O P I C 3 Creating a Line Chart — plot() PROBLEM: Plot the temperature recorded on 5 days of a week: 30, 32, 29, 35, 33 °C import matplotlib.pyplot as plt days = [1, 2, 3, 4, 5] temp = [30, 32, 29, 35, 33] plt.plot(days, temp) # draws the line plt.show() # displays the chart OUTPUT IMPORTANT NOTE plot(x, y) needs two lists of equal length: one for the X-axis, one for the Y-axis. SAMPLE PROGRAM:
T O P I C 3 Line Chart — Plot Size & Grid SPECIFYING PLOT SIZE import matplotlib.pyplot as plt days = [1, 2, 3, 4, 5] temp = [30, 32, 29, 35, 33] plt.figure(figsize=(8, 5)) plt.plot(days, temp) plt.show() REMEMBER figsize=(width, height) is measured in inches. Use it BEFORE plot(). ADDING GRID EXAM TIP grid(True) adds light background lines — makes it easy to read exact values. A favourite one-mark question! import matplotlib.pyplot as plt days = [1, 2, 3, 4, 5] temp = [30, 32, 29, 35, 33] plt.figure(figsize=(8, 5)) plt.plot(days, temp) plt.grid(True) plt.show() OUTPUT SAMPLE PROGRAM: OUTPUT SAMPLE PROGRAM:
T O P I C 3 Line Chart — Colour & Width PARAMETER color linewidth A number — bigger number = thicker line COMMON MISTAKE Writing colour = "Green" (capital letter) or a spelling mistake gives an error. Colour names are case-sensitive lowercase strings. import matplotlib.pyplot as plt days = [1, 2, 3, 4, 5] temp = [30, 32, 29, 35, 33] plt.figure(figsize=(8, 5)) plt.plot(days, temp, color = 'green',linewidth = 3) plt.show() SAMPLE PROGRAM: OUTPUT Letter Alias Color Name Hex Color Code ‘b’ ‘blue’ ‘#0000ff’ ‘g’ ‘green’ ‘#008000’ ‘r’ ‘red’ ‘#ff0000’ ‘c’ ‘cyan’ ‘#00bfbf’ ‘m' ‘magenta’ ‘#bf00bf’ ‘y' ‘yellow’ ‘#bfbf00’ ‘k' ‘black’ ‘#000000’ ‘w' ‘white’ ‘#ffffff’
T O P I C 3 Line Chart — Adding Markers A marker highlights each data point on the line with a small shape. SOME COMMON MARKER TYPES 'o' → circle '*' → star 's' → square 'D' → diamond REMEMBER markersize controls how big the marker dot appears. markerfacecolor can differ from the line colour for extra emphasis. import matplotlib.pyplot as plt days = [1, 2, 3, 4, 5] temp = [30, 32, 29, 35, 33] plt.figure(figsize=(8, 5)) plt.plot(days, temp,marker = 'o',markersize = 10,markerfacecolor = 'red') plt.show() OUTPUT SAMPLE PROGRAM:
T O P I C 3 — W R A P U P Line Chart: Summary & Key Takeaways KEY TAKEAWAYS ▸ plt.plot(x, y) draws a line chart connecting data points ▸ figsize=(w, h) controls chart size in inches ▸ grid(True) adds gridlines for readability ▸ color and linewidth style the line itself ▸ marker, markersize, markerfacecolor style the data points ▸ Always end with plt.show() to display the chart COMMON MISTAKE Common Errors: • forgetting plt.show() • mismatched list lengths for x and y • using capital letters in colour/marker names. EXAM TIP Board exams often ask you to write a program AND identify one error in a given line-chart code. Practise both! DID YOU KNOW? The very first known line chart was created by William Playfair in 1786 to show England's trade data!
QUICK CHECK 2 MCQs 1. Which parameter controls the thickness of a line in plot()? (a) linesize (b) linewidth (c) thickness (d) width Answer: (b) linewidth 2. Which parameter is used to colour the marker only (not the line)? (a) color (b) markercolor (c) markerfacecolor (d) fillcolor Answer: (c) markerfacecolor SHORT QUESTION import matplotlib.pyplot as plt # Sample data - replace with your lists x = [1, 2, 3, 4, 5] y = [2, 4, 1, 5, 3] # Plot x vs y with red dashed line, width 2 plt.plot(x, y, color='red', linestyle='--', linewidth=2) # Optional: add labels and title plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.title('Plot of x vs y') # Show the plot plt.grid(True) plt.show() THINK & ANSWER If you wanted to compare two students' monthly attendance on the same chart, how would a line chart help? Write one line of code to plot list "x" against list "y" with a red dashed line of width 2. See Trends: You can instantly see if attendance is going up, down, or staying flat for each student across 12 months. Direct Comparison: Plot both students on the same chart with 2 different colored lines. You can see who had higher attendance in which month. Spot Patterns: Easy to spot things like "Student A drops in exam months" or "Student B is more consistent".
