📊 Plotting with PyPlot

News apps show bar charts of election results and weather apps show line graphs of temperature — both are examples of Data Visualization. In Python we draw such charts with Matplotlib's PyPlot. This chapter covers line, scatter, bar and pie charts, customising a plot, histograms, frequency polygons, box plots, and plotting data directly from a DataFrame.

3.1 🌟 What is Data Visualization?

Data Visualization means presenting data in a visual format (charts, graphs, maps) so that our brain can understand patterns and insights quickly.

graph LR
    RAW["📋 Raw Data\n90, 85, 78, 92, 65\nJust numbers..."]
    VIZ["📊 Visualization\nBar Chart / Line Graph\nInstant understanding!"]
    INS["💡 Insights\nWho scored highest?\nWhat's the trend?\nWhere to improve?"]

    RAW --> VIZ --> INS

    style VIZ fill:#FF9800,color:#fff
    style INS fill:#4CAF50,color:#fff

Why do we need Data Visualization?

Analogy: Imagine reading 100 students' marks in a table vs. looking at a bar chart — with the chart, you instantly see who topped, who failed, and the overall pattern. That's the value of data visualization.

3.2 🔌 Using Pyplot of Matplotlib Library

Matplotlib is the main Python library for creating charts. Pyplot is the module inside Matplotlib that we use — it provides simple functions to draw plots.

graph TD
    MLIB["📦 Matplotlib Library\n(The whole package)"]
    PLOT["📊 pyplot Module\n(What we actually use)"]
    PLT["plt alias\n(Short name for pyplot)"]

    MLIB --> PLOT --> PLT

    style MLIB fill:#9C27B0,color:#fff
    style PLOT fill:#2196F3,color:#fff
    style PLT fill:#4CAF50,color:#fff

3.2.1 Installing and Importing Matplotlib 🔧

Step 1 — Install (done once in terminal):

pip install matplotlib

Step 2 — Import in your Python file:

# standard_import.py
import matplotlib.pyplot as plt    # plt is the universal alias — always use this!

3.2.2 Working with PyPlot Methods 🛠️

Every chart follows the same 5-step structure:

# step_structure.py
import matplotlib.pyplot as plt

# Step 1: Define your data
x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 30, 20]

# Step 2: Create the chart
plt.plot(x, y)

# Step 3: Add labels and title
plt.title("My First Chart")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# Step 4: Save (BEFORE show!)
plt.savefig("my_chart.png")

# Step 5: Display
plt.show()

The Golden Rule of PyPlot:

Define Data → Plot → Customize → Save → Show

3.3 📈 Creating Line Charts and Scatter Charts

3.3.1 Line Chart using plot() Function

A Line Chart connects data points with a line — best for showing trends over time.

Real Example: Tracking your monthly mobile data usage, temperature across days, or company revenue over years.

# basic_line_chart.py
import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales  = [5000, 7000, 6500, 8000, 7500, 9000]

plt.plot(months, sales)      # Basic line chart

plt.title("Monthly Sales 2024")
plt.xlabel("Month")
plt.ylabel("Sales (₹)")
plt.show()

With only Y values (X is auto-generated as 0, 1, 2...):

# auto_x_values.py
import matplotlib.pyplot as plt

marks = [85, 90, 78, 92, 88]
plt.plot(marks)     # X = 0, 1, 2, 3, 4 automatically
plt.title("Student Marks")
plt.show()

3.3.2 Specifying Plot Size and Grid 📐

# plot_size_grid.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 35, 28]

# Set figure size (width, height) in inches:
plt.figure(figsize=(10, 5))    # 10 inches wide, 5 inches tall

plt.plot(x, y)

# Add grid lines to make values easier to read:
plt.grid(True)              # Add grid
plt.grid(color='gray', linestyle='--', linewidth=0.5)  # Custom grid style

plt.title("Sales Trend")
plt.show()

3.3.3 Applying Various Settings in plot() Function 🎨

The plot() function has many parameters to style your line:

