ACM_DataScience_Session.ipynb

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      "source": [
        "# Hyping up Data Science- ACM Student Chapter IITH\n",
        "\n",
        "## Presenter: Tanmay Goyal\n",
        "Github: tanmaygoyal258"
      ],
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        "id": "-M21aYs5jChY"
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      "cell_type": "markdown",
      "source": [
        "## Numpy\n",
        "\n",
        "- Stands for NUMerical PYthon\n",
        "- Faster because base is in C/C++"
      ],
      "metadata": {
        "id": "CE-3FO8aLGY1"
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      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
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      "source": [
        "# difference between np.linspace and np.arange\n",
        "\n",
        "linspace = np.linspace(0,10,10 , endpoint = False) # uses number of equally seperated points\n",
        "arange = np.arange(0,10,1)  # uses distance between two points\n",
        "\n",
        "print(\"Linspace -> \" , linspace)\n",
        "print(\"Arange -> \" , arange)\n",
        "\n",
        "# other important matrices\n",
        "identity = np.identity(2)\n",
        "print(\"Identity matrix -> \\n\" , identity)"
      ],
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            "Linspace ->  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
            "Arange ->  [0 1 2 3 4 5 6 7 8 9]\n",
            "Identity matrix -> \n",
            " [[1. 0.]\n",
            " [0. 1.]]\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# simple shape operations\n",
        "\n",
        "print(\"The shape is \" , linspace.shape)\n",
        "print(\"The number of elements are \" , linspace.size)\n"
      ],
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          "text": [
            "The shape is  (10,)\n",
            "The number of elements are  10\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# using broadcasting\n",
        "print(1 + linspace)\n",
        "\n",
        "print(\"Why broadcasting is important to know is because it prevents needless copies of data, and allows for looping to happen in C instead of Python\")"
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          "text": [
            "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]\n",
            "Why broadcasting is important to know is because it prevents needless copies of data, and allows for looping to happen in C instead of Python\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "# simple numpy operations that apply to the entire array\n",
        "print(\"Squaring -> \\n\" , np.power(linspace , 2))\n",
        "print(\"Exponentiation -> \\n\" , np.exp(linspace))\n",
        "print(\"Logarithms -> \\n\" , np.log2(linspace + 1)) ## would have encountered a NaN if we wouldnt have added the 1"
      ],
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          "text": [
            "Squaring -> \n",
            " [ 0.  1.  4.  9. 16. 25. 36. 49. 64. 81.]\n",
            "Exponentiation -> \n",
            " [1.00000000e+00 2.71828183e+00 7.38905610e+00 2.00855369e+01\n",
            " 5.45981500e+01 1.48413159e+02 4.03428793e+02 1.09663316e+03\n",
            " 2.98095799e+03 8.10308393e+03]\n",
            "Logarithms -> \n",
            " [0.         1.         1.5849625  2.         2.32192809 2.5849625\n",
            " 2.80735492 3.         3.169925   3.32192809]\n"
          ]
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      "source": [
        "# getting into some matrices\n",
        " \n",
        "matrix = np.reshape(linspace , (2,5))    # two rows and five columns\n",
        "print(\"Matrix -> \\n\" , matrix)\n",
        "matrix2 = np.reshape(linspace , (5 , -1)) # if you are too lazy to calculate the number of rows or columns, let numpy do it! \n",
        "print(\"Matrix2 -> \\n\" , matrix2)\n",
        "\n",
        "## difference between (10,) and (10,1)\n",
        "print(\"Reshaped as (10,) \\n{}\".format(matrix.reshape(10,)))\n",
        "print(\"Reshaped as (10,1) \\n{}\".format(matrix.reshape(10,1)))"
      ],
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          "text": [
            "Matrix -> \n",
            " [[0. 1. 2. 3. 4.]\n",
            " [5. 6. 7. 8. 9.]]\n",
            "Matrix2 -> \n",
            " [[0. 1.]\n",
            " [2. 3.]\n",
            " [4. 5.]\n",
            " [6. 7.]\n",
            " [8. 9.]]\n",
            "Reshaped as (10,) \n",
            "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
            "Reshaped as (10,1) \n",
            "[[0.]\n",
            " [1.]\n",
            " [2.]\n",
            " [3.]\n",
            " [4.]\n",
            " [5.]\n",
            " [6.]\n",
            " [7.]\n",
            " [8.]\n",
            " [9.]]\n"
          ]
        }
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      "source": [
        "# slicing and dicing\n",
        "print(\"First row of matrix is -> \\n\" , matrix[0 , :])\n",
        "print(\"a_12 of matrix is -> \\n\" , matrix[0][1])\n",
        "print(\"Third column of matrix is ->\\n\" , matrix[: , 2])\n",
        "print(\"All the rows from column 2 to column 4 are -> \\n\" , matrix[: , 1:3])\n",
        "print(\"Reversing all the rows of matrix -> \\n\" , matrix[: , ::-1])\n",
        "print(\"In general reversing can be done using the ::-1 format. For example, reversing linspace ->\\n\" , linspace[::-1])\n",
        "print(\"Take Care, Python follows ZERO-indexing\")"
      ],
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          "text": [
            "First row of matrix is -> \n",
            " [0. 1. 2. 3. 4.]\n",
            "a_12 of matrix is -> \n",
            " 1.0\n",
            "Third column of matrix is ->\n",
            " [2. 7.]\n",
            "All the rows from column 2 to column 4 are -> \n",
            " [[1. 2.]\n",
            " [6. 7.]]\n",
            "Reversing all the rows of matrix -> \n",
            " [[4. 3. 2. 1. 0.]\n",
            " [9. 8. 7. 6. 5.]]\n",
            "In general reversing can be done using the ::-1 format. For example, reversing linspace ->\n",
            " [9. 8. 7. 6. 5. 4. 3. 2. 1. 0.]\n",
            "Take Care, Python follows ZERO-indexing\n"
          ]
        }
      ]
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      "source": [
        "# transposing matrices\n",
        "print(\"Transpose can be achoeved using np.transpose() or simply .T -> \\n\",np.transpose(matrix))\n",
        "print(\"Like we said another way using .T-> \\n\" , matrix.T)\n",
        "print(\"For more than 2D matrices, be careful of which dimension to transpose along\")"
      ],
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          "text": [
            "Transpose can be achoeved using np.transpose() or simply .T -> \n",
            " [[0. 5.]\n",
            " [1. 6.]\n",
            " [2. 7.]\n",
            " [3. 8.]\n",
            " [4. 9.]]\n",
            "Like we said another way using .T-> \n",
            " [[0. 5.]\n",
            " [1. 6.]\n",
            " [2. 7.]\n",
            " [3. 8.]\n",
            " [4. 9.]]\n",
            "For more than 2D matrices, be careful of which dimension to transpose along\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# matrix multiplication and dot products\n",
        "print(\"Matrix Multiplication -> of matrix and matrix2 -> \\n\" , np.matmul(matrix , matrix2))\n",
        "print(\"Simply use @ for matrix Multiplication -> \\n \" , matrix @ matrix2)\n",
        "print(\"Element wise multiplication -> \\n\" ,np.multiply(matrix , matrix))\n",
        "print(\"Dot product -> \\n \" , np.dot(linspace , linspace))"
      ],
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          "text": [
            "Matrix Multiplication -> of matrix and matrix2 -> \n",
            " [[ 60.  70.]\n",
            " [160. 195.]]\n",
            "Simply use @ for matrix Multiplication -> \n",
            "  [[ 60.  70.]\n",
            " [160. 195.]]\n",
            "Element wise multiplication -> \n",
            " [[ 0.  1.  4.  9. 16.]\n",
            " [25. 36. 49. 64. 81.]]\n",
            "Dot product -> \n",
            "  285.0\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# and the normal functions\n",
        "\n",
        "print(\"Sum of matrix -> \\n\" , np.sum(matrix))\n",
        "print(\"Sum of each column -> \\n\" , np.sum(matrix , axis = 0))\n",
        "print(\"Average of matrix -> \\n\" , np.mean(matrix))\n",
        "print(\"Average of matrix along each row \\n\" , np.mean(matrix , axis = 1))"
      ],
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        "id": "9gGOiZzFzMqI",
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          "text": [
            "Sum of matrix -> \n",
            " 45.0\n",
            "Sum of each column -> \n",
            " [ 5.  7.  9. 11. 13.]\n",
            "Average of matrix -> \n",
            " 4.5\n",
            "Average of matrix along each row \n",
