003S_Matplotlib_&_Seaborn.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNK995YBdFTncqY8JrPEhxg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MohanVishe/Notes/blob/main/003S_Matplotlib_%26_Seaborn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Matplotlib**\n"
      ],
      "metadata": {
        "id": "RAWTzITgvhaS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Use Matplotlib for data analysis purposes:**\n",
        "\n",
        "1. **Import Matplotlib**:\n",
        "   To get started, you need to import Matplotlib. Typically, you would import the `pyplot` module, which provides an interface similar to MATLAB for creating plots and charts."
      ],
      "metadata": {
        "id": "fU7npWsUztDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#install package\n",
        "!pip install matplotlib\n",
        "# import\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "cJvlj02NzutY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Create Data**:\n",
        "   Before creating visualizations, you'll need some data to analyze. You can use NumPy or other data manipulation libraries to create or import your data.\n",
        "\n",
        "3. **Basic Plotting**:\n",
        "   Matplotlib provides various types of plots, including line plots, scatter plots, bar plots, and more. Here's how to create a simple line plot:"
      ],
      "metadata": {
        "id": "PriKYz9Bz_hT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = [1, 2, 3, 4, 5]\n",
        "y = [10, 15, 13, 18, 20]\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "4EEcvT7x0BN1",
        "outputId": "c6954443-8dac-4cda-e52d-963362a41b58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. **Customizing Plots**:\n",
        "   You can customize your plots using various functions. Some common customizations include:\n",
        "   - Adding labels to axes:\n",
        "     ```python\n",
        "     plt.xlabel('X-axis Label')\n",
        "     plt.ylabel('Y-axis Label')\n",
        "     ```\n",
        "   - Setting a title:\n",
        "     ```python\n",
        "     plt.title('My Plot Title')\n",
        "     ```\n",
        "   - Adding legends:\n",
        "     ```python\n",
        "     plt.legend(['Data Series 1'])\n",
        "     ```"
      ],
      "metadata": {
        "id": "R_9bXTiz0R_l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. **Subplots**:\n",
        "   You can create multiple plots within a single figure using subplots. This is useful for comparing different datasets or visualizing data side by side."
