data_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "data_analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNQxS/yYEk7YafztLEPxd5N",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Harshithr3/Webscraper/blob/master/data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imEwT8DYnFAR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "3416023a-9c93-46c0-9c47-22c2e058d523"
      },
      "source": [
        "!pip install numpy"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzPaDDzXpn3b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSTMcRLBqTR0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = np.array([1,2,3.5,'p'])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPeajZYvqkK3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "4124caf3-254d-4192-a32f-4c57c86bf2d1"
      },
      "source": [
        "a"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '2', '3.5', 'p'], dtype='<U32')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lktxj_Gfq0dr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "67ef1f57-8e6a-40ae-bf31-92736a8d9182"
      },
      "source": [
        "a[2]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'3.5'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kvy9sdjOq5U_",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "dfb3ba65-c006-42af-ed71-01b3f17ed16b"
      },
      "source": [
        "a[1:4]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2', '3.5', 'p'], dtype='<U32')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNoNR2_Lq8fw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5360a867-e4a3-400e-9385-e82862ab8a35"
      },
      "source": [
        "a[:]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '2', '3.5', 'p'], dtype='<U32')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieQBYnYrrBqM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = np.array([1,2,3]) #single dimensional array"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AokP_VpkrOrr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = np.array([[1,2,3],[4,5,6]])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qHp-SgQrqha",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c42cff03-101a-43fd-b518-8bd96b4fb051"
      },
      "source": [
        "print(b)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4i29e5faruLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "52e4ecce-e2a9-4acc-809e-b808fc55019e"
      },
      "source": [
        "print(b[0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Z7o0n41r3vC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f01d850e-bec5-4ea2-c0dd-46150ae9d71e"
      },
      "source": [
        "b = np.array([[2,3],[4,5,6]])\n",
        "b"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([list([2, 3]), list([4, 5, 6])], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsVuYapisOr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "5c9e1dca-0470-455a-ad89-c7351e0d7005"
      },
      "source": [
        "for i in range(10):\n",
        "  print(i)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPIwXuL4swfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "563c38fe-4cf1-47b7-deff-0cd54a99c295"
      },
      "source": [
        "a=[1,2,3,4,5,6,7,8,9]\n",
        "a[::-1]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 8, 7, 6, 5, 4, 3, 2, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCxeo-jGs5Jn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "547e82c5-0ac2-48aa-857e-9de4041cc9df"
      },
      "source": [
        "d=np.array(10)\n",
        "d"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FenxRD8tCtb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e7ab3650-07e1-47c8-bbea-5419a37d3615"
      },
      "source": [
        "d=np.arange(10)\n",
        "d"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5l9TNdDtRIz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "45857188-41c2-44d4-b61b-11880500a1e9"
      },
      "source": [
        "import random \n",
        "from random import randint\n",
        "for i in range(15):\n",
        "  print(randint(2,10),end=' ')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4 2 6 8 2 10 2 5 3 10 2 10 8 3 3 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEijgJ7Ttwdt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "8931768d-53b0-4fed-cad4-ffbc60bda626"
      },
      "source": [
        "data=np.random.rand(2,3)\n",
        "data"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.90466211, 0.82809537, 0.93183517],\n",
              "       [0.00232958, 0.79804584, 0.07610428]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "it6eDUGJt4tY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "8ae465e8-1024-48ce-cd4a-57c2ff5de16a"
      },
      "source": [
        "data+data"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.80932423, 1.65619074, 1.86367034],\n",
              "       [0.00465916, 1.59609169, 0.15220856]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4krqnWJuVSE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6e2d34eb-028a-4680-d77b-e3c3f66c0f1f"
      },
      "source": [
        "data-data\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0.],\n",
              "       [0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omydbKlKucos",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "c088bb33-4f3a-4931-af5f-6d29e52b13b7"
      },
      "source": [
        "arr = np.arange(16).reshape(2,2,4)\n",