T O P I C 4 Bar Chart — bar() Documentation A bar plot compares categories with rectangular bars. Each bar’s height reflects its value, making differences between categories easy to spot. matplotlib.pyplot.bar(x, height, width=0.8, color=None, label=None, align='center') Parameters: • x: Specifies the positions or category labels for the bars on the x-axis. • height: Specifies the height (value) of each bar. • width: Sets the width of the bars. The default value is 0.8. • color: Specifies the color of the bars. It can be a single color or a list of colors. • label: Adds a label for the bar plot, which can be displayed using legend(). • align: Specifies how the bars are aligned with the x positions. Common values are 'center' (default) and 'edge'. Syntax:
T O P I C 4 Creating a Bar Chart — bar() PROBLEM: Compare the number of books read by 4 students: Aman-5, Bina-8, Chirag-3, Diya-7 import matplotlib.pyplot as plt names = ['Aman', 'Bina', 'Chirag', 'Diya'] books = [5, 8, 3, 7] plt.bar(names, books) # draws vertical bars plt.show() IMPORTANT NOTE bar(x, height) is best for comparing categories — here, students — side by side. OUTPUT SAMPLE PROGRAM:
T O P I C 4 Bar Chart — Changing Bar Width SAME (COMMON) WIDTH FOR ALL BARS DIFFERENT WIDTH FOR EACH BAR REMEMBER width takes a single number (all bars same width) OR a list matching the number of bars (each bar can differ). EXAM TIP Default bar width is 0.8. Values above 1.0 may cause bars to overlap — good trick question in exams! OUTPUT import matplotlib.pyplot as plt names = ['Aman', 'Bina', 'Chirag', 'Diya'] books = [5, 8, 3, 7] plt.bar(names, books, width = 0.4) plt.show() SAMPLE PROGRAM: import matplotlib.pyplot as plt names = ['Aman', 'Bina', 'Chirag', 'Diya'] books = [5, 8, 3, 7] plt.bar(names, books, width = [0.2, 0.4, 0.6, 0.8]) plt.show() SAMPLE PROGRAM: OUTPUT
T O P I C 4 Bar Chart — Changing Bar Colour SAME (COMMON) COLOUR DIFFERENT COLOUR FOR EACH BAR ACTIVITY TIME Try it yourself: change the colour list so that the bar with the HIGHEST value is always gold, and the rest stay grey. COMMON MISTAKE The colour list must have the SAME number of items as the bars — one colour per category, no more, no less. import matplotlib.pyplot as plt names = ['Aman', 'Bina', 'Chirag', 'Diya'] books = [5, 8, 3, 7] plt.bar(names, books, color = 'teal') plt.show() SAMPLE PROGRAM: OUTPUT import matplotlib.pyplot as plt names = ['Aman', 'Bina', 'Chirag', 'Diya'] books = [5, 8, 3, 7] plt.bar(names, books, color = ['red','green','blue','orange']) plt.show() SAMPLE PROGRAM: OUTPUT
T O P I C 4 — W R A P U P Bar Chart: Summary & Key Takeaways KEY TAKEAWAYS ▸ plt.bar(x, height) draws vertical bars for category comparison ▸ width sets bar thickness — one value or a list ▸ color sets bar fill — one value or a list matching bar count ▸ Bar charts are best when comparing DISCRETE categories ▸ Line charts are best when showing CONTINUOUS trends over time COMMON MISTAKE Common Errors: list length mismatch between categories and values; forgetting quotes around colour names; confusing bar() with barh(). EXAM TIP Remember: bar chart bars have gaps between them (discrete categories); histogram bars touch each other (continuous ranges). DID YOU KNOW? Bar charts are believed to have first been used by William Playfair too — in his 1786 book on trade statistics!
T O P I C 5 Creating Multiple Bar Charts Sometimes we need to compare MORE THAN ONE data series for the same categories — e.g. marks of 2 subjects for the same students. THE TRICK: ADJUST X-AXIS POSITIONS ▸ Use numpy.arange() to get base X positions for categories ▸ Shift the second series slightly using position + bar thickness ▸ Give each series a different bar thickness / colour ▸ Add plt.legend() so viewers know which colour = which subject IMPORTANT NOTE This is also called a "grouped" or "clustered" bar chart — bars for each category sit side-by-side, grouped together. GROUPED BARS — CONCEPT ■ Subject A ■ Subject B Each pair of bars represents one student — placed side by side, not overlapping.