      ],
      "metadata": {
        "id": "dUQ4l5kl0Xyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(2,2,1)\n",
        "plt.plot(x,y,'r--')\n",
        "plt.subplot(2,2,2)\n",
        "plt.plot(y,x,'g--')\n",
        "plt.subplot(2,2,3)\n",
        "plt.plot(x,y,'b--')\n",
        "plt.subplot(2,2,4)\n",
        "plt.plot(x,y,'b*')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Ulg3R86K0ZW_",
        "outputId": "c46b8a2c-9c12-41e6-e071-69daf7f7cdae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. **Saving Figures**:\n",
        "   You can save your plots as image files for use in reports or presentations. Use `savefig` to do this:\n",
        "   ```python\n",
        "   plt.savefig('my_plot.png')\n",
        "   ```\n",
        "\n",
        "7. **Advanced Plot Types**:\n",
        "   Matplotlib supports a wide range of plot types, including histograms, bar charts, pie charts, and 3D plots. Explore the Matplotlib documentation for more options.\n",
        "\n",
        "8. **Data Visualization Best Practices**:\n",
        "   - Choose the appropriate plot type for your data.\n",
        "   - Label axes and provide meaningful titles.\n",
        "   - Use colors and markers to distinguish data points.\n",
        "   - Ensure your plots are easy to interpret and aesthetically pleasing.\n",
        "\n",
        "9. **Integration with Jupyter Notebooks**:\n",
        "   If you're using Jupyter Notebooks, you can display Matplotlib plots inline by using `%matplotlib inline` magic command.\n",
        "\n",
        "10. **Resources**:\n",
        "    Matplotlib has extensive documentation and numerous tutorials online. Explore these resources to enhance your data visualization skills.\n"
      ],
      "metadata": {
        "id": "yNXTdUhPzSV1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  **Creating Basic Plots**\n",
        "\n",
        "**1. Line Plot:**"
      ],
      "metadata": {
        "id": "2EdzVnNQ1Y3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,y,'ro',linestyle='dashed',linewidth=2,markersize=12)\n",
        "plt.xlabel('X axis')\n",
        "plt.ylabel('y axis')\n",
        "plt.title('2d diagram')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "u3TcV9Bs1b_0",
        "outputId": "a217427b-622b-4927-e2b0-6b958e4fc7b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Scatter Plot**\n"
      ],
      "metadata": {