# styled_line_chart.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 35, 28]

plt.figure(figsize=(8, 5))

plt.plot(x, y,
         color='red',           # Line color
         linestyle='--',        # Dashed line
         linewidth=2,           # Line thickness
         marker='o',            # Circle marker at each point
         markersize=8,          # Marker size
         markerfacecolor='blue', # Marker fill color
         label='Sales Data'     # Label for legend
        )

plt.title("Styled Line Chart")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.legend()       # Shows the label
plt.grid(True)
plt.show()

Common plot() Parameters — Quick Reference:

Color shortcuts:

Multiple lines on same chart:

# multiple_lines.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
maths   = [85, 90, 78, 92, 88]
science = [88, 75, 82, 79, 91]
english = [70, 80, 75, 85, 78]

plt.figure(figsize=(9, 5))
plt.plot(x, maths,   color='blue',  marker='o', label='Maths',   linestyle='-')
plt.plot(x, science, color='red',   marker='s', label='Science', linestyle='--')
plt.plot(x, english, color='green', marker='^', label='English', linestyle=':')

plt.title("Subject-wise Marks Trend")
plt.xlabel("Test Number")
plt.ylabel("Marks")
plt.legend()
plt.grid(True)
plt.show()

3.3.4 Creating Scatter Charts 🌌

A Scatter Chart shows individual data points WITHOUT connecting them with lines — used to see the relationship (correlation) between two variables.

Real Example: Does studying more hours lead to higher marks? Plot hours on X, marks on Y — if points go up-right, there IS a correlation!

# scatter_chart.py
import matplotlib.pyplot as plt

study_hours = [2, 3, 4, 5, 6, 7, 8, 9, 10]
marks       = [45, 55, 60, 70, 72, 80, 85, 88, 95]

plt.figure(figsize=(8, 5))

plt.scatter(study_hours, marks,
            color='purple',      # Point color
            marker='o',          # Point shape
            s=100,               # Point size (s not markersize!)
            label='Students',
            edgecolors='black'   # Border color of markers
           )

plt.title("Study Hours vs Marks")
plt.xlabel("Study Hours per Day")
plt.ylabel("Marks Obtained")
plt.legend()
plt.grid(True)
plt.show()

Line Chart vs Scatter Chart — Key Differences:

3.4 📊 Creating Bar Charts and Pie Charts

Bar Charts — The "Comparison King" 👑

A Bar Chart compares values across categories. Each bar's height represents a value.

# basic_bar_chart.py
import matplotlib.pyplot as plt

subjects = ['Maths', 'Science', 'English', 'Hindi', 'IP']
marks    = [92, 88, 75, 80, 95]

plt.figure(figsize=(8, 5))
plt.bar(subjects, marks)

plt.title("Subject-wise Marks")
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.show()

3.4.1 Changing Widths of the Bars 📏

The default bar width is 0.8. You can make bars thinner or wider:

# bar_width_demo.py
import matplotlib.pyplot as plt

subjects = ['Maths', 'Science', 'English']
marks    = [92, 88, 75]

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

axes[0].bar(subjects, marks, width=0.3)    # Thin bars
axes[0].set_title("Width = 0.3 (Thin)")

axes[1].bar(subjects, marks, width=0.6)    # Default-ish
axes[1].set_title("Width = 0.6 (Normal)")

axes[2].bar(subjects, marks, width=0.9)    # Wide bars
axes[2].set_title("Width = 0.9 (Wide)")

plt.tight_layout()
plt.show()

width= accepts values between 0 and 1. Default is 0.8. Smaller = thinner bars with more gap.

3.4.2 Changing Colors of the Bars 🎨

# bar_colors_demo.py
import matplotlib.pyplot as plt

subjects = ['Maths', 'Science', 'English', 'Hindi', 'IP']
marks    = [92, 88, 75, 80, 95]

# Single color for all bars:
plt.figure(figsize=(8, 4))
plt.bar(subjects, marks, color='coral')
plt.title("Single Color")
plt.show()

# Different color for each bar (list of colors):
plt.figure(figsize=(8, 4))
colors = ['red', 'blue', 'green', 'orange', 'purple']
plt.bar(subjects, marks, color=colors, edgecolor='black')
plt.title("Multiple Colors")
plt.show()

Popular colors for bar charts:

# Using hex codes for nicer colors:
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']
plt.bar(subjects, marks, color=colors, edgecolor='black', linewidth=0.5)