        "arr"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1,  2,  3],\n",
              "        [ 4,  5,  6,  7]],\n",
              "\n",
              "       [[ 8,  9, 10, 11],\n",
              "        [12, 13, 14, 15]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXYrHnRbvLAl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "1639daf9-e44d-46e1-c712-e047cb8aefb4"
      },
      "source": [
        "arr.ndim"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9FczR7Yv87U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "outputId": "461f31f2-31ce-4aa0-c9e9-dd81d34584be"
      },
      "source": [
        "x=np.array([[[ 0,  1,  2,  3],\n",
        "        [ 4,  5,  6,  7]],\n",
        "\n",
        "       [[ 8,  9, 10, 11],\n",
        "        [12, 13, 14, 15]],\n",
        "        [[ 16,  17, 18, 19],\n",
        "        [20, 21, 22, 23]]])\n",
        "x"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1,  2,  3],\n",
              "        [ 4,  5,  6,  7]],\n",
              "\n",
              "       [[ 8,  9, 10, 11],\n",
              "        [12, 13, 14, 15]],\n",
              "\n",
              "       [[16, 17, 18, 19],\n",
              "        [20, 21, 22, 23]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifPMizb4xEb1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5a207c06-3bf8-43b1-d6e7-0c2dd501c067"
      },
      "source": [
        "import pandas as pd\n",
        "data=np.array(['h','a','r','s','h','i','t','h'])\n",
        "data"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['h', 'a', 'r', 's', 'h', 'i', 't', 'h'], dtype='<U1')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1J1h78fyiMh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "3beee257-b2b5-4bcc-cf0c-0e308a0909c6"
      },
      "source": [
        "a=pd.DataFrame([1,2,3])\n",
        "a"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "-vVKNDnRzsxb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
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      "source": [
        "data=[['sai',10],['harshith',20],['yogesh',30]]\n",
        "d= pd.DataFrame(data,columns=('Name',\"age\"),index=[4,2,6],dtype=float)\n",
        "d"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>4</th>\n",
              "      <td>sai</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>harshith</td>\n",
              "      <td>20.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>yogesh</td>\n",
              "      <td>30.0</td>\n",
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            ],
            "text/plain": [
              "       Name   age\n",
              "4       sai  10.0\n",
              "2  harshith  20.0\n",
              "6    yogesh  30.0"
            ]
          },
          "metadata": {
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          "execution_count": 40
        }
      ]
    },
    {
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          "height": 264
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        "outputId": "7dd759f0-b112-4f41-8962-85ffdfb2ead4"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "data = [1,2,3,4,5,6]\n",
        "plt.plot(data,'.')\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-Gg28R82wsk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "matplotlib.rcParams['axes.labelsize'] = 14\n",
        "matplotlib.rcParams['xtick.labelsize']= 12\n",
        "matplotlib.rcParams['ytick.labelsize'] = 12\n",
        "matplotlib.rcParams['text.color']='k' \n",
        "df = pd.read_excel(\"/content/Projectsample/Superstore.xls\")"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MbOGTE377xf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b3b71d30-d522-4136-f1ea-0cbddd926822"
      },
      "source": [
        "furniture = df.loc[df['Category'] == 'Furniture']\n",
        "furniture\n"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>2016-11-08</td>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-BO-10001798</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Bush Somerset Collection Bookcase</td>\n",
              "      <td>261.9600</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>41.9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>2016-11-08</td>\n",
              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.9400</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "      <td>219.5820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>US-2015-108966</td>\n",
              "      <td>2015-10-11</td>\n",
              "      <td>2015-10-18</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>Florida</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>957.5775</td>\n",
              "      <td>5</td>\n",
              "      <td>0.45</td>\n",
              "      <td>-383.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>CA-2014-115812</td>\n",
              "      <td>2014-06-09</td>\n",
              "      <td>2014-06-14</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BH-11710</td>\n",
              "      <td>Brosina Hoffman</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-FU-10001487</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Eldon Expressions Wood and Plastic Desk Access...</td>\n",
              "      <td>48.8600</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>14.1694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>CA-2014-115812</td>\n",