            " [2. 7.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# working with NaNs\n",
        "print(type(np.nan))\n",
        "\n",
        "arr = [i for i in range(10)]\n",
        "arr.append(np.nan)\n",
        "\n",
        "print(np.isnan(arr))\n",
        "print(np.isfinite(arr))"
      ],
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        },
        "id": "Gmw43rIxNsP2",
        "outputId": "67a73339-ebd7-4b73-a762-28eea7f00518"
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          "text": [
            "<class 'float'>\n",
            "[False False False False False False False False False False  True]\n",
            "[ True  True  True  True  True  True  True  True  True  True False]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pandas\n",
        "- Derived from \"PANel DAta\"\n",
        "- Either Pandas.Series (Column of Data) or Pandas.DataFrame\n"
      ],
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        "id": "xfjMMGBJQUlL"
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "QGtLGjLdoFaZ"
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        "# creating a dataframe\n",
        "\n",
        "fruits = {\"apples\" : [1,2,3,4,5,6,7,8,9,2,3,4,5,6,1,2,3] , \n",
        "          \"oranges\" : [1,4,9,16,25,36,49,64,81,4,9,16,25,36,1,4,9]}\n",
        "\n",
        "df = pd.DataFrame(fruits)\n",
        "df"
      ],
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              "    apples  oranges\n",
              "0        1        1\n",
              "1        2        4\n",
              "2        3        9\n",
              "3        4       16\n",
              "4        5       25\n",
              "5        6       36\n",
              "6        7       49\n",
              "7        8       64\n",
              "8        9       81\n",
              "9        2        4\n",
              "10       3        9\n",
              "11       4       16\n",
              "12       5       25\n",
              "13       6       36\n",
              "14       1        1\n",
              "15       2        4\n",
              "16       3        9"
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              "\n",
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              "          document.querySelector('#df-46dfdccf-c6e7-4a35-aa46-cdf2856ac2fb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
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      ]
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      "cell_type": "code",
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        "df.head()"
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        "outputId": "e54a7197-effd-461e-f3ab-3a3c5da0d99f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   apples  oranges\n",
              "0       1        1\n",
              "1       2        4\n",
              "2       3        9\n",
              "3       4       16\n",
              "4       5       25"
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9W1xcqgofoe",
        "outputId": "3f7b7dd4-4cf1-40bf-aa54-03dc2410cfd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17 entries, 0 to 16\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype\n",
            "---  ------   --------------  -----\n",
            " 0   apples   17 non-null     int64\n",
            " 1   oranges  17 non-null     int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 400.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cY-Lp5uhopxB",
        "outputId": "c9e8c152-f82c-47ec-b28c-084daded092f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          apples    oranges\n",
              "count  17.000000  17.000000\n",
              "mean    4.176471  22.882353\n",
              "std     2.404041  23.457095\n",
              "min     1.000000   1.000000\n",
              "25%     2.000000   4.000000\n",
              "50%     4.000000  16.000000\n",
              "75%     6.000000  36.000000\n",
              "max     9.000000  81.000000"
            ],
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              "      <th>count</th>\n",
              "      <td>17.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.176471</td>\n",
              "      <td>22.882353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.404041</td>\n",
              "      <td>23.457095</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>36.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96865841-e10e-4645-9ef8-beb085809290');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other ways to read Data\n",
        "\n",
        "pd.read_csv()\n",
        "\n",
        "pd.read_json()\n",
        "\n",
        "pd.read_html()\n",
        "\n",
        "pd.read_excel()\n",
        "\n",
        "Can also convert back to other formats"
      ],
      "metadata": {
        "id": "MrdcDMmVql5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)\n",
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxUwWh2uq25h",
        "outputId": "835ad132-ac4d-4869-feee-1d55049bffae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(17, 2)\n",
            "Index(['apples', 'oranges'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for Nans or Null\n",
        "print(df.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3EXh-SVq936",
        "outputId": "aa1a312b-8ba7-4ae2-989a-856dfb5aa52f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    apples  oranges\n",
            "0    False    False\n",
            "1    False    False\n",
            "2    False    False\n",
            "3    False    False\n",
            "4    False    False\n",
            "5    False    False\n",
            "6    False    False\n",
            "7    False    False\n",
            "8    False    False\n",
            "9    False    False\n",
            "10   False    False\n",
            "11   False    False\n",
            "12   False    False\n",
            "13   False    False\n",
            "14   False    False\n",
            "15   False    False\n",
            "16   False    False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What about when our df is really big\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# can simply drop using df.dropna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoYOU-YXrCzN",
        "outputId": "b2d9363b-fe58-46f0-cd11-25a2361cb1aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apples     0\n",
            "oranges    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# indexing and slicing\n",
        "print(df['apples'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEWtLt-zrYRS",
        "outputId": "15bb6da8-7d93-4699-c49b-b33bf8af14ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     1\n",
            "1     2\n",
            "2     3\n",
            "3     4\n",
            "4     5\n",
            "5     6\n",
            "6     7\n",
            "7     8\n",
            "8     9\n",
            "9     2\n",
            "10    3\n",
            "11    4\n",
            "12    5\n",
            "13    6\n",
            "14    1\n",
            "15    2\n",
            "16    3\n",
            "Name: apples, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_indices = pd.DataFrame(fruits , index = [chr(ord('A') + i) for i in range(len(fruits['apples']))])\n",
        "df_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "FCw2kATXrxC8",
        "outputId": "1c35da85-663d-4d55-9ad6-36abe8452e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   apples  oranges\n",
              "A       1        1\n",
              "B       2        4\n",
              "C       3        9\n",
              "D       4       16\n",
              "E       5       25\n",
              "F       6       36\n",
              "G       7       49\n",
              "H       8       64\n",
              "I       9       81\n",
              "J       2        4\n",
              "K       3        9\n",
              "L       4       16\n",
              "M       5       25\n",
              "N       6       36\n",
              "O       1        1\n",
              "P       2        4\n",
              "Q       3        9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01d9a10b-e2aa-4475-92c1-37efa160a896\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>7</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H</th>\n",
              "      <td>8</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>I</th>\n",
              "      <td>9</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>J</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N</th>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>O</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01d9a10b-e2aa-4475-92c1-37efa160a896')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-01d9a10b-e2aa-4475-92c1-37efa160a896 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-01d9a10b-e2aa-4475-92c1-37efa160a896');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using loc and iloc\n",
        "print(df_indices.loc[\"M\"])\n",
        "\n",