T O P I C 5 Multiple Bar Charts — Python Code import numpy as np import matplotlib.pyplot as plt students = ['Aman','Bina','Chirag'] maths = [78, 88, 65] science = [82, 74, 91] pos = np.arange(len(students)) # base positions w = 0.35 # bar thickness plt.bar(pos, maths, w, color=‘#065A82', label='Maths') plt.bar(pos + w, science, w, color=‘#F2A93B', label='Science') plt.xticks(pos + w/2, students) # centre category labels plt.legend() plt.show() REMEMBER pos + w shifts the second set of bars right by exactly one bar-width, so bars sit neatly beside each other. EXAM TIP xticks(pos + w/2, students) centres the category name UNDER the pair of bars — a common exam trick. COMMON MISTAKE Forgetting label= in bar() means legend() will show empty entries. SAMPLE PROGRAM: OUTPUT:
QUICK CHECK 3 MCQs 1. Which library function gives evenly spaced base positions for grouped bars? (a) np.range() (b) np.arange() (c) np.position() (d) np.linspace_bar() Answer: (b) np.arange() 2. What does plt.legend() require to display correct labels? (a) title= in bar() (b) label= in bar() (c) name= in bar() (d) key= in bar() Answer: (b) label= in bar() SHORT QUESTION What is the purpose of shifting the second bar series by "pos + w" instead of plotting it at the same position? THINK & ANSWER Can you think of a real school scenario where a grouped bar chart (2 data series) would be more useful than a simple bar chart? Answer: pos → first series, pos + w → second series, so the bars sit next to each other instead of hiding one another. A real school scenario: comparing boys vs girls performance in two subjects (e.g., Maths and Science). A grouped bar chart lets teachers see side-by-side subject scores for each group, making it easier to spot strengths or gaps than a simple bar chart showing only one subject.
T O P I C 6 Creating a Horizontal Bar Chart Use barh() when category names are long, or you simply want bars running left-to-right instead of bottom-to-top. import matplotlib.pyplot as plt subjects = ['AI', 'IP', 'Physics', 'Maths'] hours = [3, 5, 4, 6] plt.barh(subjects, hours, color="1C7293") plt.xlabel("Study Hours per Week") plt.show() EXAM TIP barh(y, width) — parameters are swapped compared to bar(x, height). This swap is a favourite one-mark exam question! DID YOU KNOW? Horizontal bar charts are the preferred choice in news infographics when category names (like country names) are long and would overlap if placed vertically. SAMPLE PROGRAM: OUTPUT:
T O P I C 7 Histogram — hist() Documentation plt.hist() makes histograms. It splits data into bins, counts values in each bin, and plots the results as bars to show the data distribution. matplotlib.pyplot.hist(x, bins=None, *, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, data=None, **kwargs) IMPORTANT PARAMETERS: • x: data to be represented in the histogram. • bins: Specifies the number of bins or the bin edges for the histogram. • range: The lower and upper range of the bins. • density: If True, the histogram is normalized to form a probability density. • histtype: Defines the type of histogram (e.g., 'bar' for a traditional bar histogram). • color: Sets the color of the bars. • label: Label for the histogram, used in legends. • edgecolor: Colour of the bar border — improves readability • cumulative: If True, each bar adds the count of all previous bars • orientation: 'vertical' (default) or 'horizontal' Syntax: RETURNS: • n: array or list of arrays- The values of the histogram bins. • binsarray- The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in. • patches- BarContainer or list of a single Polygon or list of such objects- Container of individual artists used to create the histogram or list of such containers if there are multiple input datasets.
T O P I C 7 Creating a Histogram — hist() A histogram shows how numeric data is DISTRIBUTED across ranges (called "bins") — unlike a bar chart, bars touch each other. PROBLEM: Show the distribution of marks of 30 students out of 100 import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=10) # divides data into 10 ranges plt.show() IMPORTANT NOTE bins decides how many groups the data range is divided into. OUTPUT: SAMPLE PROGRAM:
T O P I C 7 Histogram — Effect of Bin Count The SAME data can look very different depending on how many bins you choose. 20 BINS — BROADER GROUPS Fewer, wider bars. Shows the OVERALL shape clearly. 50 BINS — FINER DETAIL More, narrower bars. Shows fine DETAIL but can look noisy. EXAM TIP Too few bins hide patterns; too many bins create noise. Choosing the right bin count is a key data-visualization skill often tested conceptually. import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=20) plt.show() import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=50) plt.show() OUTPUT: OUTPUT: SAMPLE PROGRAM: SAMPLE PROGRAM:
