magic_commands.ipynb

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    "Q1. WHAT ARE MAGIC COMMANDS?\n",
    "Magic commands are special commands in Jupyter Notebooks (and IPython) that provide shortcuts for common tasks and enhance the functionality of the notebook environment. They are prefixed with % (for line magics) or %% (for cell magics) and are designed to make your workflow more efficient.\n",
    "\n",
    "**Types of Magic Commands.**\n",
    "1. Line Magics (%):Operate on a single line of input.Example: %timeit, %matplotlib inline.\n",
    "2. Cell Magics (%%):Operate on the entire cell.Example: %%time, %%writefile.\n",
    "\n",
    "**Common Magic Commands and Their Uses**\n",
    "1. %run: Run a Python script from within the notebook.\n",
    "2. !: Execute shell commands directly from the notebook.\n",
    "\n",
    "**Timing and Profiling**\n",
    "1. %time: Measure the execution time of a single statement.\n",
    "2. %timeit: Measure the average execution time of a statement over multiple runs.\n",
    "3. %%time: Measure the execution time of an entire cell.\n",
    "4. %prun: Run a code block with the Python profiler.\n",
    "\n",
    "**Debugging**\n",
    "1. %debug: Enter the interactive debugger after an exception.\n",
    "2. %pdb: Automatically enter the debugger on exception.\n",
    "\n",
    "**Visualization**\n",
    "1. %matplotlib inline: Display Matplotlib plots inline in the notebook.\n",
    "2. %matplotlib notebook: Enable interactive Matplotlib plots in the notebook.\n",
    "\n",
    "**File Operations**\n",
    "1. %%writefile: Write the contents of a cell to a file.\n",
    "2. %load: Load code from a file into a cell.\n",
    "\n",
    "**Environment and Configuration**\n",
    "1. %env: Get or set environment variables.\n",
    "2. %who: List all variables in the current namespace.\n",
    "3. %whos: List all variables with detailed information.\n",
    "4. %reset: Delete all variables from the current namespace.\n",
    "\n",
    "**Extensions and Plugins**\n",
    "1. %load_ext: Load an IPython extension.\n",
    "2. %autoreload: Automatically reload modules before executing code.\n",
    "\n",
    "**Documentation and Help**\n",
    "1. %pdoc: Display the docstring of an object.\n",
    "2. %pinfo: Provide detailed information about an object.\n",
    "3. %pinfo2: Provide even more detailed information about an object.\n",
    "\n",
    "**Miscellaneous**\n",
    "1. %history: Display the command history.\n",
    "2. %paste: Paste and execute code from the clipboard.\n",
    "3. %store: Store variables between sessions.\n",
    "\n",
    ">>To see a list of all available magic commands, use: %lsmagic\n",
    "\n",
    "**KEY TAKEAWAYS**\n",
    "1. Magic commands are specific to Jupyter Notebooks and IPython.\n",
    "2. They provide shortcuts for common tasks like timing, debugging, file operations, and more.\n",
    "3. Use % for line magics and %% for cell magics.\n",
    "4. Magic commands can significantly improve your productivity in a notebook environment.\n",
    "\n",
    "Q2. DIFFERENCE BETWEEN A BAR CHART, COUNTPLOT AND A HISTOGRAM.\n",
    "1. Bar Chart 📊\n",
    "🔹 Purpose: Used to compare categories of data.\n",
    "🔹 Data Type: Categorical (e.g., product names, city names, job titles).\n",
    "🔹 X-axis: Categories (e.g., \"Apple\", \"Banana\", \"Cherry\").\n",
    "🔹 Y-axis: Count or another numerical value (e.g., sales, frequency).\n",
    "🔹 Library: Matplotlib and Seaborn.\n",
    "**EXAMPLE**\n",
    "import matplotlib.pyplot as plt\n",
    "categories = ['A', 'B', 'C']\n",
    "values = [10, 25, 15]\n",
    "plt.bar(categories, values)\n",
    "plt.xlabel(\"Categories\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.title(\"Bar Chart Example\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "2. Count Plot 🔢\n",
    "🔹 Purpose: A special type of bar chart that automatically counts occurrences of categories in a dataset.\n",