        "print(df_indices.iloc[12])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhD0UDFUro5m",
        "outputId": "545d18e8-0626-4c63-a6ba-da76ccb77d11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apples      5\n",
            "oranges    25\n",
            "Name: M, dtype: int64\n",
            "apples      5\n",
            "oranges    25\n",
            "Name: M, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# can slice using loc and iloc\n",
        "print(df_indices.loc[\"H\" : \"M\"])\n",
        "print(df_indices.iloc[7:12])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAB2egfTDrrJ",
        "outputId": "ff8f02cc-d6ac-4866-9d56-2ac070da0484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   apples  oranges\n",
            "H       8       64\n",
            "I       9       81\n",
            "J       2        4\n",
            "K       3        9\n",
            "L       4       16\n",
            "M       5       25\n",
            "   apples  oranges\n",
            "H       8       64\n",
            "I       9       81\n",
            "J       2        4\n",
            "K       3        9\n",
            "L       4       16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Matplotlib\n",
        "- Documented really well\n",
        "- Allows for almost any kind of plot simply with one command"
      ],
      "metadata": {
        "id": "PBKWdoxwVAqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "E1u7dwuzWa4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = [-3 , -2 , -1 , 0 , 1 , 2 , 3]\n",
        "y = [9 , 4 , 1 , 0 , 1 , 4 , 9]\n",
        "y = np.array(y)\n",
        "\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.subplot(2 , 2 , 1)\n",
        "plt.scatter(x , y , color = 'red')\n",
        "\n",
        "plt.subplot(2 , 2 , 2)\n",
        "plt.plot(x , y , color = 'red' , marker = 'o')\n",
        "\n",
        "plt.subplot(2 , 2 , 3)\n",
        "plt.bar(x , y , width = 0.5)\n",
        "plt.plot(x , y , 'ro--')\n",
        "\n",
        "plt.subplot(2 , 2 , 4)\n",
        "plt.fill_between(x, y - 2 , y+2 , color = 'k' , where = [True , True , False , True , True , True , True])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "4UEoNwsfV1Gt",
        "outputId": "9aed02de-6983-4569-ffa7-54c3d985178e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets try adding multiple lines\n",
        "x = np.linspace(1,5,4,endpoint = False)\n",
        "\n",
        "square = x ** 2\n",
        "random = x ** 2.1\n",
        "root = x ** 0.5\n",
        "log = np.log(x)\n",
        "\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.subplot(2 , 2 , 1)\n",
        "plt.plot(x , square , 'r')\n",
        "plt.plot(x , random , 'k')\n",
        "plt.plot(x , root , 'b')\n",
        "plt.plot(x , log , 'g')\n",
        "\n",
        "\n",
        "# how about some better labelling\n",
        "plt.subplot(2 , 2 , 2)\n",
        "plt.plot(x , square , 'r' , label = \"Square\")\n",
        "plt.plot(x , random , 'k' , label = \"Power 2.1\")\n",
        "plt.plot(x , root , 'b' , label = \"Square Root\")\n",
        "plt.plot(x , log , 'g' , label = \"Logarithm\")\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "# more enhancement maybe\n",
        "plt.subplot(2 , 2 , 3)\n",
        "plt.plot(x , square , 'r' , label = \"Square\")\n",
        "plt.plot(x , random , 'k' , label = \"Power 2.1\")\n",
        "plt.plot(x , root , 'b' , label = \"Square Root\")\n",
        "plt.plot(x , log , 'g' , label = \"Logarithm\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "# finally lets add some titles and labels# more enhancement maybe\n",
        "plt.subplot(2 , 2 , 4)\n",
        "plt.plot(x , square , 'r' , label = \"Square\")\n",
        "plt.plot(x , random , 'k' , label = \"Power 2.1\")\n",
        "plt.plot(x , root , 'b' , label = \"Square Root\")\n",
        "plt.plot(x , log , 'g' , label = \"Logarithm\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.title(\"Various Mathematical Functions\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "sLGwSfd1WXRg",
        "outputId": "3cf4bc64-fd6e-4bc5-c0ac-1b9eefaae45e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now, we are kind of ready to try some analysis ourselves.. Are you ready?\n",
        "\n",
        "Reference: https://towardsdatascience.com/data-analysis-for-beginners-90a8d53fa2f9"
      ],
      "metadata": {
        "id": "4vcR78aPaPuX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Data for exercise: donot change\n",
        "import pandas as pd\n",
        "\n",
        "data = ['Activity ID,Activity Date,Activity Name,Activity Type,Activity Description,Elapsed Time,Distance,Relative Effort,Commute,Activity Gear,Filename,Athlete Weight,Bike Weight,Elapsed Time,Moving Time,Distance,Max Speed,Average Speed,Elevation Gain,Elevation Loss,Elevation Low,Elevation High,Max Grade,Average Grade,Average Positive Grade,Average Negative Grade,Max Cadence,Average Cadence,Max Heart Rate,Average Heart Rate,Max Watts,Average Watts,Calories,Max Temperature,Average Temperature,Relative Effort,Total Work,Number of Runs,Uphill Time,Downhill Time,Other Time,Perceived Exertion,\"Type\",\"Start Time\",Weighted Average Power,Power Count,Prefer Perceived Exertion,Perceived Relative Effort,Commute,Total Weight Lifted,From Upload,Grade Adjusted Distance,Weather Observation Time,Weather Condition,Weather Temperature,Apparent Temperature,Dewpoint,Humidity,Weather Pressure,Wind Speed,Wind Gust,Wind Bearing,Precipitation Intensity,Sunrise Time,Sunset Time,Moon Phase,Bike,Gear,Precipitation Probability,Precipitation Type,Cloud Cover,Weather Visibility,UV Index,Weather Ozone,\"Jump Count\",\"Total Grit\",\"Avg Flow\",\"Flagged\",\"Avg Elapsed Speed\"\\n',\n",
        "'2987876185,\"Jan 4, 2020, 2:57:37 PM\",Afternoon Run,Run,,1509,5,,FALSE,,activities/3184653380.fit.gz,63.40000153,,1509,1509,5005.200195,4.5,3.316898584,30,35,1.799999952,56.79999924,4.300000191,0.555422366,,,86,83.19561005,,,,,344,,14,,,,,,,,,,,,,,0,,1,5014.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'2987899333,\"Jan 6, 2020, 4:07:35 PM\",Afternoon Run,Run,,721,2.5,,FALSE,,activities/3184679040.fit.gz,63.40000153,,721,721,2500.899902,4.699999809,3.468654633,17,19,8.399999619,134.3999939,21.10000038,-0.127953932,,,86,82.30651855,,,,,173,,14,,,,,,,,,,,,,,0,,1,2532.300049,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3003885525,\"Jan 12, 2020, 10:06:24 AM\",Morning Run,Run,,1364,5,,FALSE,,activities/3202691924.fit.gz,63.40000153,,1364,1364,5004.399902,4.400000095,3.668915033,25,32,25.60000038,51.79999924,7.400000095,-0.143873394,,,86,83.89527893,,,,,345,,11,,,,,,,,,,,,,,0,,1,5007.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3005534889,\"Jan 12, 2020, 2:26:39 PM\",Afternoon Activity,Workout,,5360,4.29,,FALSE,,activities/3204486042.fit.gz,,,5360,3029,4291,3.400000095,1.416639209,15,8,18.39999962,47,21.10000038,0.186436728,,,107,59.51356125,,,,,210,,14,,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3066339878,\"Jan 23, 2020, 5:02:40 PM\",Afternoon Ride,Ride,,1807,0.37,,FALSE,,activities/3272705177.fit.gz,63.40000153,,1807,199,374,0.899999976,1.879397035,1,0,-90.19999695,31,1.399999976,32.29946518,,,,,,,,2.618090391,99,,26,,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3066340308,\"Feb 2, 2020, 6:01:03 PM\",Evening Run,Run,,1513,4.87,,FALSE,,activities/3272705647.fit.gz,63.40000153,,1513,1513,4876.399902,6.400000095,3.223000765,40,277,0.800000012,248.6000061,6.099999905,-4.872447014,,,83,80.45370483,,,,,331,,15,,,,,,,,,,,,,,0,,1,5058.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3159313176,\"Mar 6, 2020, 2:19:13 PM\",Leinsterman,Hike,,6235,7.62,,FALSE,,activities/3159313176.gpx,,,6235,5312,7622.299805,3.400000095,1.434920907,316.6000366,312.8999939,152.5,445.3999939,37.09999847,0.048541781,,,,,,,,,593.7642822,,,,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3297484733,\"Apr 14, 2020, 12:49:59 PM\",Afternoon Run,Run,,1449,5,,FALSE,,activities/3524783420.fit.gz,63.40000153,,1449,1449,5003.5,4.300000191,3.453071117,33,34,-202.1999969,25.79999924,4.699999809,-0.043969221,,,85,82.66435242,,,,,349,,20,,,,,,,,,,,,,,0,,1,5036.899902,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3311642631,\"Apr 17, 2020, 1:34:36 PM\",Afternoon Run,Run,,1337,4.71,,FALSE,,activities/3539658391.fit.gz,63.40000153,,1337,1337,4717,5.699999809,3.5280478,29,28,12.39999962,127,3.799999952,0.016959932,,,85,82.41127777,,,,,325,,18,,,,,,,,,,,,,,0,,1,4741.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3327536303,\"Apr 20, 2020, 4:51:35 PM\",Afternoon Ride more like afternoon stride haha,Ride,,5618,29.94,,FALSE,,activities/3556446240.fit.gz,63.40000153,,5618,4617,29948.90039,10.89999962,6.486658096,247,254,-5,50,42.09999847,-0.024708753,,,,,,,,100.1663742,790,,20,,,,,,,3,,,,,0,38,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3360705071,\"Apr 23, 2020, 2:29:40 PM\",Afternoon Run,Run,,1265,4.13,,FALSE,,activities/3591264962.fit.gz,63.40000153,,1265,1265,4130.700195,3.799999952,3.265375376,26,23,7.400000095,24.20000076,40.70000076,0.033892561,,,84,81.74185944,,,,,285,,23,,,,,,,,,,,,,,0,,1,4163.