T O P I C 7 Cumulative & Step Histogram CUMULATIVE HISTOGRAM Each bar height = total count SO FAR (adds up all previous bins). Useful to see "how many students scored below X marks". STEP HISTOGRAM Draws only the OUTLINE of the bars (no fill) — useful when comparing multiple histograms on one chart without blocking each other. REMEMBER cumulative=True changes WHAT is counted; histtype changes HOW it is drawn. The two can be combined together. import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=10, cumulative = True) plt.show() SAMPLE PROGRAM: import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=10, histtype = 'step') plt.show() SAMPLE PROGRAM: OUTPUT: OUTPUT:
T O P I C 7 Multiple & Stacked Histograms MULTIPLE HISTOGRAMS (SIDE BY SIDE) STACKED HISTOGRAM IMPORTANT NOTE Passing a LIST OF LISTS to hist() plots more than one dataset together — great for comparing two sections or two years of data. EXAM TIP stacked=True places one dataset's bars ON TOP of the other instead of beside it — the total bar height shows the combined count. import matplotlib.pyplot as plt classA = [34, 45, 67, 78] classB = [56, 60, 88, 92] plt.hist([classA, classB], bins=10, label=['A','B']) plt.legend() plt.show() SAMPLE PROGRAM: OUTPUT: import matplotlib.pyplot as plt classA = [45, 67, 78, 34] classB = [88, 92, 56, 60] plt.hist([classA,classB],bins=10,stacked=True,label=['A','B']) plt.legend() plt.show() SAMPLE PROGRAM: OUTPUT:
T O P I C 7 Horizontal Histogram import matplotlib.pyplot as plt marks = [45, 67, 78, 34, 88, 92, 56, 60, 73, 81] plt.hist(marks, bins=10, orientation = 'horizontal') plt.show() EXAM TIP orientation="horizontal" is the histogram equivalent of using barh() for bar charts. COMMON MISTAKE Common Errors: forgetting bins= (Python then uses default 10, which may not suit your data); mismatching histtype spelling; giving cumulative a string "True" instead of the boolean True. SAMPLE PROGRAM: OUTPUT:
T O P I C 7 — W R A P U P Histogram: Summary & Key Takeaways KEY TAKEAWAYS ▸ plt.hist(data, bins=n) shows the DISTRIBUTION of numeric data ▸ Bars touch each other — data is CONTINUOUS, not categorical ▸ bins controls how many ranges the data is split into ▸ cumulative=True gives running totals ▸ histtype and orientation change how the histogram looks ▸ A histogram answers "how many values fall in this range?" DID YOU KNOW? The term "histogram" was coined by Karl Pearson in 1895 — from the Greek words "histos" (mast/pole) and "gramma" (drawing). EXAM TIP A very common exam question: "Differentiate between a Bar Chart and a Histogram." Bar chart = categories with gaps; Histogram = continuous ranges, bars touch. ACTIVITY TIME Collect the heights (in cm) of all students in your class and plot a histogram with 5 bins!
QUICK CHECK 4 MCQs 1. What is the default number of bins used by plt.hist() if not specified? (a) 5 (b) 10 (c) 20 (d) 100 Answer: (b) 10 2. Which parameter draws only the outline of histogram bars? (a) outline (b) edgecolor (c) histtype="step" (d) fill=False Answer: (c) histtype="step" SHORT QUESTION State one key difference between a Bar Chart and a Histogram. THINK & ANSWER If a teacher wants to see how many students scored between 0- 33%, 34-66%, and 67-100%, which chart — bar or histogram — is more suitable, and why? Answer: A Bar Chart compares categories (like number of students in each class), while a Histogram shows the frequency distribution of continuous data (like how many students scored within certain mark ranges). In short: Bar Chart → categories, Histogram → data intervals. Answer: A Histogram is more suitable because the teacher is grouping scores into interval ranges (0–33%, 34–66%, 67–100%), and histograms are designed to show the frequency distribution of continuous data across intervals. So, use a Histogram → to visualize score ranges.
T O P I C 8 Customizing the Plot — Chart Anatomy Every good chart has these labelled components. Let's learn to add each one. Chart Title ← plt.title() ■ Series A ← plt.legend() ← plt.grid() Y VALUES plt.ylabel() → X VALUES plt.xlabel() Small marks along each axis = plt.xticks() / plt.yticks()
T O P I C 8 Customizing — Title & Axis Labels import matplotlib.pyplot as plt # Sample data days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] scores = [65, 70, 75, 80, 85] # Create line chart plt.plot(days, scores, marker='o', color='blue') # Customizing title and axis labels plt.title("Student Performance Over a Week") # Chart Title plt.xlabel("Days of the Week") # X-axis Label plt.ylabel("Scores (%)") # Y-axis Label # Show chart plt.show() WHY LABEL EVERYTHING? ▸ A chart without a title is confusing ▸ Axis labels tell the viewer WHAT the numbers mean ▸ fontsize= makes the title stand out EXAM TIP CBSE marking schemes often give separate marks for title, xlabel and ylabel — never skip them in a program-based answer! SAMPLE PROGRAM: OUTPUT:
T O P I C 8 Customizing — Axis Limits & Ticks SETTING LIMITS plt.ylim(0, 100) # Y-axis 0 to 100 plt.xlim(0, 10) # X-axis 0 to 10 Fixes the visible range of an axis — useful to keep multiple charts on the same scale for fair comparison. CUSTOM TICKS plt.xticks([0,1,2,3], ['Q1','Q2','Q3','Q4']) plt.yticks(range(0, 101, 20)) Replaces plain numbers with meaningful labels, or controls the spacing between marks on an axis. REMEMBER ylim/xlim take a MIN and MAX value. xticks/yticks can take just positions, or positions PLUS custom text labels.