              "      <td>2014-06-09</td>\n",
              "      <td>2014-06-14</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BH-11710</td>\n",
              "      <td>Brosina Hoffman</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-TA-10001539</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Chromcraft Rectangular Conference Tables</td>\n",
              "      <td>1706.1840</td>\n",
              "      <td>9</td>\n",
              "      <td>0.20</td>\n",
              "      <td>85.3092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9962</th>\n",
              "      <td>9963</td>\n",
              "      <td>CA-2015-168088</td>\n",
              "      <td>2015-03-19</td>\n",
              "      <td>2015-03-22</td>\n",
              "      <td>First Class</td>\n",
              "      <td>CM-12655</td>\n",
              "      <td>Corinna Mitchell</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77041</td>\n",
              "      <td>Central</td>\n",
              "      <td>FUR-BO-10004218</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Bush Heritage Pine Collection 5-Shelf Bookcase...</td>\n",
              "      <td>383.4656</td>\n",
              "      <td>4</td>\n",
              "      <td>0.32</td>\n",
              "      <td>-67.6704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9964</th>\n",
              "      <td>9965</td>\n",
              "      <td>CA-2016-146374</td>\n",
              "      <td>2016-12-05</td>\n",
              "      <td>2016-12-10</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>HE-14800</td>\n",
              "      <td>Harold Engle</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Newark</td>\n",
              "      <td>Delaware</td>\n",
              "      <td>19711</td>\n",
              "      <td>East</td>\n",
              "      <td>FUR-FU-10002671</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Electrix 20W Halogen Replacement Bulb for Zoom...</td>\n",
              "      <td>13.4000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.4320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9980</th>\n",
              "      <td>9981</td>\n",
              "      <td>US-2015-151435</td>\n",
              "      <td>2015-09-06</td>\n",
              "      <td>2015-09-09</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>SW-20455</td>\n",
              "      <td>Shaun Weien</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Lafayette</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>70506</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10001039</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>KI Adjustable-Height Table</td>\n",
              "      <td>85.9800</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>22.3548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9989</th>\n",
              "      <td>9990</td>\n",
              "      <td>CA-2014-110422</td>\n",
              "      <td>2014-01-21</td>\n",
              "      <td>2014-01-23</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>TB-21400</td>\n",
              "      <td>Tom Boeckenhauer</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>33180</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-FU-10001889</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Ultra Door Pull Handle</td>\n",
              "      <td>25.2480</td>\n",
              "      <td>3</td>\n",
              "      <td>0.20</td>\n",
              "      <td>4.1028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9990</th>\n",
              "      <td>9991</td>\n",
              "      <td>CA-2017-121258</td>\n",
              "      <td>2017-02-26</td>\n",
              "      <td>2017-03-03</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>DB-13060</td>\n",
              "      <td>Dave Brooks</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Costa Mesa</td>\n",
              "      <td>California</td>\n",
              "      <td>92627</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-FU-10000747</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Tenex B1-RE Series Chair Mats for Low Pile Car...</td>\n",
              "      <td>91.9600</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15.6332</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2121 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Row ID        Order ID Order Date  ... Quantity Discount    Profit\n",
              "0          1  CA-2016-152156 2016-11-08  ...        2     0.00   41.9136\n",
              "1          2  CA-2016-152156 2016-11-08  ...        3     0.00  219.5820\n",
              "3          4  US-2015-108966 2015-10-11  ...        5     0.45 -383.0310\n",
              "5          6  CA-2014-115812 2014-06-09  ...        7     0.00   14.1694\n",
              "10        11  CA-2014-115812 2014-06-09  ...        9     0.20   85.3092\n",
              "...      ...             ...        ...  ...      ...      ...       ...\n",
              "9962    9963  CA-2015-168088 2015-03-19  ...        4     0.32  -67.6704\n",
              "9964    9965  CA-2016-146374 2016-12-05  ...        1     0.00    6.4320\n",
              "9980    9981  US-2015-151435 2015-09-06  ...        1     0.00   22.3548\n",
              "9989    9990  CA-2014-110422 2014-01-21  ...        3     0.20    4.1028\n",
