Data Visualization and Preprocessing

Data Preprocessing

• Data cleaning and data transformation are explored and discussed.
• The importance of data cleaning, common challenges, and effective techniques are understood.

Data Visualization

• Helps in understanding complex data through visual representations.
• Reveals trends, patterns, and outliers in data.
• Makes data insights accessible to stakeholders unfamiliar with technical analysis.

Visualization Types

• Exploratory Visualizations are useful to explore a dataset and identify trends or patterns
• Explanatory Visualizations are useful to communicate analysis results to a broader audience

Essential Libraries

• Matplotlib, Seaborn, and Plotly are essential libraries.

Best Practices for Choosing the Right Plot

• Line Plots are best for time series or continuous data.
• Bar Plots are best for categorical data comparisons.
• Scatter Plots are best for relationships between two continuous variables.
• Box Plots are best for distribution and outliers.
• Heatmaps are best for correlation matrices.

Best Practices for Aesthetic Design

• Keep it simple and clean to avoid clutter.
• Use appropriate colors; avoid too many and consider colorblind accessibility.
• Label axes and provide a clear title.

Considerations for Color Usage

• Ensure chosen colors do not confuse interpretation.
• Use color to highlight important parts of data, but not to overwhelm the viewer.

Visualizing Data in Python

• Install Matplotlib, Seaborn, and Plotly using pip: pip install matplotlib seaborn plotly

Imports for Data Visualization

• import matplotlib.pyplot as plt
• import seaborn as sns
• import plotly.express as px
• import pandas as pd
• import numpy as np

Basic Plots - Line Plot

• Line plots represent time series data, showing trends over a continuous interval.
• Example using Matplotlib:
  • x = np.linspace(0, 10, 100)
  • y = np.sin(x)
  • plt.plot(x, y, label="Sine Wave", color='blue')
  • plt.title("Line Plot Example")
  • plt.xlabel("X-axis")
  • plt.ylabel("Y-axis")
  • plt.legend()
  • plt.show()

Bar Plot

• Used to compare quantities across different categories.
• Example using Seaborn:
  • categories = ['A', 'B', 'C', 'D']
  • values = [10, 15, 7, 12]
  • sns.barplot(x=categories, y=values, palette='coolwarm')
  • plt.title("Bar Plot Example")
  • plt.show()

Histogram

• Used to show the distribution of a dataset
• Example using Matplotlib:
  • data = np.random.randn(1000)
  • plt.hist(data, bins=30, color='purple', alpha=0.7)
  • plt.title("Histogram Example")
  • plt.xlabel("Value")
  • plt.ylabel("Frequency")
  • plt.show()

Box Plot

• Used to show the distribution of a dataset and detect outliers.
• Example using Seaborn:
  • sns.boxplot(data=data)
  • plt.title("Box Plot Example")
  • plt.show()

Scatter Plot

• Used to represent the relationship between two variables.
• Example using Seaborn:
  • x = np.random.randn(100)
  • y = 2 * x + np.random.randn(100)
  • sns.scatterplot(x=x, y=y)
  • plt.title("Scatter Plot Example")
  • plt.show()

Heatmap

• Useful for visualizing correlations between variables in a matrix format.
• Example using Seaborn:
  • data_matrix = np.random.rand(10, 12)
  • sns.heatmap(data_matrix, cmap="YlGnBu", annot=True)
  • plt.title("Heatmap Example")
  • plt.show()

Pie Chart

• Useful for showing proportions of a whole.
• Example using Matplotlib:
  • sizes = [15, 30, 45, 10]
  • labels = ['A', 'B', 'C', 'D']
  • colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
  • plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
  • plt.title("Pie Chart Example")
  • plt.show()

Pair Plot

• Used to visualize pairwise relationships between multiple variables.
• Example using Seaborn:
  • iris = sns.load_dataset("iris")
  • sns.pairplot(iris, hue="species")
  • plt.title("Pair Plot Example")
  • plt.show()

