{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bce99b4a",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"Data Visualization with Matplotlib\"\n",
    "description: \"Create professional charts and visualizations from weather data using Python's Matplotlib library\"\n",
    "date: 2025-01-27\n",
    "lastmod: 2025-01-27\n",
    "author: \"Zer0-Mistakes Team\"\n",
    "layout: notebook\n",
    "difficulty: intermediate\n",
    "tags: [python, matplotlib, visualization, data-science, charts]\n",
    "categories: [Notebooks, Tutorials]\n",
    "toc: true\n",
    "comments: true\n",
    "---\n",
    "\n",
    "# Data Visualization with Matplotlib\n",
    "\n",
    "Learn to create professional-quality charts and visualizations using Python's Matplotlib library. This tutorial uses real weather data to demonstrate various chart types including line plots, bar charts, scatter plots, and multi-panel figures.\n",
    "\n",
    "**What you'll learn:**\n",
    "- Basic line and bar charts\n",
    "- Customizing colors, labels, and legends\n",
    "- Creating subplots and multi-panel figures\n",
    "- Scatter plots with color mapping\n",
    "- Saving high-quality images for publication"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80693725",
   "metadata": {},
   "source": [
    "## Setup and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7566a988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Configure matplotlib for better display\n",
    "plt.rcParams['figure.figsize'] = [10, 6]  # Default figure size\n",
    "plt.rcParams['figure.dpi'] = 100  # Display resolution\n",
    "plt.rcParams['savefig.dpi'] = 150  # Save resolution\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['grid.alpha'] = 0.3\n",
    "\n",
    "print(\"✅ Libraries imported successfully!\")\n",
    "print(f\"Matplotlib version: {plt.matplotlib.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a7c930",
   "metadata": {},
   "source": [
    "## Load Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd368284",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the weather dataset\n",
    "weather = pd.read_csv('/Users/bamr87/github/zer0-mistakes/assets/data/notebooks/weather_data.csv', \n",
    "                      parse_dates=['date'])\n",
    "\n",
    "print(\"🌤️ Weather Data Preview:\")\n",
    "print(f\"Shape: {weather.shape[0]} days × {weather.shape[1]} columns\\n\")\n",
    "weather.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80f9fc2",
   "metadata": {},
   "source": [
    "## Basic Line Chart: Temperature Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a3f184c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a simple line chart showing temperature trends\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "# Plot each city as a separate line\n",
    "cities = weather['city'].unique()\n",
    "colors = ['#e63946', '#457b9d', '#2a9d8f', '#e9c46a', '#264653']\n",
    "\n",
    "for i, city in enumerate(cities):\n",
    "    city_data = weather[weather['city'] == city]\n",
    "    ax.plot(city_data['date'], city_data['temperature_f'], \n",
    "            label=city, color=colors[i], linewidth=2, marker='o', markersize=4)\n",
    "\n",
    "# Customize the chart\n",
    "ax.set_xlabel('Date', fontsize=12)\n",
    "ax.set_ylabel('Temperature (°F)', fontsize=12)\n",
    "ax.set_title('Daily Temperature by City', fontsize=14, fontweight='bold')\n",
    "ax.legend(loc='upper right', frameon=True)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Rotate date labels for readability\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22909398",
   "metadata": {},
   "source": [
    "## Bar Chart: Average Temperature by City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f638b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate average temperature by city\n",
    "avg_temp = weather.groupby('city')['temperature_f'].mean().sort_values(ascending=False)\n",
    "\n",
    "# Create horizontal bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "bars = ax.barh(avg_temp.index, avg_temp.values, color=colors[:len(avg_temp)])\n",
    "\n",
    "# Add value labels on bars\n",
    "for bar, temp in zip(bars, avg_temp.values):\n",
    "    ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, \n",
    "            f'{temp:.1f}°F', va='center', fontsize=11)\n",
    "\n",
    "ax.set_xlabel('Average Temperature (°F)', fontsize=12)\n",
    "ax.set_title('Average Temperature by City', fontsize=14, fontweight='bold')\n",
    "ax.set_xlim(0, max(avg_temp.values) + 15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb54b5d4",
   "metadata": {},
   "source": [
    "## Scatter Plot: Temperature vs. Humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "851781f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create scatter plot with color-coded precipitation\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "\n",
    "scatter = ax.scatter(weather['humidity'], weather['temperature_f'],\n",
    "                     c=weather['precipitation'], cmap='Blues',\n",
    "                     s=80, alpha=0.7, edgecolors='white', linewidth=0.5)\n",
    "\n",
    "# Add colorbar\n",
    "cbar = plt.colorbar(scatter, ax=ax)\n",
    "cbar.set_label('Precipitation (inches)', fontsize=11)\n",
    "\n",
    "ax.set_xlabel('Humidity (%)', fontsize=12)\n",
    "ax.set_ylabel('Temperature (°F)', fontsize=12)\n",
