{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Week5_Assignment.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrILHsZG8csv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "407b2617-34a8-4c69-8c88-d41b82a2cfe2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/data/')\n",
        "data_dir = '/data/My Drive/Colab Notebooks/Experiment'\n",
        "!ls '/data/My Drive/Colab Notebooks/Experiment'\n",
        "!pip install matplotlib"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /data/\n",
            "m_data.csv  w_data.csv\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.18.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mu0KYE8z8wdC"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "man = pd.read_csv(data_dir+'/m_data.csv')\n",
        "woman = pd.read_csv(data_dir+'/w_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3w6ejpNRWVSA"
      },
      "source": [
        "data = pd.concat([man, woman])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOFZL6qmQn32"
      },
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77rL1xCkQuDA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "6e92d5ff-33af-4315-8f87-13604bf93b30"
      },
      "source": [
        "plt.scatter(man['bmi'], man['steps'], woman['bmi'], woman['steps'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbdb855f0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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": "X3QFekIAVZaP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ea3f3e47-8ef2-4f00-da98-51eaac1ae615"
      },
      "source": [
        "z = data.sample(n=500)\n",
        "data.sample(frac=0.4).to_csv('m_data.csv')\n",
        "data.sample(frac=0.4).to_csv('w_data.csv')\n",
        "\n",
        "plt.scatter(z['bmi'], z['steps'], marker=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbdb5f32080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "H_QKdq-_S0w7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c7cc3e61-4ca6-4b25-80b3-755404e0d70c"
      },
      "source": [
        "man.head()"
      ],
      "execution_count": null,
      "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>Unnamed: 0</th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22393</td>\n",
              "      <td>14.0</td>\n",
              "      <td>217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16685</td>\n",
              "      <td>171.0</td>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15155</td>\n",
              "      <td>91.0</td>\n",
              "      <td>168.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6162</td>\n",
              "      <td>86.0</td>\n",
              "      <td>101.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22150</td>\n",
              "      <td>146.0</td>\n",
              "      <td>215.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0    bmi  steps\n",
              "0       22393   14.0  217.0\n",
              "1       16685  171.0  176.0\n",
              "2       15155   91.0  168.0\n",
              "3        6162   86.0  101.0\n",
              "4       22150  146.0  215.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggu4_he1s_G2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0c0806ce-2902-454b-ca2a-ba851f81a462"
      },
      "source": [
        "man.describe()"
      ],
      "execution_count": null,
      "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>Unnamed: 0</th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18548.000000</td>\n",
              "      <td>18548.000000</td>\n",
              "      <td>18548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23180.819388</td>\n",
              "      <td>254.411096</td>\n",
              "      <td>219.864568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13357.077654</td>\n",
              "      <td>174.845136</td>\n",
              "      <td>102.026507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11642.750000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>145.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23307.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>222.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34706.250000</td>\n",
              "      <td>441.250000</td>\n",
              "      <td>292.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46368.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>411.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Unnamed: 0           bmi         steps\n",
              "count  18548.000000  18548.000000  18548.000000\n",
              "mean   23180.819388    254.411096    219.864568\n",
              "std    13357.077654    174.845136    102.026507\n",
              "min        2.000000      0.000000      0.000000\n",
              "25%    11642.750000     97.000000    145.000000\n",
              "50%    23307.000000    202.000000    222.000000\n",
              "75%    34706.250000    441.250000    292.000000\n",
              "max    46368.000000    549.000000    411.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7agXfCMnNfHv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "dd3fcbd1-a8f8-4ecb-a6ab-dca7d99a34a4"
      },
      "source": [
        "woman.describe()"
      ],
      "execution_count": null,
      "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>Unnamed: 0</th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18548.000000</td>\n",
              "      <td>18548.000000</td>\n",
              "      <td>18548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23282.573647</td>\n",
              "      <td>254.232909</td>\n",
              "      <td>220.864406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13439.323095</td>\n",
              "      <td>174.901153</td>\n",
              "      <td>102.534039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11537.250000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>144.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23214.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>222.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>35035.750000</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>296.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46370.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>411.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Unnamed: 0           bmi         steps\n",
              "count  18548.000000  18548.000000  18548.000000\n",
              "mean   23282.573647    254.232909    220.864406\n",
              "std    13439.323095    174.901153    102.534039\n",
              "min        1.000000      0.000000      0.000000\n",
              "25%    11537.250000     97.000000    144.000000\n",
              "50%    23214.000000    200.000000    222.000000\n",
              "75%    35035.750000    442.000000    296.000000\n",
              "max    46370.000000    549.000000    411.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJZRXlOzSJKu"
      },
      "source": [
        "As we can see from the above describtion of 2 data frame, both man and woman's average steps are very similar. Man has an average of 219.86 steps and woman has a 220.86 steps. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFB1qrj-UxLZ"
      },
      "source": [
        "For the data in Man and Woman, we can see that it also has a really close standaed deviation. From these 2 tables of data, we can tell that both man and woman have a similar walking pattern. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09wnsidTSxQA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "4633a649-aa2c-4ea8-d09d-8b0399eeb0dd"
      },
      "source": [
        "man[['bmi', 'steps']].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.099439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>steps</th>\n",
              "      <td>-0.099439</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            bmi     steps\n",
              "bmi    1.000000 -0.099439\n",
              "steps -0.099439  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDxI6z34S8zX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "79f5fd92-c227-4d0a-fd26-a4e7cdd8b298"
      },
      "source": [
        "woman[['bmi', 'steps']].corr()"
      ],
      "execution_count": null,
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>-0.0956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>steps</th>\n",
              "      <td>-0.0956</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          bmi   steps\n",
              "bmi    1.0000 -0.0956\n",
              "steps -0.0956  1.0000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecZKyINETx_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "f5fc0e75-6c0a-4fde-85d1-8c458c9f9565"
      },
      "source": [
        "man[['bmi', 'steps']].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f356a91c748>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f356a49cc88>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9ufXZRbEYLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "d2eb9cab-eca0-4171-f1b0-3b0f6a161831"
      },
      "source": [
        "woman[['bmi', 'steps']].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f356a4cbf60>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f356aa714a8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-iU_X76lYoP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}