3.4.3 Creating Multiple (Grouped) Bars Chart 📊📊

When comparing two groups (like Section A vs Section B marks), you need side-by-side bars:

# multiple_bars_chart.py
import matplotlib.pyplot as plt
import numpy as np

subjects   = ['Maths', 'Science', 'English']
section_a  = [90, 85, 78]
section_b  = [82, 92, 74]

# Key trick: shift bar positions manually using numpy
bar_width = 0.35                        # Width of each bar
x = np.arange(len(subjects))           # [0, 1, 2] — base positions

plt.figure(figsize=(8, 5))

# Place Section A bars at x positions
plt.bar(x - bar_width/2, section_a,
        width=bar_width,
        label='Section A',
        color='steelblue',
        edgecolor='black')

# Place Section B bars SHIFTED RIGHT by bar_width
plt.bar(x + bar_width/2, section_b,
        width=bar_width,
        label='Section B',
        color='coral',
        edgecolor='black')

plt.title("Section A vs Section B — Subject Comparison")
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.xticks(x, subjects)          # Replace 0,1,2 with subject names
plt.legend()
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

3.4.4 Creating a Horizontal Bar Chart 📊↔️

Sometimes category names are long — a horizontal bar chart is easier to read:

# horizontal_bar.py
import matplotlib.pyplot as plt

countries     = ['India', 'China', 'USA', 'Indonesia', 'Pakistan']
population    = [144, 141, 33, 28, 23]     # in crores (approx)

plt.figure(figsize=(8, 5))

plt.barh(countries, population,           # barh = horizontal bar
         color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7'],
         edgecolor='black')

plt.title("Population by Country (Crores)")
plt.xlabel("Population (Crores)")
plt.ylabel("Country")
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.show()

plt.bar() → Vertical bars (height = value) plt.barh() → Horizontal bars (width = value) Notice: arguments swap — first argument is Y (categories), second is width!

3.4.5 Creating Pie Charts 🥧

A Pie Chart shows what percentage each category is of the whole. Best when you have 3–6 categories.

# basic_pie_chart.py
import matplotlib.pyplot as plt

subjects = ['Maths', 'Science', 'English', 'Hindi', 'IP']
marks    = [92, 88, 75, 80, 95]

plt.figure(figsize=(7, 7))

plt.pie(marks,
        labels=subjects,           # Category names
        autopct='%1.1f%%',        # Show percentage (1 decimal place)
        startangle=90             # Start from top (12 o'clock)
       )

plt.title("Marks Distribution by Subject")
plt.show()

Advanced Pie Chart with Explosion and Shadow:

# advanced_pie.py
import matplotlib.pyplot as plt

categories = ['Food', 'Transport', 'Entertainment', 'Savings', 'Others']
amounts    = [35, 20, 15, 20, 10]
colors     = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']
explode    = (0.1, 0, 0, 0, 0)    # Pull out FIRST slice (Food) by 10%

plt.figure(figsize=(8, 8))

plt.pie(amounts,
        labels=categories,
        colors=colors,
        autopct='%1.1f%%',        # Show % on each slice
        startangle=140,           # Starting angle
        explode=explode,          # Pull out first slice
        shadow=True               # Add shadow for 3D look
       )

plt.title("Monthly Expense Breakdown", fontsize=16)
plt.show()

Pie Chart Parameters — Quick Reference:

3.5 ⚙️ Customizing the Plot

3.5.1 Anatomy of a Chart 🏗️

Every Matplotlib chart has these parts:

┌─────────────────────────────────────────────────┐
│                    TITLE                         │
│                                                  │
│  Y │  *     *                                   │
│  A │    *     *     *                            │
│  X │         Chart Area (Axes)      *            │
│  I │                                             │
│  S │                                             │
│    └─────────────────────────────────────────    │
│                  X AXIS                          │
│                  LEGEND: ── Series1  -- Series2  │
└─────────────────────────────────────────────────┘

3.5.2 Adding a Title 📝

# title_demo.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 30, 25]
plt.plot(x, y)

# Simple title:
plt.title("Sales Data")

# Styled title:
plt.title("Sales Data 2024",
          fontsize=16,          # Font size
          fontweight='bold',    # Bold text
          color='darkblue',     # Title color
          pad=20                # Space between title and chart
         )
plt.show()