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3360705843,\"Apr 27, 2020, 11:15:26 AM\",Lunch Run,Run,,1279,4.49,,FALSE,,activities/3591265736.fit.gz,63.40000153,,1279,1279,4498.399902,4.599999905,3.517122746,31,29,5,97.80000305,4.199999809,0.035568204,,,86,81.16666412,,,,,311,,18,,,,,,,,,,,,,,0,,1,4529,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3370375603,\"Apr 29, 2020, 12:25:29 PM\",Afternoon Run,Run,,1418,5,,FALSE,,activities/3601452773.fit.gz,63.40000153,,1418,1418,5005.799805,4.699999809,3.530183315,34,31,17.79999924,159.6000061,5.400000095,0.047944386,,,88,81.57849884,,,,,348,,18,,,,,,,,,,,,,,0,,1,5035.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3379490804,\"May 1, 2020, 12:04:50 PM\",Afternoon Run,Run,,1460,5,,FALSE,,activities/3610999958.fit.gz,63.40000153,,1460,1460,5004.600098,4.400000095,3.427808285,36,39,-44.20000076,40.59999847,6.099999905,-0.079926468,,,88,81.69067383,,,,,348,,18,,,,,,,,,,,,,,0,,1,5035.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3413872288,\"May 7, 2020, 3:57:06 PM\",Afternoon Run,Run,,1483,5,,FALSE,,activities/3646821539.fit.gz,63.40000153,,1483,1483,5003.600098,4.300000191,3.373971701,32,33,-100.4000015,37.59999847,6.400000095,-0.043968342,,,85,82.23144531,,,,,347,,21,,,,,,,,,,,,,,0,,1,5034.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3422882825,\"May 9, 2020, 9:16:03 AM\",Morning Run,Run,,1476,5,,FALSE,,activities/3656392323.fit.gz,63.40000153,,1476,1476,5004.100098,7.800000191,3.390311718,44,43,12,45,6.400000095,0.007993446,,,86,83.40068054,,,,,345,,22,,,,,,,,,,,,,,0,,1,5055.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3461952369,\"May 16, 2020, 6:01:07 PM\",Evening Run,Run,,1446,5,,FALSE,,activities/3697434283.fit.gz,63.40000153,,1446,1446,5003.100098,4.300000191,3.459958553,32,34,-66.80000305,21,4.5,-0.063960344,,,86,82.32571411,,,,,347,,17,,,,,,,,,,,,,,0,,1,5033.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3475861110,\"May 19, 2020, 10:00:03 AM\",Lunch Run,Run,,1475,5.01,,FALSE,,activities/3712115566.fit.gz,63.40000153,,1475,1475,5012.100098,4.5,3.398033857,41,42,21.20000076,39.20000076,14,-0.015961373,,,85,82.75480652,,,,,347,,20,,,,,,,,,,,,,,0,,1,5073.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3482105945,\"May 20, 2020, 12:13:51 PM\",Afternoon Run,Run,,1471,5.01,,FALSE,,activities/3718650545.fit.gz,63.40000153,,1471,1471,5010.700195,4.5,3.406322241,40,41,6,21,8.600000381,0.015965832,,,86,82.93133545,,,,,345,,20,,,,,,,,,,,,,,0,,1,5069.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566748839,\"May 24, 2020, 11:26:01 AM\",Lunch Run,Run,,713,2.67,,FALSE,,activities/3808169519.fit.gz,,,713,713,2669.199951,9.899999619,3.743618488,139,14,-111.5999985,19.39999962,29,4.735501289,,,85,83.04488373,,,,,182,,20,,,,,,,,,,,,,,0,,1,3482.699951,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566749237,\"May 24, 2020, 11:41:40 AM\",Lunch Run,Run,,596,2,,FALSE,,activities/3808169913.fit.gz,,,596,596,2005.5,4.099999905,3.364932775,11,12,11.80000019,24.20000076,44.29999924,0,,,85,82.61383057,,,,,139,,23,,,,,,,,,,,,,,0,,1,2031.599976,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566749685,\"May 25, 2020, 4:45:05 PM\",Afternoon Run,Run,,627,2.5,,FALSE,,activities/3808170372.fit.gz,,,627,627,2503,10.39999962,3.992025614,13,16,-102.8000031,15.19999981,4.099999905,-0.151817814,,,87,84.97257996,,,,,167,,17,,,,,,,,,,,,,,0,,1,2505,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566750271,\"May 26, 2020, 2:31:24 PM\",Afternoon Run,Run,,1512,5,,FALSE,,activities/3808170991.fit.gz,,,1512,1512,5000.100098,4.099999905,3.30694437,38,39,-148.3999939,32.20000076,14.5,-0.01599968,,,87,83.07855988,,,,,349,,23,,,,,,,,,,,,,,0,,1,5036.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566750866,\"Jun 1, 2020, 9:03:59 AM\",Morning Run,Run,,1512,5,,FALSE,,activities/3808171611.fit.gz,,,1512,1472,5005.600098,4.300000191,3.400543451,35,37,20,34,20.5,-0.00799105,,,85,82.15023804,,,,,346,,25,,,,,,,,,,,,,,0,,1,5056,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3566751433,\"Jun 5, 2020, 10:21:08 AM\",Lunch Run,Run,,938,3.1,,FALSE,,activities/3808172172.fit.gz,,,938,923,3099.699951,4.800000191,3.358288288,33,132,86.40000153,198,7.599999905,-3.226118565,,,85,82.21800232,,,,,211,,21,,,,,,,,,,,,,,0,,1,3353,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3570926649,\"Jun 6, 2020, 8:45:06 AM\",Morning Run,Run,,2030,6.49,,FALSE,,activities/3812593451.fit.gz,,,2030,2024,6491,4.300000191,3.207015753,51,52,-4.800000191,68,13,-0.012324757,,,88,84.70870972,,,,,449,,16,,,,,,,,,,,,,,0,,1,6542.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3612625171,\"Jun 14, 2020, 10:11:45 AM\",Lunch Run,Run,,3159,10,,FALSE,,activities/3856662494.fit.gz,,,3159,3159,10002.5,6.099999905,3.166350126,93,89,19.60000038,44.40000153,13.5,0.027993001,,,86,83.14910889,,,,,689,,22,,,,,,,,,,,,,,0,,1,10170.2998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3625467909,\"Jun 16, 2020, 7:06:47 PM\",Evening Run,Run,,1529,5.01,,FALSE,,activities/3870334105.fit.gz,,,1529,1529,5015.299805,4.199999809,3.28011775,31,33,6.599999905,34.20000076,11.69999981,0,,,84,81.97731781,,,,,348,,19,,,,,,,,,,,,,,0,,1,5054.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3628536545,\"Jun 17, 2020, 12:16:22 PM\",Afternoon Run,Run,,1241,4,,FALSE,,activities/3873648686.fit.gz,,,1241,1241,4002.5,4.5,3.225221634,34,29,26.79999924,40.59999847,26.89999962,0.119925044,,,86,82.80065918,,,,,274,,18,,,,,,,,,,,,,,0,,1,4062.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3916520976,\"Aug 8, 2020, 4:48:44 PM\",Afternoon Ride,Ride,,4134,22.78,,FALSE,,activities/4192096633.fit.gz,,,4134,3573,22788.30078,10,6.377917767,137,133,-29,42.20000076,49.59999847,0.020185797,,,,,,,,83.26056671,564,,24,,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'3916522446,\"Aug 15, 2020, 12:43:36 PM\",Afternoon Ride,Ride,,3527,13.91,,FALSE,,activities/4192098295.fit.gz,,,3527,2700,13912.59961,7.900000095,5.152814865,80,78,25.39999962,88.40000153,4.900000095,0.014375458,,,,,,,,55.70037079,306,,25,,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4087456537,\"Sep 20, 2020, 12:00:44 PM\",20km loop,Ride,,4979,21.8,,FALSE,,activities/4087456537.gpx,,,4979,3882,21803,11.5,5.616435051,127.4839783,127.4839783,17.29999924,48.40000153,10.19999981,0,,,,,,,,70.63587952,305.7424622,,,,,,,,,6,,,,,0,102,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4313504100,\"Nov 9, 2020, 4:44:13 PM\",Afternoon Ride,Ride,,1879,4.55,,FALSE,,activities/4313504100.gpx,,,1879,1579,4555.700195,4.5,2.885180473,23.67076874,23.67076874,27.39999962,36.79999924,4.300000191,0,,,,,,,,48.83584976,85.97966766,,,,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4316293626,\"Nov 10, 2020, 9:31:26 AM\",Morning Ride,Ride,,1742,5.15,,FALSE,,activities/4316293626.gpx,,,1742,1735,5152,7.900000095,2.969452381,30.65567398,30.85567474,25.20000076,36.79999924,5.5,-0.003881988,,,,,,,,52.94978333,102.4326782,,,,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4468159551,\"Dec 13, 2020, 4:23:01 PM\",Afternoon Ride,Ride,,431,1.53,,FALSE,,activities/4468159551.gpx,,,431,422,1535.800049,7.900000095,3.639336586,7.893333435,8.293333054,27,35,2.900000095,-0.026045058,,,,,,,,46.7319603,21.9887886,,,,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4582811952,\"Jan 7, 2021, 4:53:52 PM\",Regretting starting the Jan 100k,Run,,1794,5.75,,FALSE,,activities/4582811952.gpx,,,1794,1771,5752.100098,4.5,3.24793911,28.88963318,29.08963203,19.5,36.79999924,5.699999809,-0.003476991,,,,,,,,,451.0156555,,,,,,,,,5,,,,,0,31,0,,1,5789.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4587730081,\"Jan 8, 2021, 4:30:06 PM\",Earphone fit test,Run,,2296,6.85,,FALSE,,activities/4587730081.gpx,,,2296,2296,6852.5,4.400000095,2.984538317,28.84967232,28.9496727,19.5,36.79999924,6.300000191,-0.001459321,,,,,,,,,536.7661133,,,,,,,,,5,,,,,0,41,0,,1,6890.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4596226297,\"Jan 10, 2021, 8:42:24 AM\",Sunrise run - ty town council for the lovely running track ,Run,,3049,10,,FALSE,,activities/4596226297.gpx,,,3049,3040,10002,5.800000191,3.290131569,91.72351837,91.02352142,20.20000076,25.29999924,14.69999981,0.0069986,,,,,,,,,798.3953857,,,,,,,,,6,,,,,0,80,0,,1,10249.2002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4607368527,\"Jan 12, 2021, 8:46:54 AM\",Rainy Run,Run,,1711,5.63,48,FALSE,,activities/4921449526.fit.gz,,,1711,1688,5632.5,4.800000191,3.336789131,28.77401924,28.87400055,19.5,36.79999924,6.5,-0.001775411,,,,,,164.9637756,,,323,,,48,,,,,,6,,,,,0,44,0,,1,5670.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4614041756,\"Jan 13, 2021, 11:47:05 AM\",1/3 to Jan 100k,Run,,1745,6.02,68,FALSE,,activities/4928414405.fit.gz,,,1745,1735,6023.799805,6.599999905,3.471930742,28.77323914,28.77319908,19.5,36.79999924,6.800000191,0,,,,,,172.2217102,,,341,,,68,,,,,,7,,,,,0,55,0,,1,6063.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4619922125,\"Jan 14, 2021, 4:04:29 PM\",\"Very nice, very light :)\",Run,,2497,5.01,9,FALSE,,activities/4934566668.fit.gz,,,2497,2067,5017.100098,3.799999952,2.427237511,32.5843544,32.58440018,6.099999905,18.39999962,31.20000076,0,,,,,,130.3765564,,,280,,,9,,,,,,1,,,,,0,4,0,,1,5055.