T O P I C 8 Customizing — Axis Limits & Ticks import matplotlib.pyplot as plt # Sample data days = [1, 2, 3, 4, 5] scores = [65, 70, 75, 80, 85] # Create line chart plt.plot(days, scores, marker='o', color='green') # Customizing axis limits plt.xlim(0, 6) # X-axis range from 0 to 6 plt.ylim(60, 90) # Y-axis range from 60 to 90 # Customizing ticks plt.xticks([1, 2, 3, 4, 5], ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']) # Custom labels plt.yticks([60, 70, 80, 90]) # Specific tick marks # Title and labels plt.title("Student Scores Over Days") plt.xlabel("Days of the Week") plt.ylabel("Scores (%)") # Show chart plt.show() SAMPLE PROGRAM: OUTPUT:
T O P I C 8 Customizing — Adding a Legend COMMON MISTAKE legend() only shows entries for plots that were given a label= parameter. If you forget label=, the legend box will appear empty or missing. Legend A Legend in a chart is a guide that explains the meaning of different colors, markers, or line styles used, helping the viewer identify which data series each visual element represents. SYNTAX: matplotlib.pyplot.legend(loc=PositionNumber or String) COMMON loc VALUES 1 'upper right’ 2 ‘upper left’ 3 'lower left’ 4 ‘lower right’
T O P I C 8 Customizing — Adding a Legend import matplotlib.pyplot as plt # Sample data days = [1, 2, 3, 4, 5] maths_scores = [65, 70, 75, 80, 85] science_scores = [60, 68, 72, 78, 82] # Plotting two lines plt.plot(days, maths_scores, marker='o', color='blue', label='Maths') plt.plot(days, science_scores, marker='s', color='green', label='Science') # Adding title and axis labels plt.title("Student Scores Over Days") plt.xlabel("Days") plt.ylabel("Scores (%)") # Displaying legend plt.legend() # Show chart plt.show() SAMPLE PROGRAM: OUTPUT:
T O P I C 8 Customizing — Saving the Figure Instead of just viewing a chart, you can save it permanently as an image file. plt.savefig(fname, dpi=None, facecolor='w', edgecolor='w',transparent=False, bbox_inches=None,pad_inches=0.1) • fname → File name or path (e.g., "chart.png", "output.pdf"). • dpi → Resolution in dots per inch (higher = sharper image). • facecolor → Background color of the figure. • edgecolor → Border color around the figure. • transparent → If True, saves with transparent background. • bbox_inches → Bounding box option ('tight' trims extra whitespace). • pad_inches → Padding around the figure when using bbox_inches. COMMON MISTAKE plt.show() clears the figure from memory after displaying it. Calling savefig() AFTER show() often saves a BLANK image — always savefig() first! savefig() Syntax and Parameters:
T O P I C 8 Customizing — Saving the Figure import matplotlib.pyplot as plt # Sample data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] # Create a line chart plt.plot(x, y, marker='o', color='blue', label="y = 2x") plt.title("Line Chart Example") plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.legend() # Save the figure plt.savefig("C:UsersMYPCDesktopline_chart.png", dpi=300, bbox_inches='tight', transparent=True) # Display the chart plt.show() SAMPLE PROGRAM: OUTPUT: Image file is saved at the given location i.e. Desktop
T O P I C 8 — W R A P U P Customization: Summary & Key Takeaways KEY TAKEAWAYS ▸ title(), xlabel(), ylabel() explain what the chart shows ▸ xlim()/ylim() fix the visible axis range ▸ xticks()/yticks() customize axis marks and labels ▸ legend() needs label= set inside each plot/bar call ▸ savefig() must be called BEFORE show() ▸ A well-labelled chart earns full marks in board exams! EXAM TIP A full "Chart Customization" program question typically expects: title, both axis labels, legend (if multiple series) and grid — check all four before submitting! DID YOU KNOW? Matplotlib's name is a nod to MATLAB, whose plotting style inspired Pyplot's command syntax. ACTIVITY TIME Take your bar chart from Part 1 and add a title, both axis labels, and a legend — then save it as a PNG.
QUICK CHECK 5 MCQs 1. Which function must be called BEFORE plt.show() to correctly save a chart? (a) plt.export() (b) plt.save() (c) plt.savefig() (d) plt.write() Answer: (c) plt.savefig() 2. Which function is used to display the legend box on a chart? (a) plt.key() (b) plt.legend() (c) plt.label() (d) plt.info() Answer: (b) plt.legend() SHORT QUESTION Write the code to set the Y-axis limits of a chart from 0 to 50. THINK & ANSWER Why do you think CBSE examiners give separate marks for title, labels and legend rather than just for the plot() line? import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [10, 20, 30, 40, 45] # Plot line chart plt.plot(x, y, marker='o') # Set Y-axis limits plt.ylim(0, 50) plt.show() CBSE examiners give separate marks for title, labels, and legend because these elements show whether a student can make the chart clear, complete, and meaningful for interpretation. The plot() line only draws the graph, but title, axis labels, and legend demonstrate proper presentation and communication of data, which is a key skill being assessed.