3.5.3 Setting X and Y Labels, Limits and Ticks 🔢

# labels_limits_ticks.py
import matplotlib.pyplot as plt

months = [1, 2, 3, 4, 5, 6]
sales  = [5000, 7000, 6500, 8000, 7500, 9000]

plt.figure(figsize=(9, 5))
plt.plot(months, sales, color='steelblue', marker='o')

# Axis Labels:
plt.xlabel("Month Number", fontsize=12)
plt.ylabel("Sales in ₹", fontsize=12)

# Axis Limits (set the visible range):
plt.xlim(0, 7)          # X-axis from 0 to 7
plt.ylim(4000, 10000)   # Y-axis from 4000 to 10000

# Ticks — customize the marks on the axes:
plt.xticks([1,2,3,4,5,6],
           ['Jan','Feb','Mar','Apr','May','Jun'],
           rotation=45)   # Rotate tick labels

plt.yticks([4000, 5000, 6000, 7000, 8000, 9000, 10000])

plt.title("Monthly Sales")
plt.grid(True)
plt.tight_layout()     # Fixes overlapping labels
plt.show()

Summary of customization functions:

3.5.4 Adding Legends 🗺️

A Legend is a key that explains what each line/bar in the chart represents. Essential when you have multiple datasets!

# legend_demo.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
maths   = [85, 90, 78, 92, 88]
science = [80, 75, 88, 82, 91]

plt.figure(figsize=(8, 5))

# MUST add label= parameter to each plot for legend to work:
plt.plot(x, maths,   label='Maths',   color='blue',  marker='o')
plt.plot(x, science, label='Science', color='red',   marker='s')

# Simple legend:
plt.legend()

# Positioned legend:
plt.legend(loc='upper left')     # Options: upper/lower + left/center/right

# Custom styled legend:
plt.legend(loc='best',           # 'best' auto-picks best position
           fontsize=10,
           title='Subjects',
           framealpha=0.8)        # Legend box transparency

plt.title("Subject Marks Comparison")
plt.xlabel("Test Number")
plt.ylabel("Marks")
plt.show()

Legend loc options:

3.5.5 Saving a Figure 💾

# save_figure.py
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 30, 20]

plt.plot(x, y, color='blue')
plt.title("My Chart")

# SAVE FIRST, then show!
plt.savefig("my_chart.png")        # Save as PNG
plt.savefig("my_chart.pdf")        # Save as PDF
plt.savefig("my_chart.jpg", dpi=300)   # High resolution JPEG
plt.savefig("my_chart.svg")        # Scalable Vector Graphics

plt.show()    # Always AFTER savefig!

savefig() parameters:

3.6 📊 Creating Histograms with PyPlot

A Histogram shows how frequently values fall into different ranges (called bins). It's used for continuous numerical data.

Analogy: Divide 100 students' marks into groups: 0-20, 20-40, 40-60, 60-80, 80-100. Count how many fall in each group. That count-per-range is a histogram!

Key Difference from Bar Chart:

Bar Chart:    Discrete categories (Subjects, Cities)  → Gaps between bars
Histogram:    Continuous ranges (Marks, Heights)      → No gaps between bars

# histogram_demo.py
import matplotlib.pyplot as plt

marks = [12, 23, 34, 45, 45, 56, 67, 67, 67, 78,
         78, 78, 89, 89, 90, 92, 94, 96, 98, 100]

plt.figure(figsize=(9, 5))

plt.hist(marks,
         bins=5,              # Divide data into 5 ranges
         color='steelblue',
         edgecolor='black',   # Black border between bars
         linewidth=0.5
        )

plt.title("Distribution of Student Marks")
plt.xlabel("Marks Range")
plt.ylabel("Number of Students")
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

Understanding bins:

# bins=5 means 5 groups:
# Bin 1: 12–31  (few students)
# Bin 2: 31–51  (some students)
# Bin 3: 51–71  (more students)
# Bin 4: 71–90  (most students)
# Bin 5: 90–100 (many students)

Custom bin edges:

plt.hist(marks,
         bins=[0, 20, 40, 60, 80, 100],   # Define exact bin edges
         color='coral',
         edgecolor='black')

Histogram with density (frequency as %):

plt.hist(marks,
         bins=10,
         density=True,         # Show as proportion (0 to 1)
         color='lightgreen',
         edgecolor='black')
plt.ylabel("Frequency Density")

3.7 📉 Creating Frequency Polygons

A Frequency Polygon is a line graph drawn by connecting the midpoints of each bar in a histogram. It shows the shape of the data distribution.