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4624241650,\"Jan 15, 2021, 1:39:16 PM\",Afternoon Run,Run,,1734,5.6,46,FALSE,,activities/4939072087.fit.gz,,,1734,1729,5600,5.300000191,3.238866329,28.75479126,29.0547924,19.5,36.79999924,6.599999905,-0.005357143,,,,,,162.2858734,,,317,,,46,,,,,,,,,,,,,0,,1,5638.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4634093110,\"Jan 17, 2021, 9:18:55 AM\",Big hill 🥵,Run,,3390,10.36,69,FALSE,,activities/4949341166.fit.gz,,,3390,3346,10365.2998,4.699999809,3.097818375,81.55368805,81.4536972,19.5,42.5,18.70000076,0.000964757,,,,,,157.5407257,,,604,,,69,,,,,,7,,,,,0,106,0,,1,10561.2998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4650661272,\"Jan 20, 2021, 8:23:32 AM\",2/3 to Jan 100k,Run,,3164,10.01,105,FALSE,,activities/4966576666.fit.gz,,,3164,3160,10013.09961,5.199999809,3.168702602,81.57991028,78.57990265,19.5,42.59999847,17.29999924,0.029960752,,,,,,168.8744049,,,632,,,105,,,,,,7,,,,,0,100,0,,1,10218.7002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4656882837,\"Jan 21, 2021, 1:10:14 PM\",Recovery run ended up being one of my better runs,Run,,1461,5.01,48,FALSE,,activities/4973049149.fit.gz,,,1461,1458,5014.299805,4.699999809,3.439163208,25.6980629,18.69809914,19.5,36.79999924,11.39999962,0.139600739,,,,,,168.731781,,,297,,,48,,,,,,6,,,,,0,38,0,,1,5065.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4666542076,\"Jan 23, 2021, 10:42:14 AM\",Morning Run,Run,,2473,7.7,63,FALSE,,activities/4983106050.fit.gz,,,2473,2446,7699.5,4.599999905,3.147792339,61.05551529,60.95551682,19.5,42.5,20,0.001298786,,,,,,161.34552,,,460,,,63,,,,,,,,,,,,,0,,1,7864.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4674589378,\"Jan 24, 2021, 2:52:40 PM\",Afternoon Run,Run,,1768,5.66,40,FALSE,,activities/4991484945.fit.gz,,,1768,1755,5667.5,4.300000191,3.229344845,28.80679131,28.70679092,19.5,36.79999924,7,0.001764446,,,,,,157.7692261,,,334,,,40,,,,,,,,,,,,,0,,1,5706.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4683265912,\"Jan 26, 2021, 10:48:42 AM\",Morning Run,Run,,1646,5.64,47,FALSE,,activities/5000526488.fit.gz,,,1646,1642,5648.700195,4.800000191,3.440134048,28.73003387,28.63003349,19.5,36.79999924,7.300000191,0.001770319,,,,,,165.0762177,,,335,,,47,,,,,,,,,,,,,0,,1,5687.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4695570599,\"Jan 28, 2021, 3:50:01 PM\",First half solo second half mama ,Run,Ty for 100k let’s go feb,3842,11.41,99,FALSE,,activities/5013295206.fit.gz,,,3842,3725,11418.5,5.599999905,3.065369129,58.16294479,58.36289978,19.5,36.79999924,6.599999905,-0.001751544,,,,,,161.0064545,,,746,,,99,,,,,,7,,,,,0,118,0,,1,11480.7998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4699273059,\"Jan 29, 2021, 8:20:18 AM\",Morning Run,Run,,1942,5.7,25,FALSE,,activities/5017149051.fit.gz,,,1942,1939,5701.299805,4,2.940330029,28.82689667,28.72689629,19.5,36.79999924,7.5,0.001753986,,,,,,149.9911957,,,352,,,25,,,,,,,,,,,,,0,,1,5739,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4711824243,\"Jan 31, 2021, 2:16:54 PM\",Afternoon Run,Run,,1673,5.64,49,FALSE,,activities/5030216579.fit.gz,,,1673,1663,5641.700195,5.199999809,3.392483473,28.76101112,28.86101151,19.5,36.79999924,7.300000191,-0.001772515,,,,,,164.1985474,,,327,,,49,,,,,,,,,,,,,0,,1,5680.700195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4716859368,\"Feb 1, 2021, 1:49:47 PM\",Lovely dipply rain 👺,Run,,3513,10.38,82,FALSE,,activities/5035497793.fit.gz,,,3513,3496,10385.09961,4.699999809,2.970566273,81.53449249,81.53450012,19.5,42.59999847,17.79999924,0,,,,,,158.0631256,,,637,,,82,,,,,,7,,,,,0,111,0,,1,10579.09961,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4727576684,\"Feb 3, 2021, 11:54:46 AM\",Lunch Run,Run,,2352,7.71,63,FALSE,,activities/5046611300.fit.gz,,,2352,2344,7712.399902,6.099999905,3.290272951,61.1024971,61.10250092,19.5,42.5,18.60000038,0,,,,,,163.0299683,,,459,,,63,,,,,,7,,,,,0,74,0,,1,7872.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4737656676,\"Feb 5, 2021, 9:33:01 AM\",Morning Run,Run,,3438,10.46,74,FALSE,,activities/5057026728.fit.gz,,,3438,3278,10459.2002,6.400000095,3.190726042,81.48852539,81.58852386,19.5,42.5,20.20000076,-0.000956096,,,,,,157.9935455,,,638,,,74,,,,,,,,,,,,,0,,1,10656.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4750782594,\"Feb 7, 2021, 2:43:44 PM\",Afternoon Run,Run,,1373,4.99,42,FALSE,,activities/5070676662.fit.gz,,,1373,1373,4995.399902,4.599999905,3.638310194,27.87534904,20.87534904,19.5,36.79999924,6,0.140128925,,,,,,167.2294769,,,292,,,42,,,,,,,,,,,,,0,,1,5046.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4759641774,\"Feb 9, 2021, 12:23:59 PM\",Lunch Run,Run,,1597,5.03,31,FALSE,,activities/5079873249.fit.gz,,,1597,1583,5034.399902,4.900000095,3.180290699,28.69754982,28.99754906,19.5,40.90000153,16,-0.005959002,,,,,,156.9244995,,,298,,,31,,,,,,,,,,,,,0,,1,5093.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4765439029,\"Feb 10, 2021, 2:26:16 PM\",Afternoon Run,Run,,1561,5.02,31,FALSE,,activities/5085868236.fit.gz,,,1561,1557,5023.899902,4.400000095,3.226653814,24.8578949,24.65789413,19.5,38,17.70000076,0.003980971,,,,,,156.7068329,,,292,,,31,,,,,,,,,,,,,0,,1,5070.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4770899161,\"Feb 11, 2021, 9:40:47 AM\",Morning Run,Run,,2551,7.71,39,FALSE,,activities/5091529428.fit.gz,,,2551,2530,7716.899902,4.699999809,3.050158024,61.00584412,60.90584564,19.5,42.5,18.89999962,0.001295857,,,,,,151.876236,,,462,,,39,,,,,,,,,,,,,0,,1,7877.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4774120119,\"Feb 12, 2021, 8:21:56 AM\",Morning Run,Run,,3516,10.32,29,FALSE,,activities/5094860791.fit.gz,,,3516,3513,10328.59961,4.599999905,2.940108061,81.5072937,81.5072937,19.5,42.5,19.79999924,0,,,,,,144.8929138,,,612,,,29,,,,,,,,,,,,,0,,1,10523.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4787926957,\"Feb 14, 2021, 10:55:21 AM\",Morning Run,Run,,1700,5.68,43,FALSE,,activities/5109197547.fit.gz,,,1700,1696,5684.299805,5.099999905,3.351592064,28.82511711,28.52511597,19.5,36.79999924,7.300000191,0.005277695,,,,,,160.9887695,,,326,,,43,,,,,,,,,,,,,0,,1,5724,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4796777281,\"Feb 16, 2021, 2:58:29 PM\",Afternoon Run,Run,,3168,10,85,FALSE,,activities/5118376417.fit.gz,,,3168,3160,10003,4.599999905,3.165506363,81.5758667,78.87586975,19.5,42.59999847,18.89999962,0.026991902,,,,,,162.499054,,,596,,,85,,,,,,,,,,,,,0,,1,10204.90039,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4800636257,\"Feb 17, 2021, 9:41:54 AM\",Morning Run,Run,,2434,7.65,35,FALSE,,activities/5122358736.fit.gz,,,2434,2407,7658.200195,4.900000095,3.18163681,61.03914642,61.1391449,19.5,42.5,17.89999962,-0.00130579,,,,,,150.957901,,,450,,,35,,,,,,,,,,,,,0,,1,7822.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4813356754,\"Feb 19, 2021, 6:03:56 PM\",Evening Run,Run,,1369,4.97,41,FALSE,,activities/5135517175.fit.gz,,,1369,1363,4970,6.199999809,3.646368265,27.20114326,27.30114365,19.5,39.59999847,18,-0.002012072,,,,,,165.741745,,,293,,,41,,,,,,,,,,,,,0,,1,5022.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4818720862,\"Feb 20, 2021, 3:09:52 PM\",Mama lied and said it was a 5k,Run,Feb 100k yeet,4373,11.02,23,FALSE,,activities/5141086785.fit.gz,,,4373,4323,11022.7002,5.5,2.54978013,82.08472443,82.18470001,21,41.20000076,16.5,-0.000907219,,,,,,132.5668793,,,641,,,23,,,,,,5,,,,,0,78,0,,1,11136.09961,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4842619546,\"Feb 24, 2021, 4:32:27 PM\",200k this year knocked 2:30 mins off my 5k,Run,Sub 20min 5k by the end of the year? Yes please ,1346,5.03,47,FALSE,,activities/5165966235.fit.gz,,,1346,1343,5032.700195,5.199999809,3.747356653,28.17155647,20.77160072,19.5,36.79999924,7.099999905,0.14703837,,,,,,170.4324188,,,292,,,47,,,,,,8,,,,,0,57,0,,1,5085.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4847343918,\"Feb 25, 2021, 1:12:30 PM\",Afternoon Run,Run,,1417,5,46,FALSE,,activities/5170870445.fit.gz,,,1417,1414,5008.899902,4.900000095,3.542361975,28.78936195,21.48936272,19.5,36.79999924,6.800000191,0.145740584,,,,,,168.6514893,,,304,,,46,,,,,,,,,,,,,0,,1,5060.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4872366816,\"Mar 1, 2021, 4:24:13 PM\",Afternoon Run,Run,,1354,5,45,FALSE,,activities/5198017471.fit.gz,,,1354,1351,5008.700195,5.199999809,3.707401991,28.33622551,21.13622475,19.5,36.79999924,6.699999809,0.143749878,,,,,,168.9814911,,,290,,,45,,,,,,,,,,,,,0,,1,5061.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4884370330,\"Mar 3, 2021, 4:32:57 PM\",I swear it was 10k,Run,,2836,9.99,94,FALSE,,activities/5210481110.fit.gz,,,2836,2809,9992.200195,5.699999809,3.557209015,81.44786835,77.64790344,19.5,42.5,17.79999924,0.038029663,,,,,,169.2440186,,,593,,,94,,,,,,5,,,,,0,50,0,,1,10198.90039,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4894616645,\"Mar 5, 2021, 1:35:14 PM\",Afternoon Run,Run,,1854,5.01,41,FALSE,,activities/5221226898.fit.gz,,,1854,1764,5011.399902,5.300000191,2.840929747,32.11332321,31.41332436,13.5,24.70000076,9.699999809,0.013968153,,,,,,160.3514862,,,298,,,41,,,,,,,,,,,,,0,,1,5066.