              "9990    9991  CA-2017-121258 2017-02-26  ...        2     0.00   15.6332\n",
              "\n",
              "[2121 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kDF40W19Wak",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC2N0-0d87Du",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "08bda56c-8e50-4f66-c963-6951bebbd4f9"
      },
      "source": [
        "cols = ['Row ID', 'Order ID', 'Ship Date', 'Ship Mode','Customer ID','Customer Name','Segment','Country','City','State','Postal Code','Region','Product ID','Category','Sub-Category','Product Name','Quantity','Discount','Profit']\n",
        "furniture.drop(cols, axis=1,inplace=True)\n",
        "furniture=furniture.sort_values('Order Date')\n",
        "furniture.isnull().sum()\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order Date    0\n",
              "Sales         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5lgou_d9Jk6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "ed7627b5-e535-40e8-f70f-67298623a43a"
      },
      "source": [
        "furniture = furniture.groupby('Order Date')['Sales'].sum().reset_index()\n",
        "furniture"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order Date</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-06</td>\n",
              "      <td>2573.8200</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>76.7280</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-10</td>\n",
              "      <td>51.9400</td>\n",
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              "      <td>2014-01-11</td>\n",
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              "      <th>4</th>\n",
              "      <td>2014-01-13</td>\n",
              "      <td>879.9390</td>\n",
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              "      <th>884</th>\n",
              "      <td>2017-12-24</td>\n",
              "      <td>1393.4940</td>\n",
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              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>2017-12-25</td>\n",
              "      <td>832.4540</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>2017-12-28</td>\n",
              "      <td>551.2568</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>2017-12-29</td>\n",
              "      <td>2330.7180</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>2017-12-30</td>\n",
              "      <td>323.1360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>889 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Order Date      Sales\n",
              "0   2014-01-06  2573.8200\n",
              "1   2014-01-07    76.7280\n",
              "2   2014-01-10    51.9400\n",
              "3   2014-01-11     9.9400\n",
              "4   2014-01-13   879.9390\n",
              "..         ...        ...\n",
              "884 2017-12-24  1393.4940\n",
              "885 2017-12-25   832.4540\n",
              "886 2017-12-28   551.2568\n",
              "887 2017-12-29  2330.7180\n",
              "888 2017-12-30   323.1360\n",
              "\n",
              "[889 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQTbmR9M9tBF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "65b87bd9-e456-4a89-fd50-96d26caddd7d"
      },
      "source": [
        "furniture= furniture.set_index('Order Date')\n",
        "furniture"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Order Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>2573.8200</td>\n",
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              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
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              "    <tr>\n",
              "      <th>2014-01-10</th>\n",
              "      <td>51.9400</td>\n",
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              "    <tr>\n",
              "      <th>2014-01-11</th>\n",
              "      <td>9.9400</td>\n",
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              "      <th>2014-01-13</th>\n",
              "      <td>879.9390</td>\n",
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>2017-12-24</th>\n",
              "      <td>1393.4940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25</th>\n",
              "      <td>832.4540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>551.2568</td>\n",
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              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>2330.7180</td>\n",
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              "    <tr>\n",
              "      <th>2017-12-30</th>\n",
              "      <td>323.1360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>889 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Sales\n",
              "Order Date           \n",
              "2014-01-06  2573.8200\n",
              "2014-01-07    76.7280\n",
              "2014-01-10    51.9400\n",
              "2014-01-11     9.9400\n",
              "2014-01-13   879.9390\n",
              "...               ...\n",
              "2017-12-24  1393.4940\n",
              "2017-12-25   832.4540\n",
              "2017-12-28   551.2568\n",
              "2017-12-29  2330.7180\n",
              "2017-12-30   323.1360\n",
              "\n",
              "[889 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 850
        },
        "outputId": "d867eda6-62a1-44d8-ccc5-b08605019b7b"