Think of it as: Drawing a line chart ON TOP of a histogram, but connecting the middle-top points of each bar.

# frequency_polygon.py
import matplotlib.pyplot as plt
import numpy as np

marks = [12, 23, 34, 45, 45, 56, 67, 67, 67, 78,
         78, 78, 89, 89, 90, 92, 94, 96, 98, 100]

plt.figure(figsize=(10, 6))

# Step 1: Create histogram and get frequency counts:
counts, bin_edges, patches = plt.hist(marks,
                                       bins=5,
                                       color='lightblue',
                                       edgecolor='black',
                                       alpha=0.6,    # Semi-transparent
                                       label='Histogram')

# Step 2: Calculate midpoints of each bin:
bin_midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2

# Step 3: Draw frequency polygon (line connecting midpoints):
plt.plot(bin_midpoints, counts,
         color='red',
         marker='o',
         linewidth=2,
         markersize=6,
         label='Frequency Polygon')

plt.title("Marks Distribution — Histogram + Frequency Polygon")
plt.xlabel("Marks")
plt.ylabel("Frequency (No. of Students)")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Step-by-step explanation:

Bin 1: 12–31  → midpoint = 21.5 → count = 2
Bin 2: 31–51  → midpoint = 41   → count = 3
Bin 3: 51–71  → midpoint = 61   → count = 4
Bin 4: 71–90  → midpoint = 80   → count = 6
Bin 5: 90–100 → midpoint = 95   → count = 5

Frequency Polygon connects:
(21.5, 2) → (41, 3) → (61, 4) → (80, 6) → (95, 5)

3.8 📦 Creating Box Plots

A Box Plot (also called Box-and-Whisker plot) shows the statistical summary of a dataset in one compact visual — it shows spread, middle, and outliers all at once!

What does a Box Plot show?

Minimum       Q1        Median       Q3          Maximum
   |──────────[   BOX    |    BOX   ]──────────────|
   ↑           ↑          ↑          ↑              ↑
  Whisker    25th%      50th%      75th%         Whisker

   *                                              ← Outliers (dots outside whiskers)

# box_plot_demo.py
import matplotlib.pyplot as plt

# Marks of three different sections:
section_a = [78, 85, 90, 92, 88, 76, 95, 82, 87, 91]
section_b = [55, 65, 70, 78, 82, 60, 88, 72, 68, 75]
section_c = [90, 92, 95, 98, 88, 94, 97, 85, 91, 96]

data = [section_a, section_b, section_c]

plt.figure(figsize=(9, 6))

plt.boxplot(data,
            labels=['Section A', 'Section B', 'Section C'],
            notch=False,          # True = notched box (shows CI of median)
            patch_artist=True,    # Fill boxes with color
            boxprops=dict(facecolor='lightblue', color='navy'),
            medianprops=dict(color='red', linewidth=2),
            whiskerprops=dict(color='gray'),
            flierprops=dict(marker='o', color='red', markersize=6)
           )

plt.title("Section-wise Marks Distribution (Box Plot)")
plt.xlabel("Section")
plt.ylabel("Marks")
plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.show()

Simple Box Plot (for exam use):

# simple_boxplot.py
import matplotlib.pyplot as plt

marks = [45, 55, 60, 65, 70, 72, 75, 78, 80, 85, 88, 90, 92, 95, 98]

plt.boxplot(marks)
plt.title("Marks Distribution")
plt.ylabel("Marks")
plt.show()

Multiple box plots for comparison:

plt.boxplot([section_a, section_b, section_c])
plt.xticks([1, 2, 3], ['Section A', 'Section B', 'Section C'])

3.9 📊 Plotting Data from a DataFrame

Real-world data is stored in DataFrames — here's how to create charts directly from Pandas DataFrames!