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4914341021,\"Mar 8, 2021, 12:18:26 PM\",Lunch Run,Run,,2080,4.98,26,FALSE,,activities/5242095353.fit.gz,,,2080,2000,4988.600098,4.900000095,2.494299889,25.77063942,19.77063942,19.5,36.79999924,12.89999962,0.120274223,,,,,,144.9199982,,,281,,,26,,,,,,,,,,,,,0,,1,5035.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4923832390,\"Mar 10, 2021, 12:30:06 PM\",Trails making me work 🥵,Run,,2548,8.28,69,FALSE,,activities/5252106543.fit.gz,,,2548,2526,8284.700195,5.199999809,3.279770374,51.141716,51.14170074,13.5,38.5,30,0,,,,,,164.3249664,,,515,,,69,,,,,,6,,,,,0,66,0,,1,8406.700195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4929314160,\"Mar 11, 2021, 3:07:33 PM\",Afternoon Run,Run,,1421,5,52,FALSE,,activities/5257893256.fit.gz,,,1421,1418,5005.5,5.400000095,3.529971838,25.81785774,18.71785736,19.5,36.79999924,11.10000038,0.141843975,,,,,,173.0905304,,,300,,,52,,,,,,,,,,,,,0,,1,5056.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4938123808,\"Mar 13, 2021, 9:40:35 AM\",Morning Run,Run,,3052,10.35,83,FALSE,,activities/5267188430.fit.gz,,,3052,3048,10351.90039,4.900000095,3.396292686,81.5331955,81.43319702,19.5,42.5,19.10000038,0.000966006,,,,,,163.1642609,,,638,,,83,,,,,,,,,,,,,0,,1,10550.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4951668214,\"Mar 15, 2021, 3:26:17 PM\",Afternoon Run,Run,,2098,7.02,64,FALSE,,activities/5281491983.fit.gz,,,2098,2089,7021.200195,7.699999809,3.361033916,58.18712234,60.78712082,19.5,42.5,17.60000038,-0.037030708,,,,,,165.9414063,,,429,,,64,,,,,,,,,,,,,0,,1,7180.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4963411963,\"Mar 17, 2021, 5:52:35 PM\",Afternoon Run,Run,,1412,5,53,FALSE,,activities/5293758342.fit.gz,,,1412,1412,5004.5,4.599999905,3.544263363,27.77837372,20.77837372,19.5,36.79999924,6.599999905,0.139874116,,,,,,171.0539398,,,302,,,53,,,,,,,,,,,,,0,,1,5055.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4976217656,\"Mar 19, 2021, 5:31:22 PM\",Afternoon Run,Run,,1850,5,19,FALSE,,activities/5307148879.fit.gz,,,1850,1564,5000.899902,5.199999809,3.197506428,28.42457581,28.32457733,27.39999962,36.20000076,5.400000095,0.00199964,,,,,,147.4078827,,,293,,,19,,,,,,,,,,,,,0,,1,5021.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'4984670704,\"Mar 21, 2021, 12:38:40 PM\",Lunch Run,Run,,3032,9.97,93,FALSE,,activities/5315984340.fit.gz,,,3032,3014,9978,5.199999809,3.31055069,69.33795929,70.03795624,13.5,25.10000038,26.29999924,-0.007015434,,,,,,167.1491089,,,629,,,93,,,,,,,,,,,,,0,,1,10117.2002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5001871400,\"Mar 24, 2021, 10:58:48 AM\",Morning Run,Run,,1654,5.66,47,FALSE,,activities/5333993102.fit.gz,,,1654,1654,5668.600098,4.900000095,3.427206755,28.72732925,28.62732887,19.5,36.79999924,6.5,0.001764104,,,,,,165.2956848,,,336,,,47,,,,,,,,,,,,,0,,1,5707,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5008752534,\"Mar 25, 2021, 3:32:35 PM\",Afternoon Run,Run,,3242,10.02,113,FALSE,,activities/5341220418.fit.gz,,,3242,3188,10028.59961,6.300000191,3.145734072,78.09612274,73.69612122,13.5,25.20000076,28.79999924,0.043874517,,,,,,170.8447418,,,648,,,113,,,,,,,,,,,,,0,,1,10196.2002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5017653869,\"Mar 27, 2021, 9:57:12 AM\",Morning Run,Run,,1594,5.62,49,FALSE,,activities/5350572102.fit.gz,,,1594,1590,5625.100098,4.800000191,3.537798643,28.73493576,28.73493576,19.5,36.79999924,6.599999905,0,,,,,,166.7715607,,,325,,,49,,,,,,,,,,,,,0,,1,5663.700195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5037102243,\"Mar 30, 2021, 12:55:38 PM\",Afternoon Run,Run,,1451,5,37,FALSE,,activities/5371004600.fit.gz,,,1451,1451,5005.100098,4.900000095,3.449414253,27.87680626,20.77680588,19.5,36.79999924,8.699999809,0.141855314,,,,,,160.1575623,,,301,,,37,,,,,,,,,,,,,0,,1,5057.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5051344905,\"Apr 1, 2021, 4:55:28 PM\",Afternoon Run,Run,,1652,5.68,43,FALSE,,activities/5385910698.fit.gz,,,1652,1652,5685.100098,4.699999809,3.441343784,28.74295425,28.34295464,19.5,36.79999924,6.099999905,0.007035936,,,,,,160.3444672,,,339,,,43,,,,,,,,,,,,,0,,1,5724.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5055513465,\"Apr 2, 2021, 1:16:05 PM\",Afternoon Run,Run,,2413,7.31,55,FALSE,,activities/5390284077.fit.gz,,,2413,2359,7311.399902,5.099999905,3.099364042,51.32363129,48.42362976,13.5,25.10000038,27.10000038,0.039664086,,,,,,159.0220795,,,484,,,55,,,,,,,,,,,,,0,,1,7420.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5063624438,\"Apr 3, 2021, 4:24:55 PM\",Afternoon Ride,Ride,,2775,11.49,9,FALSE,,activities/5398810814.fit.gz,,,2775,2250,11497,8.699999809,5.109777927,59.15607452,67.15607452,19.5,41.40000153,6,-0.069583371,,,,,,121.1538467,,56.38415909,240,,,9,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5072736270,\"Apr 4, 2021, 12:48:10 PM\",Father Ted,Hike,,5396,5.05,12,FALSE,,activities/5408390460.fit.gz,,,5396,5172,5058.700195,2.099999905,0.978093565,170.5157166,171.9160004,24.89999962,176.8999939,44.70000076,-0.027675094,,,,,,102.082283,,,412,,,12,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5074164607,\"Apr 5, 2021, 2:17:12 PM\",Afternoon Run,Run,,1398,5,53,FALSE,,activities/5409882984.fit.gz,,,1398,1394,5008,5.099999905,3.592539549,27.80196571,20.70196724,19.5,36.79999924,6.599999905,0.141773164,,,,,,173.3759003,,,301,,,53,,,,,,,,,,,,,0,,1,5059.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5079738320,\"Apr 6, 2021, 3:13:30 PM\",Afternoon Run,Run,,1409,4.99,51,FALSE,,activities/5415716276.fit.gz,,,1409,1405,4995.799805,4.699999809,3.555729628,25.66437149,18.86437225,19.5,36.79999924,9.5,0.136114329,,,,,,171.4321442,,,307,,,51,,,,,,,,,,,,,0,,1,5045.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5095613834,\"Apr 8, 2021, 3:18:39 PM\",Afternoon Run,Run,,2740,8.9,70,FALSE,,activities/5432269798.fit.gz,,,2740,2723,8906.200195,4.599999905,3.270730734,72.86840057,73.36840057,17.10000038,25.29999924,12.5,-0.005614066,,,,,,161.2819061,,,542,,,70,,,,,,,,,,,,,0,,1,9078.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5108947625,\"Apr 11, 2021, 1:19:54 PM\",Afternoon Run,Run,,2860,10.03,112,FALSE,,activities/5446234761.fit.gz,,,2860,2860,10034.7998,5.099999905,3.508671284,81.4693985,78.36940002,19.5,42.59999847,18.10000038,0.030892493,,,,,,173.5755768,,,618,,,112,,,,,,,,,,,,,0,,1,10236.90039,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5114813624,\"Apr 12, 2021, 4:23:36 PM\",Afternoon Run,Run,,1364,5,42,FALSE,,activities/5452427696.fit.gz,,,1364,1357,5006.600098,5.099999905,3.689461946,27.70903587,20.60903549,19.5,36.79999924,6.400000095,0.141812801,,,,,,166.2982941,,,299,,,42,,,,,,,,,,,,,0,,1,5058.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5125900776,\"Apr 14, 2021, 2:47:38 PM\",Afternoon Run,Run,,1439,5,36,FALSE,,activities/5463992208.fit.gz,,,1439,1416,5001.799805,4.5,3.53234458,25.69522095,18.79522133,19.5,36.79999924,7.800000191,0.137950331,,,,,,161.600708,,,301,,,36,,,,,,,,,,,,,0,,1,5051.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5136016144,\"Apr 16, 2021, 10:59:11 AM\",Lunch Run,Run,,707,2.35,12,FALSE,,activities/5474498054.fit.gz,,,707,697,2350.5,4.099999905,3.372309923,9.399380684,6.999380589,19.5,30.29999924,13.30000019,0.102105938,,,,,,152.4300079,,,142,,,12,,,,,,,,,,,0,,0,,1,2374.300049,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5136016355,\"Apr 16, 2021, 11:11:10 AM\",Lunch Run,Run,,2307,8,77,FALSE,,activities/5474498170.fit.gz,,,2307,2302,8005.600098,5.099999905,3.477671623,66.74192047,69.641922,19.60000038,42.5,26.20000076,-0.036224641,,,,,,169.6983032,,,492,,,77,,,,,,,,,,,,,0,,1,8182.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5156108897,\"Apr 19, 2021, 5:25:21 PM\",Evening Run,Run,,1437,5.04,40,FALSE,,activities/5495497514.fit.gz,,,1437,1437,5039.600098,4.800000191,3.50702858,29.57853127,29.27853012,19.5,41.59999847,18.29999924,0.005952853,,,,,,164.4444427,,,306,,,40,,,,,,,,,,,,,0,,1,5102.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5161987565,\"Apr 20, 2021, 4:34:48 PM\",Afternoon Run,Run,,652,2.51,21,FALSE,,activities/5501574552.fit.gz,,,652,652,2515.100098,6.099999905,3.857515335,6.184126854,6.084126949,21.5,28,1.899999976,0.003975985,,,,,,167.5357208,,,146,,,21,,,,,,,,,,,,,0,,1,2518.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5167509627,\"Apr 21, 2021, 3:55:03 PM\",Afternoon Run,Run,,3106,10.38,87,FALSE,,activities/5507301955.fit.gz,,,3106,3102,10380.5,5.199999809,3.346389532,81.53314972,81.2331543,19.5,42.5,17.89999962,0.002890034,,,,,,163.9854736,,,643,,,87,,,,,,,,,,,,,0,,1,10576.7998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5176486847,\"Apr 23, 2021, 8:28:41 AM\",Limerick Tour pt.1,Run,,1792,5.15,13,FALSE,,activities/5516599101.fit.gz,,,1792,1745,5153.799805,4.800000191,2.953467131,13.91299152,14.61299992,2.599999905,8.600000381,6.5,-0.013582211,,,,,,142.8877258,,,307,,,13,,,,,,,,,,,0,,0,,1,5169,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5190907333,\"Apr 25, 2021, 2:16:10 PM\",Afternoon Run,Run,,1533,4.99,24,FALSE,,activities/5531602376.fit.gz,,,1533,1520,4998.799805,4.5,3.28868413,25.71078491,19.41078377,19.5,36.79999924,7.5,0.126030251,,,,,,152.0540924,,,299,,,24,,,,,,,,,,,,,0,,1,5047.