        "id": "fUxVsfHr1f-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y,c=\"r\")\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"y axis\")\n",
        "plt.title(\"Scatter Plot\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "F5w7x6_v1kWn",
        "outputId": "f8124850-2c27-4dd1-e270-78a01399d145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Bar Chart**\n",
        "\n"
      ],
      "metadata": {
        "id": "KBDFZz171iof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x1=[2,3,4,5]\n",
        "y1=[11,16,19,20]\n",
        "\n",
        "x2=[3,9,11,14]\n",
        "y2=[6,15,7,9]\n",
        "\n",
        "\n",
        "plt.bar(x1,y1,color='g')\n",
        "plt.bar(x2,y2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "LJUB4i6e1p94",
        "outputId": "58f7af7b-0898-4bd2-dec4-e3f6aa419a2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Histogram**\n"
      ],
      "metadata": {
        "id": "3IaVA2dj1xqW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])\n",
        "plt.hist(a,bins=5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "P2o5lG0610BH",
        "outputId": "3e01dddf-c3e0-4de2-d650-1ef1c24c2a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhJElEQVR4nO3dfXBU1cHH8d/ytgHNroAmG8ICaWF4kZeEgLChY7RGUybjkNphKEMbaoEZbWiD6WiJVRx0fBZlqC+FEtAibTGNogIVUZqGJgwlKAHSElSUEUnUbNAp7EKUhcne548+rt2HJOSGwCHh+5m5M83dc+49O9earzc3WYdlWZYAAAAM6WF6AQAA4OpGjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMCoXqYX0B6RSESfffaZ4uPj5XA4TC8HAAC0g2VZOnXqlAYNGqQePVq//9ElYuSzzz6T1+s1vQwAANAB9fX1Gjx4cKuvd4kYiY+Pl/SfN+NyuQyvBgAAtEcoFJLX641+H29Nl4iRr38043K5iBEAALqYCz1iwQOsAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYdVExsmzZMjkcDi1atKjNcRs3btSoUaMUFxencePGadu2bRdzWgAA0I10OEb27t2rNWvWaPz48W2O2717t2bPnq158+bpwIEDys3NVW5urmprazt6agAA0I10KEZOnz6tOXPm6LnnnlP//v3bHPvMM8/oe9/7nu6//36NHj1ajz32mCZOnKiVK1d2aMEAAKB76VCM5OfnKycnR1lZWRccW1VVdd647OxsVVVVtTonHA4rFArFbAAAoHvqZXdCaWmp9u/fr71797ZrfCAQUGJiYsy+xMREBQKBVuf4/X4tXbrU7tI6ZNjiNy7Lea52Hy/LMb0EAMAVytadkfr6ehUUFOjFF19UXFzcpVqTioqKFAwGo1t9ff0lOxcAADDL1p2Rffv26fjx45o4cWJ0X3Nzs3bu3KmVK1cqHA6rZ8+eMXM8Ho8aGxtj9jU2Nsrj8bR6HqfTKafTaWdpAACgi7J1Z+S2227TwYMHVVNTE90mTZqkOXPmqKam5rwQkSSfz6fy8vKYfWVlZfL5fBe3cgAA0C3YujMSHx+vsWPHxuy75pprNHDgwOj+vLw8JScny+/3S5IKCgqUmZmpFStWKCcnR6WlpaqurtbatWs76S0AAICurNP/AmtdXZ0aGhqiX2dkZKikpERr167VhAkT9Morr2jz5s3nRQ0AALg6OSzLskwv4kJCoZDcbreCwaBcLlenHpvfprk8+G0aALj6tPf7N59NAwAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjLIVI6tXr9b48ePlcrnkcrnk8/n05ptvtjp