    "ax.set_title('Temperature vs. Humidity\\n(Color = Precipitation)', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70141fd",
   "metadata": {},
   "source": [
    "## Multi-Panel Figure: Weather Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8284ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 2x2 dashboard with multiple visualizations\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "fig.suptitle('Weather Analysis Dashboard', fontsize=16, fontweight='bold', y=1.02)\n",
    "\n",
    "# 1. Temperature distribution (histogram)\n",
    "ax1 = axes[0, 0]\n",
    "ax1.hist(weather['temperature_f'], bins=15, color='#e63946', edgecolor='white', alpha=0.7)\n",
    "ax1.axvline(weather['temperature_f'].mean(), color='#264653', linestyle='--', \n",
    "            linewidth=2, label=f'Mean: {weather[\"temperature_f\"].mean():.1f}°F')\n",
    "ax1.set_xlabel('Temperature (°F)')\n",
    "ax1.set_ylabel('Frequency')\n",
    "ax1.set_title('Temperature Distribution')\n",
    "ax1.legend()\n",
    "\n",
    "# 2. Weather conditions pie chart\n",
    "ax2 = axes[0, 1]\n",
    "condition_counts = weather['condition'].value_counts()\n",
    "colors_pie = ['#ffd166', '#06d6a0', '#118ab2', '#073b4c', '#ef476f']\n",
    "wedges, texts, autotexts = ax2.pie(condition_counts.values, labels=condition_counts.index,\n",
    "                                    autopct='%1.1f%%', colors=colors_pie[:len(condition_counts)],\n",
    "                                    explode=[0.05] * len(condition_counts))\n",
    "ax2.set_title('Weather Conditions')\n",
    "\n",
    "# 3. Wind speed by city (box plot)\n",
    "ax3 = axes[1, 0]\n",
    "city_wind_data = [weather[weather['city'] == city]['wind_speed'].values for city in cities]\n",
    "bp = ax3.boxplot(city_wind_data, labels=cities, patch_artist=True)\n",
    "for patch, color in zip(bp['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    patch.set_alpha(0.7)\n",
    "ax3.set_xlabel('City')\n",
    "ax3.set_ylabel('Wind Speed (mph)')\n",
    "ax3.set_title('Wind Speed Distribution by City')\n",
    "\n",
    "# 4. Precipitation by city (grouped bar)\n",
    "ax4 = axes[1, 1]\n",
    "city_precip = weather.groupby('city')['precipitation'].agg(['mean', 'max']).reset_index()\n",
    "x = np.arange(len(city_precip))\n",
    "width = 0.35\n",
    "bars1 = ax4.bar(x - width/2, city_precip['mean'], width, label='Average', color='#457b9d')\n",
    "bars2 = ax4.bar(x + width/2, city_precip['max'], width, label='Maximum', color='#1d3557')\n",
    "ax4.set_xlabel('City')\n",
    "ax4.set_ylabel('Precipitation (inches)')\n",
    "ax4.set_title('Precipitation by City')\n",
    "ax4.set_xticks(x)\n",
    "ax4.set_xticklabels(city_precip['city'], rotation=45, ha='right')\n",
    "ax4.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ae7e39",
   "metadata": {},
   "source": [
    "## Saving Figures\n",
    "\n",
    "Save your visualizations as high-quality images for reports and presentations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f581f7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a publication-ready figure and save it\n",
    "import os\n",
    "\n",
    "# Create output directory if it doesn't exist\n",
    "output_dir = '/Users/bamr87/github/zer0-mistakes/assets/images/notebooks/matplotlib-visualization_files'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Create a clean, professional chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Group data by city and calculate daily averages\n",
    "for i, city in enumerate(cities):\n",
    "    city_data = weather[weather['city'] == city]\n",
    "    ax.plot(city_data['date'], city_data['temperature_c'], \n",
    "            label=city, color=colors[i], linewidth=2.5)\n",
    "\n",
    "ax.set_xlabel('Date', fontsize=12)\n",
    "ax.set_ylabel('Temperature (°C)', fontsize=12)\n",
    "ax.set_title('Temperature Trends Across Cities', fontsize=14, fontweight='bold')\n",
    "ax.legend(loc='upper right', frameon=True, fancybox=True, shadow=True)\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Save in multiple formats\n",
    "fig.savefig(f'{output_dir}/temperature_trends.png', dpi=150, bbox_inches='tight',\n",
    "            facecolor='white', edgecolor='none')\n",
    "fig.savefig(f'{output_dir}/temperature_trends.svg', bbox_inches='tight',\n",
    "            facecolor='white', edgecolor='none')\n",
    "\n",
    "print(f\"✅ Figures saved to: {output_dir}\")\n",
    "print(\"   - temperature_trends.png (150 DPI)\")\n",
    "print(\"   - temperature_trends.svg (vector format)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d6ceb7",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned:\n",
    "\n",
    "1. **Line charts** - Show trends over time with `plt.plot()`\n",
    "2. **Bar charts** - Compare categories with `plt.bar()` and `plt.barh()`\n",
    "3. **Scatter plots** - Visualize relationships with color mapping using `plt.scatter()`\n",
    "4. **Subplots** - Create multi-panel dashboards with `plt.subplots()`\n",
    "5. **Saving figures** - Export high-quality images with `fig.savefig()`\n",
    "\n",
    "**Next steps:**\n",
    "- Try [Seaborn](https://seaborn.pydata.org/) for statistical visualizations\n",
    "- Explore [Plotly](https://plotly.com/python/) for interactive charts\n",
    "- Check out our [Python Statistics](/notebooks/python-statistics/) tutorial"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}