Matplotlib - Simple Line Plot

• Example:
  • x = [1, 2, 3, 4, 5]
  • y = [2, 4, 6, 8, 10]
  • plt.plot(x, y, color='green', marker='o')
  • plt.title("Matplotlib Line Plot")
  • plt.xlabel("X-axis")
  • plt.ylabel("Y-axis")
  • plt.grid(True)
  • plt.show()

Matplotlib - Customize Plot

• Example:
  • plt.plot(x, y, label="Line", color="red", linestyle='--', marker='x')
  • plt.title("Customized Plot")
  • plt.xlabel("X-axis")
  • plt.ylabel("Y-axis")
  • plt.legend(loc='best')
  • plt.grid(True)
  • plt.show()

Statistical Plots - Boxplot using Seaborn

• Example:
  • sns.boxplot(data=iris, x='species', y='sepal_length')
  • plt.title("Boxplot Example")
  • plt.show()

Statistical Plots - Violin Plot using Seaborn

• Combines aspects of box plots and density plots.
• Example:
  • sns.violinplot(x='species', y='sepal_length', data=iris, palette='muted')
  • plt.title("Violin Plot Example")
  • plt.show()

Interactive Scatter Plot using Plotly

• Plotly interactive plots can be embedded into web applications.
• Example:
  • fig = px.scatter(iris, x="sepal_width", y="sepal_length", color="species", title="Interactive Scatter Plot")
  • fig.show()

Practice Exercises

• Create a line plot comparing two different time series datasets.
• Create a box plot and violin plot using Seaborn for the Iris dataset and compare the distributions.
• Create an interactive heatmap using Plotly to show correlations between variables.

Integrated Development Environment (IDE)

• Designed to simplify the software development process
• A comprehensive set of tools within a single interface aids in developing, managing, compiling, testing, deploying, and debugging code
• IDE selection depends on the programming language and specific project requirements

Main Components of an IDE

• Source Code Editor: Provides syntax highlighting, auto-completion, and code formatting
• Compiler/Interpreter: Converts code into machine-executable form
• Debugger: Helps find and fix errors in the code
• Build Automation Tools: Automates repetitive tasks like compiling, linking, and packaging
• Version Control Integration: Supports Git, SVN, and other version control systems
• Terminal/Command Line Interface: Allows running scripts and commands inside the IDE
• Project Management Tools: Organizes files, dependencies, and libraries

Commonly Used IDEs for Data Science

• Spyder is useful for scientific computing, data cleaning, statistical analysis, and small projects
  • Language: Python
  • Has a Dataframe viewer for Pandas and part of Anaconda
  • has built in debugging and profiling tools
  • Matlab-like interface with variable explorer, console, and plots
  • Comes with Anaconda or pip install spyder
• PyCharm is useful for Python development, large-scale data science, AI, and ML projects
  • Language: Python
  • Advanced code completion and debugging
  • Has Virtual environment and package management
  • Integrated with Jupyter Notebook, GitHub, Docker, and database
  • is best for full-scale machine learning applications
• Jupyter Notebook is useful for machine learning, data analysis, and data visualization; best for beginners
  • Language: Python, R
  • Web-based interface and supports Markdowns for documentation
  • Excellent for data visualization (Matplotlib, Seaborn, Plotly)
  • Integration to Python libraries like NumPy, Pandas, Scikit-learn
  • Easy to share notebooks via .ipynb format
  • Available via Anaconda or pip install jupyter
• RStudio is useful for statistical analysis, data visualization, and R programming.
  • Language: R, Python
  • Optimized for R-based data science workflows
  • Integrated support for R Markdown & Shiny Apps
  • SQL & Python support via Reticulate
  • Best for R-based data science
• Visual Studio Code (VS Code) is useful for machine learning, deep learning, and big data.
  • Language: General Purpose, Python, R, SQL, Julia, etc.
  • Extensions for Python, Jupyter, R, SQL
  • Has support for Git version control with integrated terminal and debugging tools