    "🔹 Data Type: Categorical (like a bar chart).\n",
    "🔹 X-axis: Unique categories.\n",
    "🔹 Y-axis: Count of occurrences in the dataset.\n",
    "🔹 Library: Seaborn.\n",
    "**EXAMPLE**\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "# Sample Data\n",
    "df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'C', 'B', 'A']})\n",
    "sns.countplot(x='Category', data=df)\n",
    "plt.title(\"Count Plot Example\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "3. Histogram 📈\n",
    "🔹 Purpose: Used to show the distribution of a continuous variable by dividing it into bins.\n",
    "🔹 Data Type: Numerical (e.g., age, salary, weight).\n",
    "🔹 X-axis: Ranges (bins) of a continuous variable.\n",
    "🔹 Y-axis: Frequency (how many values fall into each bin).\n",
    "🔹 Library: Matplotlib and Seaborn.\n",
    "**EXAMPLE**\n",
    "import numpy as np\n",
    "data = np.random.randn(1000)  # Generate 1000 random numbers\n",
    "plt.hist(data, bins=30, edgecolor='black')\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram Example\")\n",
    "plt.show()\n",
    "\n",
    "Q3. Learn the different customisation for each visualization.\n",
    "1. Bar Chart Customizations 📊\n",
    "🔹 Customizations:\n",
    "✔️ Change colors & edge colors\n",
    "✔️ Rotate x-axis labels\n",
    "✔️ Add annotations (data labels)\n",
    "✔️ Adjust bar width & transparency\n",
    "**EXAMPLE**\n",
    "import matplotlib.pyplot as plt\n",
    "categories = ['A', 'B', 'C']\n",
    "values = [10, 25, 15]\n",
    "plt.bar(categories, values, color=['blue', 'green', 'red'], edgecolor='black', alpha=0.7, width=0.5)\n",
    "plt.xlabel(\"Categories\", fontsize=12, fontweight='bold')\n",
    "plt.ylabel(\"Values\", fontsize=12)\n",
    "plt.title(\"Customized Bar Chart\", fontsize=14, fontweight='bold')\n",
    "# Rotate labels\n",
    "plt.xticks(rotation=45)\n",
    "# Add labels on bars\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 1, str(v), ha='center', fontsize=10, fontweight='bold')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "2. Count Plot Customizations 🔢\n",
    "🔹 Customizations:\n",
    "✔️ Change bar colors & add palette\n",
    "✔️ Change bar width (dodge=False for single bars)\n",
    "✔️ Add titles & axis labels\n",
    "✔️ Style with Seaborn themes\n",
    "**EXAMPLE**\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# Sample Data\n",
    "df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'C', 'B', 'A']})\n",
    "# Set Seaborn theme\n",
    "sns.set_style(\"darkgrid\")\n",
    "# Create count plot\n",
    "ax = sns.countplot(x='Category', data=df, palette=\"coolwarm\", edgecolor='black')\n",
    "# Add title and labels\n",
    "ax.set_title(\"Customized Count Plot\", fontsize=14, fontweight='bold')\n",
    "ax.set_xlabel(\"Category\", fontsize=12)\n",
    "ax.set_ylabel(\"Count\", fontsize=12)\n",
    "# Add labels on bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2, p.get_height() + 0.1), \n",
    "                ha='center', fontsize=10, fontweight='bold')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "3. Histogram Customizations 📈\n",
    "🔹 Customizations:\n",
    "✔️ Change bin size\n",
    "✔️ Add grid & transparency\n",
    "✔️ Change bar colors\n",
    "✔️ Overlay KDE (density plot)\n",
    "**EXAMPLE**\n",
    "import numpy as np\n",
    "data = np.random.randn(1000)  # Generate 1000 random values\n",
    "plt.hist(data, bins=25, color='purple', edgecolor='black', alpha=0.7, density=True)\n",
    "plt.xlabel(\"Value\", fontsize=12)\n",
    "plt.ylabel(\"Density\", fontsize=12)\n",
    "plt.title(\"Customized Histogram\", fontsize=14, fontweight='bold')\n",
    "# Add grid for better readability\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "# Overlay Kernel Density Estimate (KDE) for smooth distribution\n",
    "import seaborn as sns\n",
    "sns.kdeplot(data, color='red', linewidth=2)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
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