COMPLETE CHAPTER SUMMARY — 2 MINUTE REVISION 1. Visualization Pictures explain data faster 2. Setup import matplotlib.pyplot as plt 3. plot() Line Chart — trends 4. bar()/barh() Bar Chart — comparison 5. hist() Histogram — distribution 6. Customize title, label, legend, grid, save MEMORY TRICK: "Very Simple Line Bars Have Colours" → Visualize, Setup, Line, Bar, Histogram, Customize Chart choice reminder: Line = trend over time | Bar = compare categories | Histogram = distribution of continuous data KEYWORDS plot • bar • barh • hist • bins • title • xlabel • ylabel • legend • grid • figsize • savefig
R E V I S I O N K I T Common Mistakes Students Make Forgetting plt.show() — the chart is built but never displayed Mismatched list lengths between x and y data Using capital letters in colour names, e.g. "Red" instead of "red" Calling plt.savefig() AFTER plt.show() — produces a blank saved image Forgetting label= in plot()/bar(), so legend() shows empty entries Confusing bar() (vertical) with barh() (horizontal) parameter order Using a bar chart for continuous numeric data instead of a histogram Not resetting figsize before creating a new chart in the same program
R E V I S I O N K I T Important Functions — Cheat Sheet plt.plot(x,y) Line chart plt.bar(x,h) Vertical bar chart plt.barh(y,w) Horizontal bar chart plt.hist(data,bins) Histogram plt.title(t) Chart title plt.xlabel(t)/ylabel(t) Axis labels plt.xlim()/ylim() Axis range plt.xticks()/yticks() Axis marks plt.legend() Legend box plt.grid(True) Gridlines plt.figure(figsize=(w,h)) Canvas size plt.savefig(name) Save chart as file plt.show() Display chart
R E V I S I O N K I T Syntax — Cheat Sheet import matplotlib.pyplot as plt # Line Chart plt.plot(x, y, color='blue', linewidth=2, marker='o', markersize=8) # Bar Chart plt.bar(x, height, width=0.5, color='teal') # Horizontal Bar Chart plt.barh(y, width, color='orange') # Histogram plt.hist(data, bins=10, color='green', edgecolor='black') # Customization (add before show) plt.title("My Chart") plt.xlabel("X Axis") plt.ylabel("Y Axis") plt.legend() plt.grid(True) plt.xlim(0, 10) plt.ylim(0, 100) # Save & Display plt.savefig("chart.png", dpi=300) plt.show()
R E V I S I O N K I T Most Important Viva Questions Q1. What is the difference between plt.show() and plt.savefig()? Q2. Why must a colour name in matplotlib always be written in lowercase? Q3. What happens if the x and y lists passed to plot() are of unequal length? Difference in brief: • plt.show() → Displays the chart on the screen (interactive window). • plt.savefig() → Saves the chart to a file (e.g., PNG, PDF) for later use. In short: show() = view, savefig() = store. In Matplotlib, color names must be written in lowercase because the library’s internal color dictionary is case-sensitive and only recognizes predefined lowercase strings (like "red", "blue", "green"). In short: lowercase ensures consistency and avoids errors when matching color names. If the x and y lists passed to plot() are of unequal length, Matplotlib raises a ValueError because each x-value must have a corresponding y-value. In short: Unequal lengths → error (cannot plot).
R E V I S I O N K I T Most Important Viva Questions Q4. How is a Histogram different from a Bar Chart? Give one example of each. Q5. What is the purpose of the bins parameter in hist()? Q6. Which function would you use to change the size of the figure before plotting? A Histogram shows the frequency distribution of continuous data divided into intervals, while a Bar Chart compares discrete categories. Example: •Histogram → Number of students scoring between 0–20, 21–40, 41–60, etc. •Bar Chart → Number of students in Class 6, Class 7, Class 8. The bins parameter in hist() decides how the data range is divided into intervals (bins). Each bin represents a range, and the histogram shows how many values fall into each range. In short: bins controls the number/width of intervals in a histogram. You can change the figure size before plotting using the plt.figure(figsize=(width, height)) function in Matplotlib.
