Installing Python and Visualization Libraries

# Create virtual environment
python -m venv viz_env
viz_env\Scripts\activate      # Windows
source viz_env/bin/activate   # Mac/Linux

# Install all visualization libraries
pip install matplotlib seaborn plotly dash pandas numpy jupyter

# Optional but recommended
pip install plotly-express kaleido   # Static image export for Plotly
pip install nbformat ipywidgets      # Jupyter widgets

import matplotlib
import seaborn as sns
import plotly
import pandas as pd
import numpy as np

print(f"Matplotlib: {matplotlib.__version__}")   # 3.8.x
print(f"Seaborn:    {sns.__version__}")          # 0.13.x
print(f"Plotly:     {plotly.__version__}")       # 5.18.x
print(f"Pandas:     {pd.__version__}")           # 2.1.x
print(f"NumPy:      {np.__version__}")           # 1.26.x

# Quick test — all three libraries
import matplotlib.pyplot as plt
import plotly.express as px

# Matplotlib test
fig, ax = plt.subplots()
ax.plot([1,2,3], [4,5,6])
ax.set_title('Matplotlib ✅')
plt.savefig('test_mpl.png')

# Seaborn test
sns.set_theme()
print("Seaborn ✅")

# Plotly test
fig = px.line(x=[1,2,3], y=[4,5,6], title='Plotly ✅')
fig.show()

print("\n✅ All visualization libraries ready!")

+────────────────────────────────────────────────────────────────────+
│ Library    │ Type        │ Best For                    │ Output    │
+────────────+─────────────+─────────────────────────────+───────────+
│ Matplotlib │ Static      │ Publication-quality charts  │ PNG/PDF   │
│ Seaborn    │ Statistical │ Statistical plots, EDA      │ PNG/PDF   │
│ Plotly     │ Interactive │ Web dashboards, hover info  │ HTML/JSON │
│ Dash       │ Dashboard   │ Full web analytics apps     │ Web app   │
│ Bokeh      │ Interactive │ Streaming, large datasets   │ HTML      │
│ Altair     │ Declarative │ Grammar-of-graphics style   │ HTML/PNG  │
+────────────+─────────────+─────────────────────────────+───────────+

# Launch Jupyter
# jupyter notebook   # Classic
# jupyter lab        # Modern (recommended)

# Essential magic commands for visualization
%matplotlib inline    # Show Matplotlib plots in notebook
%matplotlib widget    # Interactive Matplotlib in JupyterLab

# Global style settings (add to first cell of every notebook)
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['font.size'] = 12
plt.rcParams['axes.titlesize'] = 14
plt.rcParams['axes.titleweight'] = 'bold'
sns.set_palette('husl')

print("Visualization environment ready!")