+/fr1cjgcMVtcXNxFLxoAAHQfvewMHjx4sJYtW6YRI0bIsiz94Q9/0IwZM3TgwAHdeOONLc5xuVw6fPhw9GuHw3FxKwYAAN2KrRi58847Y75+/PHHtXr1au3Zs6fVGHE4HPJ4PB1fIQAA6NY6/MxIc3OzSktL1dTUJJ/P1+q406dPa+jQofJ6vZoxY4YOHTp0wWOHw2GFQqGYDQAAdE+2Y+TgwYO69tpr5XQ6dc8992jTpk0aM2ZMi2NHjhypdevWacuWLdqwYYMikYgyMjL0ySeftHkOv98vt9sd3bxer91lAgCALsJhWZZlZ8LZs2dVV1enYDCoV155Rc8//7wqKytbDZL/du7cOY0ePVqzZ8/WY4891uq4cDiscDgc/ToUCsnr9SoYDMrlctlZ7gUNW/xGpx4PLft4WY7pJQAALrNQKCS3233B79+2nhmRpD59+mj48OGSpPT0dO3du1fPPPOM1qxZc8G5vXv3Vlpamo4cOdLmOKfTKafTaXdpAACgC7rovzMSiURi7mK0pbm5WQcPHlRSUtLFnhYAAHQTtu6MFBUVafr06RoyZIhOnTqlkpISVVRUaPv27ZKkvLw8JScny+/3S5IeffRRTZ06VcOHD9fJkye1fPlyHTt2TPPnz+/8dwIAALokWzFy/Phx5eXlqaGhQW63W+PHj9f27dt1++23S5Lq6urUo8c3N1tOnDihBQsWKBAIqH///kpPT9fu3bvb9XwJAAC4Oth+gNWE9j4A0xE8wHp58AArAFx92vv9m8+mAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARtmKkdWrV2v8+PFyuVxyuVzy+Xx6880325yzceNGjRo1SnFxcRo3bpy2bdt2UQsGAADdi60YGTx4sJYtW6Z9+/apurpa3/3udzVjxgwdOnSoxfG7d+/W7NmzNW/ePB04cEC5ubnKzc1VbW1tpyweAAB0fQ7LsqyLOcCAAQO0fPlyzZs377zXZs2apaamJm3dujW6b+rUqUpNTVVxcXG7zxEKheR2uxUMBuVyuS5muecZtviNTj0eWvbxshzTSwAAXGbt/f7d4WdGmpubVVpaqqamJvl8vhbHVFVVKSsrK2Zfdna2qqqq2jx2OBxWKBSK2QAAQPfUy+6EgwcPyufz6cyZM7r22mu1adMmjRkzpsWxgUBAiYmJMfsSExMVCATaPIff79fSpUvtLg1XMO5Aobvhbt/lwb87Lg/T/zzbvjMycuRI1dTU6O2339a9996ruXPn6t133+3URRUVFSkYDEa3+vr6Tj0+AAC4cti+M9KnTx8NHz5ckpSenq69e/fqmWee0Zo1a84b6/F41NjYGLOvsbFRHo+nzXM4nU45nU67SwMAAF3QRf+dkUgkonA43OJrPp9P5eXlMfvKyspafcYEAABcfWzdGSkqKtL06dM1ZMgQnTp1SiUlJaqoqND27dslSXl5eUpOTpbf75ckFRQUKDMzUytWrFBOTo5KS0tVXV2ttWvXdv47AQAAXZKtGDl+/Ljy8vLU0NAgt9ut8ePHa/v27br99tslSXV1derR45ubLRkZGSopKdFDDz2kBx98UCNGjNDmzZs1duzYzn0XAACgy7IVI7///e/bfL2iouK8fTNnztTMmTNtLQoAAFw9+GwaAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABglK0Y8fv9mjx5suLj45WQkKDc3FwdPny4zTnr16+Xw+GI2eLi4i5q0QAAoPuwFSOVlZXKz8/Xnj17VFZWpnPnzumOO+5QU1NTm/NcLpcaGhqi27Fjxy5q0QAAoPvoZWfwW2+9FfP1+vXrlZCQoH379unmm29udZ7D4ZDH4+nYCgEAQLd2Uc+MBINBSdKAAQPaHHf69GkNHTpUXq9XM2bM0KFDh9ocHw6HFQqFYjYAANA9dThGIpGIFi1apGnTpmns2LGtjhs5cqTWrVunLVu2aMOGDYpEIsrIyNAnn3zS6hy/3y+32x3dvF5vR5cJAACucB2Okfz8fNXW1qq0tLTNcT6fT3l5eUpNTVVmZqZee+013XDDDVqzZk2rc4qKihQMBqNbfX19R5cJAACucLaeGfnawoULtXXrVu3cuVODBw+2Nbd3795KS0vTkSNHWh3jdDrldDo7sjQAANDF2LozYlmWFi5cqE2bNmnHjh1KSUmxfcLm5mYdPHhQSUlJtucCAIDux9adkfz8fJWUlGjLli2Kj49XIBCQJLndbvXt21eSlJeXp+TkZPn9fknSo48+qqlTp2r48OE6efKkli9frmPHjmn+/Pmd/FYAAEBXZCtGVq9eLUm65ZZbYva/8MIL+slPfiJJqqurU48e39xwOXHihBYsWKBAIKD+/fsrPT1du3fv1pgxYy5u5QAAoFuwFSOWZV1wTEVFRczXTz31lJ566ilbiwIAAFcPPpsGAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYZStG/H6/Jk+erPj4eCUkJCg3N1eHDx++4LyNGzdq1KhRiouL07hx47Rt27YOLxgAAHQvtmKksrJS+fn52rNnj8rKynTu3DndcccdampqanXO7t27NXv2bM2bN08HDhxQbm6ucnNzVVtbe9GLBwAAXZ/Dsiyro5M///xzJSQkqLKyUjfffHOLY2bNmqWmpiZt3bo1um/q1KlKTU1VcXFxu84TCoXkdrsVDAblcrk6utwWDVv8RqceD8DV4eNlOaaXcFXg39GXx6X657m9378v6pmRYDAoSRowYECrY6qqqpSVlRWzLzs7W1VVVa3OCYfDCoVCMRsAAOieenV0YiQS0aJFizRt2jSNHTu21XGBQECJiYkx+xITExUIBFqd4/f7tXTp0o4uDQAuOf6LHeg8Hb4zkp+fr9raWpWWlnbmeiRJRUVFCgaD0a2+vr7TzwEAAK4MHbozsnDhQm3dulU7d+7U4MGD2xzr8XjU2NgYs6+xsVEej6fVOU6nU06nsyNLAwAAXYytOyOWZWnhwoXatGmTduzYoZSUlAvO8fl8Ki8vj9lXVlYmn89nb6UAAKBbsnVnJD8/XyUlJdqyZYvi4+Ojz3243W717dtXkpSXl6fk5GT5/X5JUkFBgTIzM7VixQrl5OSotLRU1dXVWrt2bSe/FQAA0BXZujOyevVqBYNB3XLLLUpKSopuL730UnRMXV2dGhoaol9nZGSopKREa9eu1YQJE/TKK69o8+bNbT70CgAArh627oy050+SVFRUnLdv5syZmjlzpp1TAQCAqwSfTQMAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIyyHSM7d+7UnXfeqUGDBsnhcGjz5s1tjq+oqJDD4ThvCwQCHV0zAADoRmzHSFNTkyZMmKBVq1bZmnf48GE1NDREt4SEBLunBgAA3VAvuxOmT5+u6dOn2z5RQkKCrrvuOtvzAABA93bZnhlJTU1VUlKSbr/9dv3jH/9oc2w4HFYoFIrZAABA93TJYyQpKUnFxcV69dVX9eqrr8rr9eqWW27R/v37W53j9/vldrujm9frvdTLBAAAhjgsy7I6PNnh0KZNm5Sbm2trXmZmpoYMGaI//elPLb4eDocVDoejX4dCIXm9XgWDQblcro4ut0XDFr/RqccDAKCr+XhZziU5bigUktvtvuD3b9vPjHSGm266Sbt27Wr1dafTKafTeRlXBAAATDHyd0ZqamqUlJRk4tQAAOAKY/vOyOnTp3XkyJHo10ePHlVNTY0GDBigIUOGqKioSJ9++qn++Mc/SpKefvpppaSk6MYbb9SZM2f0/PPPa8eOHfrrX//aee8CAAB0WbZjpLq6Wrfeemv068LCQknS3LlztX79ejU0NKiuri76+tmzZ/XLX/5Sn376qfr166fx48frb3/7W8wxAADA1euiHmC9XNr7AExH8AArAOBqZ/oBVj6bBgAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGGU7Rnbu3Kk777xTgwYNksPh0ObNmy84p6KiQhMnTpTT6dTw4cO1fv36DiwVAAB0R7ZjpKmpSRMmTNCqVavaNf7o0aPKycnRrbfeqpqaGi1atEjz58/X9u3bbS8WAAB0P73sTpg+fbqmT5/e7vHFxcVKSUnRihUrJEmjR4/Wrl279NRTTyk7O9vu6QEAQDdzyZ8ZqaqqUlZWVsy+7OxsVVVVtTonHA4rFArFbAAAoHu65DESCASUmJgYsy8xMVGhUEhfffVVi3P8fr/cbnd083q9l3qZAADAkCvyt2mKiooUDAajW319veklAQCAS8T2MyN2eTweNTY2xuxrbGyUy+VS3759W5zjdDrldDov9dIAAMAV4JLfGfH5fCovL4/ZV1ZWJp/Pd6lPDQAAugDbMXL69GnV1NSopqZG0n9+dbempkZ1dXWS/vMjlry8vOj4e+65Rx999JEeeOABvf/++/rd736nl19+Wffdd1/nvAMAANCl2Y6R6upqpaWlKS0tTZJUWFiotLQ0LVmyRJLU0NAQDRNJSklJ0RtvvKGysjJNmDBBK1as0PPPP8+v9QIAAEmSw7Isy/QiLiQUCsntdisYDMrlcnXqsYctfqNTjwcAQFfz8bKcS3Lc9n7/viJ/mwYAAFw9iBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjOhQjq1at0rBhwxQXF6cpU6bonXfeaXXs+vXr5XA4Yra4uLgOLxgAAHQvtmPkpZdeUmFhoR555BHt379fEyZMUHZ2to4fP97qHJfLpYaGhuh27Nixi1o0AADoPmzHyG9+8xstWLBAd999t8aMGaPi4mL169dP69ata3WOw+GQx+OJbomJiRe1aAAA0H3YipGzZ89q3759ysrK+uYAPXooKytLVVVVrc47ffq0hg4dKq/XqxkzZujQoUNtniccDisUCsVsAACge7IVI1988YWam5vPu7ORmJioQCDQ4pyRI0dq3bp12rJlizZs2KBIJKKMjAx98sknrZ7H7/fL7XZHN6/Xa2eZAACgC7nkv03j8/mUl5en1NRUZWZm6rXXXtMNN9ygNWvWtDqnqKhIwWAwutXX11/qZQIAAEN62Rl8/fXXq2fPnmpsbIzZ39jYKI/H065j9O7dW2lpaTpy5EirY5xOp5xOp52lAQCALsrWnZE+ffooPT1d5eXl0X2RSETl5eXy+XztOkZzc7MOHjyopKQkeysFAADdkq07I5JUWFiouXPnatKkSbrpppv09NNPq6mpSXfffbckKS8vT8nJyfL7/ZKkRx99VFOnTtXw4cN18uRJLV++XMeOHdP8+fM7950AAIAuyXaMzJo1S59//rmWLFmiQCCg1NRUvfXWW9GHWuvq6tSjxzc3XE6cOKEFCxYoEAiof//+Sk9P1+7duzVmzJjOexcAAKDLcliWZZlexIWEQiG53W4Fg0G5XK5OPfawxW906vEAAOhqPl6Wc0mO297v33w2DQAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMKpDMbJq1SoNGzZMcXFxmjJlit555502x2/cuFGjRo1SXFycxo0bp23btnVosQAAoPuxHSMvvfSSCgsL9cgjj2j//v2aMGGCsrOzdfz48RbH7969W7Nnz9a8efN04MAB5ebmKjc3V7W1tRe9eAAA0PU5LMuy7EyYMmWKJk+erJUrV0qSIpGIvF6vfv7zn2vx4sXnjZ81a5aampq0devW6L6pU6cqNTVVxcXF7TpnKBSS2+1WMBiUy+Wys9wLGrb4jU49HgAAXc3Hy3IuyXHb+/27l52Dnj17Vvv27VNRUVF0X48ePZSVlaWqqqoW51RVVamwsDBmX3Z2tjZv3tzqecLhsMLhcPTrYDAo6T9vqrNFwl92+jEBAOhKLsX31/8+7oXue9iKkS+++ELNzc1KTEyM2Z+YmKj333+/xTmBQKDF8YFAoNXz+P1+LV269Lz9Xq/XznIBAEA7uJ++tMc/deqU3G53q6/bipHLpaioKOZuSiQS0b///W8NHDhQDofD4MrQmlAoJK/Xq/r6+k7/URouHa5b18R165quxutmWZZOnTqlQYMGtTnOVoxcf/316tmzpxobG2P2NzY2yuPxtDjH4/HYGi9JTqdTTqczZt91111nZ6kwxOVyXTX/J+tOuG5dE9eta7rarltbd0S+Zuu3afr06aP09HSVl5dH90UiEZWXl8vn87U4x+fzxYyXpLKyslbHAwCAq4vtH9MUFhZq7ty5mjRpkm666SY9/fTTampq0t133y1JysvLU3Jysvx+vySpoKBAmZmZWrFihXJyclRaWqrq6mqtXbu2c98JAADokmzHyKxZs/T5559ryZIlCgQCSk1N1VtvvRV9SLWurk49enxzwyUjI0MlJSV66KGH9OCDD2rEiBHavHmzxo4d23nvAsY5nU498sgj5/14DVc2rlvXxHXrmrhurbP9d0YAAAA6E59NAwAAjCJGAACAUcQIAAAwihgBAABGESNoN7/fr8mTJys+Pl4JCQnKzc3V4cOHY8acOXNG+fn5GjhwoK699lr94Ac/OO+P3sGsZcuWyeFwaNGiRdF9XLcr06effqof/ehHGjhwoPr27atx48apuro6+rplWVqyZImSkpLUt29fZWVl6cMPPzS4YjQ3N+vhhx9WSkqK+vbtq29/+9t67LHHYj6bhet2PmIE7VZZWan8/Hzt2bNHZWVlOnfunO644w41NTVFx9x33316/fXXtXHjRlVWVuqzzz7TXXfdZXDV+G979+7VmjVrNH78+Jj9XLcrz4kTJzRt2jT17t1bb775pt59912tWLFC/fv3j4558skn9eyzz6q4uFhvv/22rrnmGmVnZ+vMmTMGV351e+KJJ7R69WqtXLlS7733np544gk9+eST+u1vfxsdw3VrgQV00PHjxy1JVmVlpWVZlnXy5Emrd+/e1saNG6Nj3nvvPUuSVVVVZWqZ+D+nTp2yRowYYZWVlVmZmZlWQUGBZVlctyvVr371K+s73/lOq69HIhHL4/FYy5cvj+47efKk5XQ6rT//+c+XY4loQU5OjvXTn/40Zt9dd91lzZkzx7IsrltruDOCDgsGg5KkAQMGSJL27dunc+fOKSsrKzpm1KhRGjJkiKqqqoysEd/Iz89XTk5OzPWRuG5Xqr/85S+aNGmSZs6cqYSEBKWlpem5556Lvn706FEFAoGY6+Z2uzVlyhSum0EZGRkqLy/XBx98IEn65z//qV27dmn69OmSuG6tuSI/tRdXvkgkokWLFmnatGnRv6YbCATUp0+f8z7UMDExUYFAwMAq8bXS0lLt379fe/fuPe81rtuV6aOPPtLq1atVWFioBx98UHv37tUvfvEL9enTR3Pnzo1em6//+vXXuG5mLV68WKFQSKNGjVLPnj3V3Nysxx9/XHPmzJEkrlsriBF0SH5+vmpra7Vr1y7TS8EF1NfXq6CgQGVlZYqLizO9HLRTJBLRpEmT9D//8z+SpLS0NNXW1qq4uFhz5841vDq05uWXX9aLL76okpIS3XjjjaqpqdGiRYs0aNAgrlsb+DENbFu4cKG2bt2qv//97xo8eHB0v8fj0dmzZ3Xy5MmY8Y2NjfJ4PJd5lfjavn37dPz4cU2cOFG9evVSr169VFlZqWeffVa9evVSYmIi1+0KlJSUpDFjxsTsGz16tOrq6iQpem3+/289cd3Muv/++7V48WL98Ic/1Lhx4/TjH/9Y9913X/TDY7luLSNG0G6WZWnhwoXatGmTduzYoZSUlJjX09PT1bt3b5WXl0f3HT58WHV1dfL5fJd7ufg/t912mw4ePKiamproNmnSJM2ZMyf6v7luV55p06ad96vzH3zwgYYOHSpJSklJkcfjibluoVBIb7/9NtfNoC+//DLmw2IlqWfPnopEIpK4bq0y/QQtuo57773XcrvdVkVFhdXQ0BDdvvzyy+iYe+65xxoyZIi1Y8cOq7q62vL5fJbP5zO4arTkv3+bxrK4bleid955x+rVq5f1+OOPWx9++KH14osvWv369bM2bNgQHbNs2TLruuuus7Zs2WL961//smbMmGGlpKRYX331lcGVX93mzp1rJScnW1u3brWOHj1qvfbaa9b1119vPfDAA9ExXLfzESNoN0ktbi+88EJ0zFdffWX97Gc/s/r372/169fP+v73v281NDSYWzRa9P9jhOt2ZXr99detsWPHWk6n0xo1apS1du3amNcjkYj18MMPW4mJiZbT6bRuu+026/Dhw4ZWC8uyrFAoZBUUFFhDhgyx4uLirG9961vWr3/9ayscDkfHcN3O57Cs//qzcAAAAJcZz4wAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFH/CyNKm1Se5VbtAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Boxplot**"
      ],
      "metadata": {
        "id": "Hud8SVlH4I1I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
        "plt.boxplot(data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ja6nVS584Cpx",
        "outputId": "ba323bfa-7d96-4c9c-97b7-5921ed49d7e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. Pie Chart**"
      ],
      "metadata": {
        "id": "LG2H9CIR4bUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data to plot\n",
        "labels = 'Python', 'C++', 'Ruby', 'Java'\n",
        "sizes = [215, 130, 245, 210]\n",
        "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']\n",
        "explode = (0.4, 0, 0, 0)  # explode 1st slice\n",
        "\n",
        "# Plot\n",
        "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
        "autopct='%1.1f%%', shadow=False)\n",
        "\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "IkLbeiYt4hk4",
        "outputId": "79183172-fd9c-4608-86e6-50ea827ac42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **for more (https://matplotlib.org/)**"
      ],
      "metadata": {
        "id": "vTPJ5JWv-gsZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Seaborn**\n",
        "\n",
        "### **Introduction to Seaborn**\n",
        "- Seaborn is a powerful Python data visualization library built on top of Matplotlib.\n",
        "- It provides a high-level interface for creating informative and attractive statistical graphics."