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5196213626,\"Apr 26, 2021, 1:43:12 PM\",Hot weather bad knees 🥵🥵,Run,,3154,10.02,68,FALSE,,activities/5537154432.fit.gz,,,3154,3150,10021.7998,4.300000191,3.1815238,81.52844238,79.82839966,19.5,42.5,18.79999924,0.01696302,,,,,,158.4246521,,,641,,,68,,,,,,,,,,,0,,0,,1,10222.2998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5213079353,\"Apr 29, 2021, 10:13:56 AM\",Lunch Run,Run,,1449,5.03,49,FALSE,,activities/5555000470.fit.gz,,,1449,1446,5033.899902,4.800000191,3.481258631,27.88322449,20.78322411,19.5,36.79999924,8.300000191,0.141043723,,,,,,170.6671295,,,301,,,49,,,,,,,,,,,,,0,,1,5084.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5222322943,\"May 1, 2021, 9:25:20 AM\",Morning Run,Run,,3286,11.01,67,FALSE,,activities/5564788708.fit.gz,,,3286,3286,11015.7998,5,3.352343321,82.10368347,82.10368347,21,41.20000076,15.5,0,,,,,,157.0088348,,,673,,,67,,,,,,,,,,,,,0,,1,11131.7002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5229582473,\"May 2, 2021, 11:37:41 AM\",Lunch Run,Run,,2292,7.67,64,FALSE,,activities/5572348427.fit.gz,,,2292,2289,7672.700195,4.699999809,3.351987839,61.04593277,61.14593124,19.5,42.5,18.29999924,-0.001303322,,,,,,162.3903046,,,473,,,64,,,,,,,,,,,,,0,,1,7837.200195,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5235391009,\"May 3, 2021, 11:44:57 AM\",Lunch Run,Run,,2576,8.67,74,FALSE,,activities/5578409745.fit.gz,,,2576,2573,8670.900391,5,3.369957209,66.38127136,66.58127594,19.5,42.5,16.79999924,-0.002306566,,,,,,164.1573792,,,541,,,74,,,,,,,,,,,,,0,,1,8806.599609,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5239304186,\"May 4, 2021, 8:11:02 AM\",Morning Run,Run,,1752,5.64,30,FALSE,,activities/5582471635.fit.gz,,,1752,1741,5643.799805,4.400000095,3.241700172,28.80533028,28.80533028,19.5,36.79999924,8.199999809,0,,,,,,153.3924103,,,339,,,30,,,,,,,,,,,,,0,,1,5682.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5250967424,\"May 6, 2021, 11:37:33 AM\",Lunch Run,Run,,3098,10.02,109,FALSE,,activities/5594560799.fit.gz,,,3098,3069,10023.5,4.900000095,3.266047478,81.55551147,78.05551147,19.5,42.5,18.10000038,0.034917943,,,,,,171.2809143,,,664,,,109,,,,,,,,,,,,,0,,1,10226.90039,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5269761125,\"May 9, 2021, 5:07:35 PM\",Evening Run,Run,,1523,5,38,FALSE,,activities/5614272364.fit.gz,,,1523,1511,5001.399902,4.5,3.309993267,28.0161953,21.0161953,19.5,36.79999924,6.599999905,0.139960811,,,,,,160.1331177,,,304,,,38,,,,,,,,,,,,,0,,1,5052.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5273999860,\"May 10, 2021, 4:31:10 PM\",Afternoon Run,Run,,1662,5.68,31,FALSE,,activities/5618745761.fit.gz,,,1662,1659,5685.200195,5.099999905,3.426883698,28.85607147,28.75607109,19.5,36.79999924,6.400000095,0.001758953,,,,,,153.4749603,,,336,,,31,,,,,,,,,,,,,0,,1,5725,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5284105291,\"May 12, 2021, 12:27:21 PM\",Afternoon Run,Run,,2981,9.99,82,FALSE,,activities/5629313557.fit.gz,,,2981,2969,9996.700195,4.599999905,3.367025852,81.54337311,77.74337769,19.5,42.5,20.20000076,0.038012546,,,,,,164.3793945,,,623,,,82,,,,,,,,,,,,,0,,1,10203.59961,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5297178303,\"May 14, 2021, 6:30:08 PM\",Final km sprint for a new pr ,Run,,2843,10,67,FALSE,,activities/5643005781.fit.gz,,,2843,2839,10002.2002,5.599999905,3.523141861,81.5411377,77.54109955,19.5,42.5,20.39999962,0.039991204,,,,,,160.711319,,,603,,,67,,,,,,,,,,,0,,0,,1,10209,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5323922507,\"May 19, 2021, 3:03:36 PM\",Afternoon Run,Run,,3078,10.01,75,FALSE,,activities/5670844770.fit.gz,,,3078,3065,10014.40039,4.400000095,3.267340899,78.90622711,73.40622711,21,41.20000076,14.5,0.054920916,,,,,,162.0991211,,,654,,,75,,,,,,,,,,,,,0,,1,10137.2002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5328922651,\"May 20, 2021, 1:53:01 PM\",I think I run faster when there’s fear of rain 🌧 ,Run,,1413,4.99,39,FALSE,,activities/5676020036.fit.gz,,,1413,1410,4998.700195,5.199999809,3.545177221,27.73801613,20.83799934,19.5,36.79999924,8.800000191,0.138035893,,,,,,164.0583191,,,295,,,39,,,,,,,,,,,0,,0,,1,5050.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5333050234,\"May 21, 2021, 9:19:10 AM\",Hit 100k a bit earlier this month 😮‍💨😮‍💨,Run,,3880,13,96,FALSE,,activities/5680311666.fit.gz,,,3880,3880,13000.7002,4.699999809,3.350695848,87.20733643,86.80729675,17.70000076,42.5,19,0.003076757,,,,,,161.0193634,,,810,,,96,,,,,,,,,,,0,,0,,1,13160.2002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5344173067,\"May 23, 2021, 11:05:48 AM\",Lunch Run,Run,,2208,7.7,56,FALSE,,activities/5691945631.fit.gz,,,2208,2205,7699.700195,5.599999905,3.491927385,61.05784225,61.15784454,19.5,42.5,20.20000076,-0.001298752,,,,,,161.6946259,,,472,,,56,,,,,,,,,,,,,0,,1,7863.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5356506883,\"May 25, 2021, 4:48:42 PM\",Afternoon Run,Run,,1375,5.01,39,FALSE,,activities/5704807798.fit.gz,,,1375,1372,5011.700195,5.800000191,3.652842522,27.84462357,20.74462318,19.5,36.79999924,8.300000191,0.141668499,,,,,,163.9312439,,,297,,,39,,,,,,,,,,,,,0,,1,5062.600098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5362914945,\"May 26, 2021, 6:36:14 PM\",Evening Run,Run,,1354,4.99,42,FALSE,,activities/5711466287.fit.gz,,,1354,1350,4995.899902,5.099999905,3.700666666,25.65813637,19.15813637,19.5,36.79999924,10.80000019,0.130106688,,,,,,166.719696,,,300,,,42,,,,,,,,,,,,,0,,1,5045.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5389988902,\"May 31, 2021, 4:36:59 PM\",Afternoon Run,Run,,1466,5.02,29,FALSE,,activities/5739700281.fit.gz,,,1466,1463,5019.899902,4.699999809,3.431237221,28.39826012,21.19825935,19.5,36.79999924,6.699999809,0.143429145,,,,,,156.4480133,,,300,,,29,,,,,,,,,,,,,0,,1,5072,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5394269776,\"Jun 1, 2021, 11:08:26 AM\",Half Marathon to start the month off,Run,,6971,22.34,193,FALSE,,activities/5744159736.fit.gz,,,6971,6958,22340.69922,4.800000191,3.210793257,164.0983734,164.2980042,17.70000076,48.59999847,18,-0.000895227,,,,,,163.2337036,,,1461,,,193,,,,,,8,,,,,0,299,0,,1,22637.59961,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5407787729,\"Jun 3, 2021, 5:11:48 PM\",My time might suggest I’m recovered but my knees would say otherwise ,Run,,1434,5.02,40,FALSE,,activities/5758238750.fit.gz,,,1434,1434,5023.899902,6,3.503417015,25.57797623,25.27799988,19.5,38.59999847,20,0.005971456,,,,,,164.0917358,,,303,,,40,,,,,,5,,,,,0,25,0,,1,5072.899902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5422351029,\"Jun 6, 2021, 8:19:29 AM\",Morning Run,Run,,1462,5,32,FALSE,,activities/5773508509.fit.gz,,,1462,1455,5002.399902,4.699999809,3.438075542,25.75090599,18.95090675,19.5,36.79999924,12,0.135934755,,,,,,158.3661346,,,297,,,32,,,,,,,,,,,,,0,,1,5052.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5429453430,\"Jun 7, 2021, 1:51:21 PM\",Afternoon Run,Run,,2946,10.01,96,FALSE,,activities/5780950243.fit.gz,,,2946,2943,10010.09961,5.400000095,3.401325226,81.4828949,78.18289185,19.5,42.5,18.5,0.032966703,,,,,,168.1787262,,,639,,,96,,,,,,,,,,,,,0,,1,10213.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5447086069,\"Jun 10, 2021, 3:20:12 PM\",Afternoon Run,Run,,3074,10.01,78,FALSE,,activities/5799315765.fit.gz,,,3074,3065,10011.09961,4.800000191,3.2662642,81.52753448,77.12753296,19.5,42.5,19.60000038,0.043951213,,,,,,161.6751709,,,641,,,78,,,,,,,,,,,,,0,,1,10216.7002,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5459201945,\"Jun 12, 2021, 6:18:05 PM\",Evening Run,Run,,2700,5.47,8,FALSE,,activities/5811941845.fit.gz,,,2700,1800,5475.5,4,3.041944504,28.73799515,25.33799553,19.5,36.79999924,6.400000095,0.062094785,,,,,,137.2841187,,,305,,,8,,,,,,,,,,,,,0,,1,5505.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5465516904,\"Jun 13, 2021, 11:40:56 AM\",Lunch Run,Run,,3092,10.02,67,FALSE,,activities/5818528003.fit.gz,,,3092,3081,10026.59961,5.900000095,3.254333019,81.57922363,78.67922211,19.5,42.59999847,21.39999962,0.028923064,,,,,,157.6205292,,,625,,,67,,,,,,,,,,,,,0,,1,10228.09961,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5472899695,\"Jun 15, 2021, 9:53:44 AM\",Morning Run,Run,,1450,5.01,30,FALSE,,activities/5826210242.fit.gz,,,1450,1433,5016.5,5.099999905,3.500697851,25.71894836,19.11894798,19.5,36.79999924,7.900000095,0.131565839,,,,,,158.0133209,,,303,,,30,,,,,,,,,,,,,0,,1,5066.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5480181184,\"Jun 16, 2021, 2:42:48 PM\",Afternoon Run,Run,,2337,7.7,66,FALSE,,activities/5833772372.fit.gz,,,2337,2332,7700.5,4.5,3.302101135,61.04289246,60.94289398,19.5,42.5,18.39999962,0.001298617,,,,,,164.9484711,,,489,,,66,,,,,,,,,,,,,0,,1,7864,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5494348980,\"Jun 19, 2021, 8:45:48 AM\",Morning Run,Run,,1530,5,30,FALSE,,activities/5848540722.fit.gz,,,1530,1527,5000.799805,4.699999809,3.274918079,25.72794151,19.22794151,19.5,36.79999924,11.10000038,0.129979208,,,,,,154.7667542,,,301,,,30,,,,,,,,,,,,,0,,1,5050,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5500179727,\"Jun 20, 2021, 8:49:35 AM\",Morning Run,Run,,7088,21.48,153,FALSE,,activities/5854632965.fit.gz,,,7088,7085,21484.69922,4.199999809,3.032420635,150.152832,150.8528442,17.70000076,48.59999847,16,-0.003258133,,,,,,156.7823181,,,1412,,,153,,,,,,,,,,,,,0,,1,21744.19922,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5506438882,\"Jun 21, 2021, 1:28:00 PM\",Lil recovery run,Run,,2636,6.12,23,FALSE,,activities/5861181957.fit.gz,,,2636,2529,6127.299805,6.400000095,2.422815323,28.85847473,29.15850067,19.5,36.79999924,11.10000038,-0.004896121,,,,,,144.0758972,,,387,,,23,,,,,,,,,,,0,,0,,1,6164.