R E V I S I O N K I T Previous Year Board-Style Questions Practice these exam-pattern questions (based on past CBSE trends): 1 Mark: Which pyplot function is used to draw a horizontal bar graph? [1] 2 Marks: Write the output of: plt.plot([1,2,3],[4,5,6]); plt.show() — describe what will appear. [2] 3 Marks: Write a program to plot a bar chart for 5 subjects and their marks, with proper title and axis labels. [3] plt.barh() import matplotlib.pyplot as plt # Data subjects = ['Maths', 'Science', 'English', 'History', 'Computer'] marks = [85, 78, 92, 74, 88] # Plot bar chart plt.bar(subjects, marks, color='skyblue') # Add title and axis labels plt.title("Marks Obtained in 5 Subjects") plt.xlabel("Subjects") plt.ylabel("Marks") # Show chart plt.show()
R E V I S I O N K I T Previous Year Board-Style Questions 4 Marks: Differentiate between plt.bar() and plt.hist() with one example each. [4] Difference between plt.bar() and plt.hist() (4 Marks): • plt.bar() → Used to plot a bar chart for discrete categories. • plt.hist() → Used to plot a histogram for continuous data distribution divided into intervals (bins). Examples: import matplotlib.pyplot as plt # Example of Bar Chart subjects = ['Maths', 'Science', 'English', 'History', 'Computer'] marks = [85, 78, 92, 74, 88] plt.bar(subjects, marks, color='skyblue') plt.title("Bar Chart - Marks in Subjects") plt.show() # Example of Histogram data = [12, 15, 17, 18, 19, 20, 21, 22, 22, 23, 24, 25, 25, 26, 27, 28, 30] plt.hist(data, bins=5, color='lightgreen', edgecolor='black') plt.title("Histogram - Frequency Distribution") plt.show()
R E V I S I O N K I T Previous Year Board-Style Questions 5 Marks: Write a Python program using matplotlib to plot both a Line Chart and a Bar Chart for the same dataset, side by side, with legends. [5] import matplotlib.pyplot as plt # Dataset subjects = ['Maths', 'Science', 'English', 'History', 'Computer'] marks = [85, 78, 92, 74, 88] # Create subplots (1 row, 2 columns) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) # Line Chart ax1.plot(subjects, marks, marker='o', color='blue', label='Marks') ax1.set_title("Line Chart - Marks") ax1.set_xlabel("Subjects") ax1.set_ylabel("Marks") ax1.legend() # Bar Chart ax2.bar(subjects, marks, color='green', label='Marks') ax2.set_title("Bar Chart - Marks") ax2.set_xlabel("Subjects") ax2.set_ylabel("Marks") ax2.legend() # Adjust layout and show plt.tight_layout() plt.show()
R E V I S I O N K I T Competency-Based Questions Application-oriented questions that test real understanding, not memorization: 1. A shopkeeper wants to show monthly sales trends across the year. Which chart should he use and why? 2. Your school wants to display the age distribution of 200 sports-day participants. Suggest a suitable chart and justify. The shopkeeper should use a Line Chart because it clearly shows sales trends over time (months). Reason: A line chart highlights rise and fall patterns across the year, making it easier to observe seasonal changes and overall growth compared to just static values. A Histogram is most suitable to display the age distribution of 200 sports-day participants. Reason: A histogram groups ages into intervals (bins) (e.g., 5–10 years, 11–15 years, etc.) and shows how many participants fall in each range, making it easy to see the overall distribution pattern.
R E V I S I O N K I T Competency-Based Questions 3. A weather app shows daily temperature for 30 days. Identify the best chart type, and write the key code line. 4. Given marks of 3 sections (A, B, C) in the same subject, design a chart that lets viewers compare all three at a glance. The best chart type is a Line Chart, because it clearly shows how temperature changes day by day over 30 days. Key code line: plt.plot(days, temperature) This plots the daily temperature trend across the month. To compare marks of Sections A, B, and C in the same subject at a glance, the best choice is a Grouped Bar Chart. Reason: It places bars for each section side-by-side under the same subject, making comparison easy. Example Code: import matplotlib.pyplot as plt # Data sections = ['A', 'B', 'C'] marks = [85, 78, 90] # Plot grouped bar chart plt.bar(sections, marks, color=['red','green','blue']) plt.title("Marks Comparison of Sections A, B, C") plt.xlabel("Sections") plt.ylabel("Marks") plt.show()
R E V I S I O N K I T Assertion–Reason Questions Choose: (a) Both A and R true, R explains A (b) Both true, R does not explain A (c) A true, R false (d) A false, R true Assertion (A): A histogram is used to show the frequency distribution of continuous data. Reason (R): In a histogram, the bars are drawn with gaps between them, similar to a bar chart. Answer: (c) Assertion (A): plt.legend() displays a box identifying each plotted series by colour. Reason (R): legend() automatically works even if no label= parameter was set in plot() or bar(). Answer: (c) Assertion (A): plt.savefig() should be called before plt.show() to save the chart correctly. Reason (R): plt.show() clears the current figure from memory after displaying it. Answer: (a)