      ],
      "metadata": {
        "id": "6s9bUY699Ejp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Importing Seaborn**\n",
        "- To use Seaborn, import it alongside Matplotlib:"
      ],
      "metadata": {
        "id": "3V5R32wD9IBV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#installation\n",
        "!pip install seaborn\n",
        "# import\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GuKQ0Cgr9JaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**2. Loading Data**\n",
        "- Seaborn works seamlessly with Pandas DataFrames.\n",
        "- Use `sns.load_dataset()` or `pd.read_csv()` to load your data.\n",
        "\n",
        "**3. Setting Aesthetic Parameters**\n",
        "- Customize the appearance of your plots using Seaborn's aesthetic parameters.\n",
        "- Use `sns.set_style()` to set a style: 'whitegrid', 'darkgrid', 'ticks', etc.\n",
        "- Adjust the context with `sns.set_context()`: 'notebook', 'paper', 'talk', or 'poster'.\n",
        "- Set color palettes with `sns.set_palette()`.\n",
        "  \n",
        "**4. Creating Basic Plots**\n",
        "- Seaborn makes it easy to create a variety of basic plots:\n",
        "  - Scatter plots: `sns.scatterplot()`\n",
        "  - Line plots: `sns.lineplot()`\n",
        "  - Bar plots: `sns.barplot()`\n",
        "  - Histograms: `sns.histplot()`\n",
        "  - Box plots: `sns.boxplot()`\n",
        "  - Violin plots: `sns.violinplot()`\n",
        "\n",
        "**5. Customizing Plots**\n",
        "- Customize your plots using Seaborn's functions and arguments.\n",
        "- Add titles and labels using `plt.title()`, `plt.xlabel()`, and `plt.ylabel()`.\n",
        "- Modify colors, styles, and markers as needed.\n",
        "- Use `plt.legend()` to add legends to your plots.\n",
        "\n",
        "**6. Advanced Visualization**\n",
        "- Seaborn excels at advanced visualizations:\n",
        "  - Heatmaps: `sns.heatmap()`\n",
        "  - Pair plots: `sns.pairplot()`\n",
        "  - Facet grids: `sns.FacetGrid()`\n",
        "  - Joint plots: `sns.jointplot()`\n",
        "  \n",
        "**7.  Working with Categorical Data**\n",
        "- Seaborn offers tools for categorical data visualization:\n",
        "  - Count plots: `sns.countplot()`\n",
        "  - Categorical scatter plots: `sns.stripplot()`, `sns.swarmplot()`\n",
        "  - Categorical box plots: `sns.boxplot()`\n",
        "  \n",
        "**8. Data Exploration**\n",
        "- Use Seaborn's functions like `sns.distplot()`, `sns.pairplot()`, and `sns.heatmap()` to explore data distributions and relationships.\n",
        "\n",
        "**9. Saving Plots**\n",
        "- Save your Seaborn plots using `plt.savefig(\"filename.png\")`.\n",
        "\n",
        "#### **For more : (https://seaborn.pydata.org/)**\n",
        "\n"
      ],
      "metadata": {
        "id": "pO0lcPhn8kEe"
      }
    }
  ]
}