3.9.1 Plotting a DataFrame's Data using PyPlot's Graph Functions

You can pass DataFrame columns directly to plt functions:

# df_to_pyplot.py
import pandas as pd
import matplotlib.pyplot as plt

# Create sample DataFrame:
data = {
    'Month':   ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'Sales_A': [5000, 7000, 6500, 8000, 7500, 9000],
    'Sales_B': [4000, 6000, 7500, 7000, 8500, 8000]
}
df = pd.DataFrame(data)
print(df)

# Line Chart from DataFrame columns:
plt.figure(figsize=(9, 5))
plt.plot(df['Month'], df['Sales_A'],
         color='blue', marker='o', label='Branch A')
plt.plot(df['Month'], df['Sales_B'],
         color='red',  marker='s', label='Branch B')

plt.title("Monthly Sales Comparison")
plt.xlabel("Month")
plt.ylabel("Sales (₹)")
plt.legend()
plt.grid(True)
plt.show()

# Bar Chart from DataFrame:
plt.figure(figsize=(9, 5))
plt.bar(df['Month'], df['Sales_A'],
        color='steelblue', label='Branch A')
plt.title("Branch A Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.legend()
plt.show()

# Scatter Chart from DataFrame:
plt.figure(figsize=(7, 5))
plt.scatter(df['Sales_A'], df['Sales_B'],
            color='purple', s=100, label='Month')
plt.title("Sales A vs Sales B Correlation")
plt.xlabel("Branch A Sales")
plt.ylabel("Branch B Sales")
plt.legend()
plt.show()

3.9.2 Plotting a DataFrame's Data using DataFrame's plot() 🐼

The easiest way! DataFrames have a built-in plot() method that creates charts directly:

# df_plot_method.py
import pandas as pd
import matplotlib.pyplot as plt

data = {
    'Month':    ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Maths':    [85, 90, 78, 92, 88],
    'Science':  [88, 82, 75, 90, 85],
    'English':  [75, 80, 72, 85, 79]
}
df = pd.DataFrame(data)
df = df.set_index('Month')    # Set Month as index for clean charts

# LINE CHART (default):
df.plot(kind='line', figsize=(9, 5), marker='o')
plt.title("Monthly Marks — Line Chart")
plt.ylabel("Marks")
plt.grid(True)
plt.show()

# BAR CHART:
df.plot(kind='bar', figsize=(9, 5), color=['blue', 'red', 'green'])
plt.title("Monthly Marks — Bar Chart")
plt.ylabel("Marks")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# HORIZONTAL BAR:
df.plot(kind='barh', figsize=(9, 6))
plt.title("Monthly Marks — Horizontal Bar")
plt.show()

# AREA CHART (filled line chart):
df.plot(kind='area', figsize=(9, 5), alpha=0.6)
plt.title("Monthly Marks — Area Chart")
plt.show()

Plotting a SINGLE column:

# Single column pie chart:
avg_marks = pd.Series({
    'Maths': 86.6,
    'Science': 84.0,
    'English': 78.2
})

avg_marks.plot(kind='pie',
               autopct='%1.1f%%',
               figsize=(7, 7),
               startangle=90)
plt.title("Average Marks Distribution")
plt.ylabel("")         # Remove default y-label for pie charts
plt.show()

All kind= options for DataFrame's plot():

# Complete example with all customizations:
df.plot(kind='bar',
        x='Month',        # (if not using index)
        y=['Maths', 'Science'],   # Plot only specific columns
        color=['navy', 'coral'],
        figsize=(10, 6),
        width=0.7,
        edgecolor='black',
        title="Subject Marks Comparison"
       )

plt.xlabel("Month")
plt.ylabel("Marks")
plt.legend(loc='upper left')
plt.tight_layout()
plt.savefig("subject_chart.png", dpi=150, bbox_inches='tight')
plt.show()

df.plot(kind='bar', color='steelblue')
plt.title("My Chart")
plt.show()

📐 All Chart Types — Quick Comparison

graph TD
    DATA["Your Data"]
    TREND["Shows TREND over time?"]
    COMP["COMPARING categories?"]
    PART["Parts of a WHOLE?"]
    DIST["DISTRIBUTION of numbers?"]
    CORR["RELATIONSHIP between 2 vars?"]
    STATS["STATISTICAL SPREAD?"]