R E V I S I O N K I T Case Study Based Question CASE STUDY A school conducted a survey of screen-time (in hours) of 50 students during summer vacation. The IT teacher wants to (i) see the overall distribution of screen-time, and (ii) compare the average screen-time of boys vs girls on the same chart. (i) Which chart type is most suitable to show the overall distribution of screen-time? Name the pyplot function. (ii) Which chart type would best compare boys vs girls average screen-time side by side? Name the pyplot function. (iii) Write the code line to add a legend to the boys-vs-girls comparison chart. (iv) Suggest one customization (title/label/color) that would make the chart clearer for a school report. To see the overall distribution of screen-time → use a Histogram, as it groups students’ screen-time into ranges (bins). compare average screen-time of boys vs girls → use a Grouped Bar Chart, showing both categories side-by-side for easy comparison. plt.legend() This will display the labels (e.g., "Boys", "Girls") that you set in the label parameter of plt.bar() or plt.plot(). plt.legend() plt.title("Average Screen-Time of Boys vs Girls")
R E V I S I O N K I T MCQs — Easy, Moderate & Difficult EASY Which library is used for plotting in Python? (a) numpy (b) matplotlib (c) pandas Which function displays the chart? (a) plt.show() (b) plt.open() (c) plt.view() MODERATE To draw bars of different widths, "width" should be: (a) a string (b) a list (c) a tuple only Which parameter of hist() controls range grouping? (a) range (b) bins (c) group DIFFICULT Which combination correctly places grouped bars side-by-side? (a) same x for both (b) pos and pos+w (c) random x values plt.savefig() called AFTER plt.show() typically produces: (a) sharper image (b) (b) blank image (c) (c) an error always Answer: (b) Answer: (a) Answer: (b) Answer: (b) Answer: (b) Answer: (b)
R E V I S I O N K I T Programming Practice Questions Write complete Python programs (with import, data, chart, labels, and show) for: 1. Plot a line chart of the temperature of your city for 7 days of a week, with a title and axis labels. 2. Plot a bar chart comparing the population of 5 countries, with different colours for each bar. 3. Plot a histogram of the marks of 40 students (out of 50) using 8 bins, with edgecolor black. 4. Plot two line charts (Maths and Science marks of 5 students) on the same axes with a legend. 5. Plot a horizontal bar chart of the time (in hours) spent on 5 different hobbies in a week.
R E V I S I O N K I T Homework Assignment TASK ▸ Collect the daily study hours of yourself for the last 7 days. ▸ Write a Python program to plot this data as BOTH a line chart and a bar chart. ▸ Add a proper title, x-label ("Day") and y-label ("Hours Studied") to each chart. ▸ Save both charts using plt.savefig() with meaningful filenames. ▸ Write 2 lines explaining which chart represents your data better, and why. ▸ Write a Python program to plot a line chart showing daily temperature for 7 days, with proper title and axis labels. ▸ Differentiate between a Line Chart and a Histogram with one example each. ▸ A survey records the average study hours of boys and girls in your class. Write a Python program to plot a Grouped Bar Chart comparing both, with legends and axis labels. ▸ Write a Python program to plot a Histogram showing the distribution of daily study hours of 30 students. Add a suitable title and axis labels. ▸ A company records quarterly sales for 4 regions (North, South, East, West). Write a Python program to plot a Grouped Bar Chart comparing sales across regions, with legends and different colors for each quarter. EXAM TIP Submit your .py file along with both saved chart images (.png) — this mirrors how a practical-exam program is evaluated.
R E V I S I O N K I T Mini Project Idea "MY CLASS DASHBOARD" ▸ Collect marks of your class in 3 recent tests (any subject) ▸ Chart 1 — Line chart showing YOUR OWN progress across the 3 tests ▸ Chart 2 — Bar chart comparing class average of each test ▸ Chart 3 — Histogram showing the distribution of marks in the latest test ▸ Combine all 3 charts into one PDF report with titles and labels ▸ Present your findings to the class in 2 minutes EVALUATION CRITERIA Correct chart choice • Clean code • Proper labels & titles • Clear conclusions • Neat presentation
R E V I S I O N K I T Teacher's Classroom Activity "CHART DETECTIVE" — 15 minute activity ▸ Step 1: Divide the class into groups of 4. Each group gets a real newspaper/magazine chart (or printed chart). ▸ Step 2: Groups identify the chart type (line/bar/histogram) and justify their answer in one sentence. ▸ Step 3: Each group writes the matplotlib code that COULD have generated a similar chart. ▸ Step 4: Groups swap code with another team and try to spot one deliberate error planted by the teacher. ▸ Step 5: Class discussion — which chart types were most common in the media samples, and why?
"Every chart you code is a story you help others see." You now know how to turn raw numbers into powerful visual stories using Python and Matplotlib. Keep practising — the best data scientists started exactly where you are today! E N D O F C H A P T E R — P L O T T I N G W I T H P Y P L O T Thank You