      },
      "source": [
        "month = furniture['Sales'].resample('M').sum()\n",
        "month"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order Date\n",
              "2014-01-31     6242.5250\n",
              "2014-02-28     1839.6580\n",
              "2014-03-31    14573.9560\n",
              "2014-04-30     7944.8370\n",
              "2014-05-31     6912.7870\n",
              "2014-06-30    13206.1256\n",
              "2014-07-31    10821.0510\n",
              "2014-08-31     7320.3465\n",
              "2014-09-30    23816.4808\n",
              "2014-10-31    12304.2470\n",
              "2014-11-30    21564.8727\n",
              "2014-12-31    30645.9665\n",
              "2015-01-31    11739.9416\n",
              "2015-02-28     3134.3740\n",
              "2015-03-31    12499.7830\n",
              "2015-04-30    10475.6985\n",
              "2015-05-31     9374.9505\n",
              "2015-06-30     7714.1790\n",
              "2015-07-31    13674.4200\n",
              "2015-08-31     9638.5922\n",
              "2015-09-30    26273.0230\n",
              "2015-10-31    12026.6235\n",
              "2015-11-30    30880.8325\n",
              "2015-12-31    23085.8192\n",
              "2016-01-31     7622.7430\n",
              "2016-02-29     3925.5510\n",
              "2016-03-31    12801.0920\n",
              "2016-04-30    13212.0900\n",
              "2016-05-31    15119.8350\n",
              "2016-06-30    13070.5720\n",
              "2016-07-31    13068.5190\n",
              "2016-08-31    12483.2323\n",
              "2016-09-30    27262.8809\n",
              "2016-10-31    11872.5770\n",
              "2016-11-30    31783.6288\n",
              "2016-12-31    36678.7150\n",
              "2017-01-31     5964.0320\n",
              "2017-02-28     6866.3374\n",
              "2017-03-31    10893.4448\n",
              "2017-04-30     9065.9581\n",
              "2017-05-31    16957.5582\n",
              "2017-06-30    19008.5867\n",
              "2017-07-31    11813.0220\n",
              "2017-08-31    15441.8740\n",
              "2017-09-30    29028.2060\n",
              "2017-10-31    21884.0682\n",
              "2017-11-30    37056.7150\n",
              "2017-12-31    31407.4668\n",
              "Freq: M, Name: Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQZCbggi-_Dj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "9c461a7d-47a9-414f-a7ad-ef557803dc04"
      },
      "source": [
        "Quarterly = furniture['Sales'].resample('Q').sum()\n",
        "Quarterly"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order Date\n",
              "2014-03-31    22656.1390\n",
              "2014-06-30    28063.7496\n",
              "2014-09-30    41957.8783\n",
              "2014-12-31    64515.0862\n",
              "2015-03-31    27374.0986\n",
              "2015-06-30    27564.8280\n",
              "2015-09-30    49586.0352\n",
              "2015-12-31    65993.2752\n",
              "2016-03-31    24349.3860\n",
              "2016-06-30    41402.4970\n",
              "2016-09-30    52814.6322\n",
              "2016-12-31    80334.9208\n",
              "2017-03-31    23723.8142\n",
              "2017-06-30    45032.1030\n",
              "2017-09-30    56283.1020\n",
              "2017-12-31    90348.2500\n",
              "Freq: Q-DEC, Name: Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDsJkpAe_KEv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "outputId": "18629597-3f5b-474e-9918-5450504650bf"
      },
      "source": [
        "Yearly = furniture['Sales'].resample('Y').sum()\n",
        "Yearly"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order Date\n",
              "2014-12-31    157192.8531\n",
              "2015-12-31    170518.2370\n",
              "2016-12-31    198901.4360\n",
              "2017-12-31    215387.2692\n",
              "Freq: A-DEC, Name: Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAhyI9ad_X5p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "a5774374-cd06-4d62-fcdf-c7932c46f910"
      },
      "source": [
        "Yearly.plot(kind='area')"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec34169748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LlqMMtT_j3u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "83f3752e-62ac-4c3e-cf59-ff61b39100c6"
      },
      "source": [
        "month.plot(kind='area')"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec339c11d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-H0CSiLz_2C8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "619f6e80-b613-40e8-f002-13bce4fb4f20"
      },
      "source": [
        "month.plot()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec339c1c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPbYBieJ_5yo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "43c2b178-fcbd-4e37-d1ad-efacd4c7b0b0"
      },
      "source": [
        "month.plot(kind='bar')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec33ed76a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT8pJeeMACI6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "914f57cf-a90b-4af5-db46-2f9c95703231"
      },
      "source": [
        "Quarterly.plot(kind='area')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fec33943e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xba7pCTATkm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}