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Line Plot

  • This module explains how to create and customize line plots in Seaborn. You will learn about confidence intervals, plotting multiple lines, and applying styling options for clear and effective data visualization.

  • Creating Line Plot

    Theory

    • Used for time series data

    • Shows increasing / decreasing trends

    • Helps identify patterns

    Key Components:

    • x → Independent variable (time, months, etc.)

    • y → Dependent variable (sales, marks, growth, etc.)

    • plt.plot() → Draws line

    • plt.grid() → Adds grid

    • plt.title() → Title

    Example image shown above: Basic Line Plot

Monthly Sales – Basic Line Plot

This code creates a line plot using Seaborn to visualize the trend of sales over months. 

import matplotlib.pyplot as plt
import numpy as np

# Sample Data
np.random.seed(10)
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Data
x = np.arange(1, 11)
y = np.array([4, 6, 5, 7, 6, 8, 10, 9, 11, 13])

# DataFrame banana (Seaborn ke liye better practice)
df = pd.DataFrame({
    "Months": x,
    "Sales": y
})
# Plot
plt.figure()
sns.lineplot(x="Months", y="Sales", data=df)
plt.title("Basic Line Plot")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.grid(True)
plt.show()
Lesson image

  • Confidence Interval (CI)

    Theory

    Confidence Interval shows:

    • Possible variation range

    • Uncertainty in data

    • Upper & lower bound

    We use:

    plt.fill_between(x, lower, upper)

    Why Important?

    In real-world:

    • Sales forecast range

    • Temperature prediction range

    • Statistical analysis

    Example image above: Line Plot with Confidence Interval

    Shaded area = uncertainty range.

Monthly Sales – Line Plot with Confidence Interval

This code creates a line plot using Seaborn and includes a confidence interval (CI) around the line to show the uncertainty or variability of the data. 

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Sample Data
np.random.seed(10)
x = np.arange(1, 11)
y = np.array([4, 6, 5, 7, 6, 8, 10, 9, 11, 13])
df = pd.DataFrame({
    "Months": x,
    "Sales": y
})
plt.figure()
sns.lineplot(
    x="Months",
    y="Sales",
    data=df,
    errorbar="ci"   # Confidence Interval
)
plt.title("Line Plot with Confidence Interval")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.grid(True)
plt.show()
Lesson image

  • Multiple Lines

    Theory

    Used when:

    • Comparing two or more categories

    • Comparing years

    • Comparing products

    We call plt.plot() multiple times.

    Example:

    • Product A vs Product B

    • 2024 vs 2025 sales

    Example image above: Multiple Line Plot

    Two lines = two datasets comparison.

Monthly Sales – Comparing Two Sales Trends

This code creates a line plot with two separate lines using Seaborn, allowing comparison between two sales trends over the same months. 

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Sample Data
np.random.seed(10)
x = np.arange(1, 11)
y = np.array([4, 6, 5, 7, 6, 8, 10, 9, 11, 13])
y2 = y + np.random.randint(-2, 3, size=len(y))

# DataFrame ko long format me convert karna
df = pd.DataFrame({
    "Months": list(x) * 2,
    "Sales": list(y) + list(y2),
    "Type": ["Sales 1"] * len(y) + ["Sales 2"] * len(y2)
})

# Plot
plt.figure()
sns.lineplot(x="Months", y="Sales", hue="Type", data=df)
plt.title("Multiple Line Plot")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.grid(True)
plt.show()
Lesson image

  • Styling & Customization

    Theory

    Important customization options:

    Parameter

    Purpose

    linestyle

    Solid, dashed, dotted

    marker

    Show data points

    linewidth

    Thickness

    alpha

    Transparency

    grid

    Background lines

    Example:

    plt.plot(x, y, linestyle="--", marker="o", linewidth=2)

    Example image above: Styled Line Plot

Monthly Sales – Line Plot with Custom Style

This code creates a line plot using Seaborn and applies custom styling such as dashed lines, markers, and line width. 

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Sample Data
np.random.seed(10)
x = np.arange(1, 11)
y = np.array([4, 6, 5, 7, 6, 8, 10, 9, 11, 13])

# DataFrame
df = pd.DataFrame({
    "Months": x,
    "Sales": y
})

plt.figure()
sns.lineplot(
    x="Months",
    y="Sales",
    data=df,
    linestyle="--",
    marker="o",
    linewidth=2
)

plt.title("Styled Line Plot")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.grid(True)
plt.show()
Lesson image
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