Matplotlib

• What is Matplotlib?
  • Comprehensive library for creating static, animated, and interactive visualizations in Python.
  • Provides a wide range of plotting functionalities.
  • Highly customizable.

Matplotlib

• Key Features:
  • Versatility: Line plots, scatter plots, bar charts, histograms, and more.
  • Customization: Extensive control over plot elements.
  • Integration: Works well with other Python libraries.
  • Extensibility: Can be used as a backend for other plotting libraries.

Matplotlib

import matplotlib.pyplot as plt

# Simple line plot
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.title('Simple Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Seaborn

• What is Seaborn?
  • Data visualization library built on top of Matplotlib.
  • Provides a high-level interface for drawing attractive and informative statistical graphics.
  • Designed to work well with Pandas data structures.

Seaborn

• Key Features:
  • Ease of Use: Simplifies creating complex visualizations with less code.
  • Aesthetics: Default themes and color palettes for visually appealing plots.
  • Statistical Plots: Includes specialized plots like violin plots, box plots, and pair plots.
  • Data Handling: Works directly with Pandas DataFrames.

Seaborn

import seaborn as sns
import pandas as pd

# Load example dataset
df = sns.load_dataset('tips')

# Simple scatter plot
sns.scatterplot(x='total_bill', y='tip', data=df)
plt.title('Total Bill vs Tip')
plt.xlabel('Total Bill')
plt.ylabel('Tip')
plt.show()

1. Loading the Dataset

import pandas as pd

# Load the dataset
df = pd.read_csv('most_streamed_spotify_songs_2024.csv')

# Display the first few rows
df.head()

2. Line Plot

Used to visualize trends over time.

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

df = pd.read_csv('pokemon.csv')

# Line plot of Sp. Atk over generations
plt.figure(figsize=(12, 6))
sns.lineplot(x='Generation', y='Sp. Atk', data=df)
plt.title('Spc. Atk Over Generations')
plt.xlabel('Generation')
plt.ylabel('Spc. Atk')
plt.xticks(rotation=45)
plt.show()

3. Bar Plot

Used to compare different categories.

# Bar plot of top 10 Pokémon by total stats
top_10_pokemon = df.nlargest(10, 'Total')

plt.figure(figsize=(12, 6))
sns.barplot(x='Total', y='Name', data=top_10_pokemon)
plt.title('Top 10 Pokémon by Total Stats')
plt.xlabel('Total Stats')
plt.ylabel('Name')
plt.show()

4. Scatter Plot

Used to explore the relationship between two variables.

plt.figure(figsize=(12, 6))
sns.scatterplot(x='Attack', y='Sp. Atk', data=df)
plt.title('Attack vs Sp. Atk')
plt.xlabel('Attack')
plt.ylabel('Sp. Atk')
plt.show()

5. Histogram

Used to understand the distribution of a single variable.

# Histogram of HP
plt.figure(figsize=(12, 6))
sns.histplot(df['HP'], bins=30, kde=True)
plt.title('Distribution of HP')
plt.xlabel('HP')
plt.ylabel('Frequency')
plt.show()

6. Box Plot

Used to display the distribution of data based on a five-number summary.

# Box plot of Defense by Pokémon type
plt.figure(figsize=(12, 6))
sns.boxplot(x='Type 1', y='Defense', data=df)
plt.title('Defense by Pokémon Type')
plt.xlabel('Type 1')
plt.ylabel('Defense')
plt.xticks(rotation=45)
plt.show()

7. Heatmap

Used to visualize matrix-like data, showing correlation between variables.

# Heatmap of count of pokemon that share types
df['Type 2'].fillna(df['Type 1'], inplace=True)
type_counts = df.groupby(['Type 1', 'Type 2']).size().unstack()
plt.figure(figsize=(12, 6))
sns.heatmap(type_counts, cmap='coolwarm', annot=True)
plt.title('Count of Pokemon With Type 1 and Type 2')
plt.xlabel('Type 2')
plt.ylabel('Type 1')
plt.show()