    DATA --> TREND
    DATA --> COMP
    DATA --> PART
    DATA --> DIST
    DATA --> CORR
    DATA --> STATS

    TREND --> LINE["📈 Line Chart\nplt.plot()"]
    COMP --> BAR["📊 Bar Chart\nplt.bar()"]
    PART --> PIE["🥧 Pie Chart\nplt.pie()"]
    DIST --> HIST["📉 Histogram\nplt.hist()"]
    CORR --> SCAT["🌌 Scatter Chart\nplt.scatter()"]
    STATS --> BOX["📦 Box Plot\nplt.boxplot()"]

    style LINE fill:#2196F3,color:#fff
    style BAR fill:#4CAF50,color:#fff
    style PIE fill:#FF9800,color:#fff
    style HIST fill:#9C27B0,color:#fff
    style SCAT fill:#F44336,color:#fff
    style BOX fill:#009688,color:#fff

⚠️ Common Errors and Misconceptions

import matplotlib.pyplot as plt

🎯 Sample Board Exam Questions

Q1: Very Short Answer [1 mark each]

a) Write the import statement for Matplotlib's pyplot. → import matplotlib.pyplot as plt

b) Which function is used to display a pie chart in Matplotlib? → plt.pie()

c) What does autopct='%1.1f%%' do in a pie chart? → It displays the percentage value on each slice of the pie chart, formatted to 1 decimal place.

d) What is the difference between plt.bar() and plt.barh()? → plt.bar() creates a vertical bar chart; plt.barh() creates a horizontal bar chart.

e) Why should savefig() be called before show()? → Because show() clears the figure from memory. If savefig() is called after show(), it saves a blank image.

Q2: Short Answer [2 marks]

Q: What is a histogram? How is it different from a bar chart?

A histogram is a graph that shows the frequency distribution of continuous data grouped into ranges called bins.

Q3: Program Writing [3 marks]

Q: Write a Python program to create a bar chart showing marks of 5 students.

import matplotlib.pyplot as plt

students = ['Arjun', 'Bina', 'Chirag', 'Divya', 'Esha']
marks    = [85, 90, 78, 92, 88]

plt.bar(students, marks, color='steelblue', edgecolor='black')

plt.title("Student Marks")
plt.xlabel("Student Name")
plt.ylabel("Marks")
plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.savefig("marks_chart.png")
plt.show()

Q4: Program Writing [4 marks]

Q: Create a DataFrame of monthly sales for two branches and plot a line chart with legends.

import pandas as pd
import matplotlib.pyplot as plt

data = {
    'Month':    ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Branch_A': [5000, 7000, 6500, 8000, 9000],
    'Branch_B': [4000, 6000, 7500, 7000, 8500]
}
df = pd.DataFrame(data)
df = df.set_index('Month')

df.plot(kind='line', figsize=(9, 5), marker='o')
plt.title("Monthly Sales — Both Branches")
plt.ylabel("Sales (₹)")
plt.grid(True)
plt.legend(loc='upper left')
plt.tight_layout()
plt.savefig("sales_chart.png")
plt.show()

✏️ Practice Problems

1. Create a line chart showing daily temperature for 7 days. Use red dashed line, square markers, and add grid.

2. Create a pie chart showing how you spend 24 hours: Sleep(8), School(7), Study(4), Play(2), Other(3).

3. Write code to create a histogram of these marks: [45, 52, 63, 71, 72, 80, 82, 85, 87, 90, 91, 93, 95] with 4 bins.

4. Create a multiple bar chart comparing Section A and Section B marks in Maths, Science, and English.

5. Create a Pandas DataFrame of 5 products with their prices. Plot a horizontal bar chart using df.plot(kind='barh').

6. Create a box plot comparing the marks of three classes. What does the box represent?

7. What is a Frequency Polygon? Write code to overlay a frequency polygon on a histogram.

8. Write code to create a scatter chart showing relationship between study hours (2-10) and marks (50-95).

9. Create a line chart with two lines, add a legend, set axis limits, and save the chart as "result.png".

10. What is the kind= parameter in df.plot()? List at least 5 chart types you can create using it.