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5527027940,\"Jun 25, 2021, 10:27:17 AM\",Lunch Run,Run,,3155,9.99,75,FALSE,,activities/5882599549.fit.gz,,,3155,3136,9990.900391,4.599999905,3.185873747,81.54492188,77.84492493,19.5,42.59999847,20,0.037033699,,,,,,160.4476318,,,637,,,75,,,,,,,,,,,,,0,,1,10193.90039,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5534195253,\"Jun 26, 2021, 2:19:59 PM\",Afternoon Run,Run,,2039,5.13,18,FALSE,,activities/5890080571.fit.gz,,,2039,1193,5130.100098,9.199999809,4.300167561,27.96682358,20.56682396,19.70000076,36.79999924,8.800000191,0.144246697,,,,,,150.5141144,,,223,,,18,,,,,,,,,,,,,0,,1,5182.299805,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5542766978,\"Jun 28, 2021, 11:37:17 AM\",Lunch Run,Run,,7133,21.48,238,FALSE,,activities/5899027132.fit.gz,,,7133,7125,21481.40039,4.599999905,3.014933348,150.039978,150.1399841,17.70000076,48.59999847,16.10000038,-0.000465519,,,,,,169.3539734,,,1564,,,238,,,,,,,,,,,,,0,,1,21740,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5567995392,\"Jul 3, 2021, 11:43:47 AM\",Lunch Run,Run,,2380,7.73,55,FALSE,,activities/5925296685.fit.gz,,,2380,2374,7736.799805,5.300000191,3.258972168,61.02317429,60.923172,19.5,42.5,18.79999924,0.001292524,,,,,,159.0436096,,,476,,,55,,,,,,,,,,,,,0,,1,7902.100098,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5567995472,\"Jul 2, 2021, 5:02:09 PM\",Evening Run,Run,,1518,5,32,FALSE,,activities/5925296739.fit.gz,,,1518,1518,5008.899902,4.900000095,3.299670696,25.78105927,19.08106041,19.5,36.79999924,9.5,0.133761898,,,,,,156.617981,,,299,,,32,,,,,,,,,,,,,0,,1,5059.399902,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5586035135,\"Jul 6, 2021, 4:26:21 PM\",Afternoon Run,Run,,1484,5.01,26,FALSE,,activities/5944147947.fit.gz,,,1484,1480,5010.899902,4.800000191,3.385743141,25.64742661,19.14742661,19.5,36.79999924,11.80000019,0.129717216,,,,,,154.4303131,,,296,,,26,,,,,,,,,,,,,0,,1,5060.5,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5593664281,\"Jul 8, 2021, 8:43:07 AM\",Morning Run,Run,,3054,9.97,74,FALSE,,activities/5952106858.fit.gz,,,3054,3050,9970.299805,5.699999809,3.268950701,81.5503006,77.0503006,19.5,42.59999847,34.59999847,0.045134049,,,,,,161.3969727,,,624,,,74,,,,,,,,,,,,,0,,1,10174.7998,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5596299507,\"Jul 8, 2021, 5:36:21 PM\",This is now a tri account ,Swim,Very very bad two years ago I’d do a kilometre in 15 minutes. What happened?,2890,\"1,025\",9,FALSE,,activities/5954848754.fit.gz,,,2890,2889,1025,5,0.354794055,0,,,,0,0,,,,,,106.6008987,,,249,,,9,,,,,,,,,,,0,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,\\n',\n",
        "'5611935848,\"Jul 11, 2021, 12:26:05 PM\",Afternoon Run,Run,,3176,10,85,FALSE,,activities/5971171490.fit.gz,,,3176,3172,10006.90039,4.699999809,3.154760361,81.54406738,77.54406738,19.5,42.5,19.79999924,0.039972421,,,,,,163.1661987,,,670,,,85,,,,,,,,,,,,,0,,1,10211.5,,,,,,,,,,,,,,,,,,,,,,,,,,,3.150787115\\n',\n",
        "'5620026819,\"Jul 12, 2021, 1:25:52 PM\",Afternoon Ride,Ride,,5223,23.79,19,FALSE,,activities/5979629834.fit.gz,,,5223,4216,23795.80078,11.39999962,5.644165039,156.8526764,156.6526794,17.70000076,48.59999847,17,0.000840484,,,,,,123.4677734,,75.54042816,501,,,19,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.555963993\\n',\n",
        "'5622120152,\"Jul 13, 2021, 11:39:15 AM\",Lunch Run,Run,,1572,4.99,22,FALSE,,activities/5981805834.fit.gz,,,1572,1560,4995.899902,4.599999905,3.202500105,27.7451725,20.84517097,19.5,36.79999924,8,0.13811326,,,,,,150.0952454,,,299,,,22,,,,,,,,,,,,,0,,1,5047,,,,,,,,,,,,,,,,,,,,,,,,,,,3.178053379\\n',\n",
        "'5630939643,\"Jul 15, 2021, 10:00:36 AM\",Lunch Run,Run,,3346,10.03,53,FALSE,,activities/5990979812.fit.gz,,,3346,3338,10034.7998,4.5,3.006231308,81.56835175,78.66835022,19.5,42.5,19.60000038,0.028899429,,,,,,151.1549072,,,644,,,53,,,,,,,,,,,,,0,,1,10234.59961,,,,,,,,,,,,,,,,,,,,,,,,,,,2.999043703\\n',\n",
        "'5633000779,\"Jul 15, 2021, 5:38:30 PM\",Evening Swim,Swim,,3003,\"1,025\",9,FALSE,,activities/5993122228.fit.gz,,,3003,3002,1025,5,0.341439039,0,,,,0,0,,,,,,101.3007965,,,266,,,9,,,,,,,,,,,,,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.341325343\\n',\n",
        "'5647120006,\"Jul 17, 2021, 7:19:33 AM\",Morning Run,Run,,1620,5.02,17,FALSE,,activities/6007882573.fit.gz,,,1620,1617,5026.299805,4.400000095,3.108410597,25.67543793,19.07543755,19.5,36.79999924,8.300000191,0.131309316,,,,,,146.0454559,,,298,,,17,,,,,,,,,,,,,0,,1,5075.799805,,,,,,,,,,,,,,,,,,,,,,,,,,,3.102654219\\n',\n",
        "'5647120551,\"Jul 18, 2021, 7:59:34 AM\",Truly almost died ,Ride,,15526,87.73,253,FALSE,,activities/6007882572.fit.gz,,,15526,13096,87734.39844,13.80000019,6.699327946,722.2398071,722.2399902,1.5,294.1000061,50,0,,,,,,154.193161,,99.80258179,2162,,,253,,,,,,8,,,,,0,563,0,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.650804996\\n'] \n",
        "\n",
        "with open(\"data.txt\" , 'w') as f:\n",
        "    f.writelines(data)\n",
        "\n",
        "f.close()\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "LxjXFfWvOuNs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"data.txt\")"
      ],
      "metadata": {
        "id": "9MPzwvsiRaOq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "li0EmtZ4RhNH",
        "outputId": "c871d488-e664-4058-e1e8-e5547bbe733c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method NDFrame.head of      Activity ID              Activity Date       Activity Name Activity Type  \\\n",
            "0     2987876185    Jan 4, 2020, 2:57:37 PM       Afternoon Run           Run   \n",
            "1     2987899333    Jan 6, 2020, 4:07:35 PM       Afternoon Run           Run   \n",
            "2     3003885525  Jan 12, 2020, 10:06:24 AM         Morning Run           Run   \n",
            "3     3005534889   Jan 12, 2020, 2:26:39 PM  Afternoon Activity       Workout   \n",
            "4     3066339878   Jan 23, 2020, 5:02:40 PM      Afternoon Ride          Ride   \n",
            "..           ...                        ...                 ...           ...   \n",
            "138   5622120152  Jul 13, 2021, 11:39:15 AM           Lunch Run           Run   \n",
            "139   5630939643  Jul 15, 2021, 10:00:36 AM           Lunch Run           Run   \n",
            "140   5633000779   Jul 15, 2021, 5:38:30 PM        Evening Swim          Swim   \n",
            "141   5647120006   Jul 17, 2021, 7:19:33 AM         Morning Run           Run   \n",
            "142   5647120551   Jul 18, 2021, 7:59:34 AM  Truly almost died           Ride   \n",
            "\n",
            "    Activity Description  Elapsed Time Distance  Relative Effort  Commute  \\\n",
            "0                    NaN          1509        5              NaN    False   \n",
            "1                    NaN           721      2.5              NaN    False   \n",
            "2                    NaN          1364        5              NaN    False   \n",
            "3                    NaN          5360     4.29              NaN    False   \n",
            "4                    NaN          1807     0.37              NaN    False   \n",
            "..                   ...           ...      ...              ...      ...   \n",
            "138                  NaN          1572     4.99             22.0    False   \n",
            "139                  NaN          3346    10.03             53.0    False   \n",
            "140                  NaN          3003    1,025              9.0    False   \n",
            "141                  NaN          1620     5.02             17.0    False   \n",
            "142                  NaN         15526    87.73            253.0    False   \n",
            "\n",
            "     Activity Gear  ... Precipitation Type  Cloud Cover  Weather Visibility  \\\n",
            "0              NaN  ...                NaN          NaN                 NaN   \n",
            "1              NaN  ...                NaN          NaN                 NaN   \n",
            "2              NaN  ...                NaN          NaN                 NaN   \n",
            "3              NaN  ...                NaN          NaN                 NaN   \n",
            "4              NaN  ...                NaN          NaN                 NaN   \n",
            "..             ...  ...                ...          ...                 ...   \n",
            "138            NaN  ...                NaN          NaN                 NaN   \n",
            "139            NaN  ...                NaN          NaN                 NaN   \n",
            "140            NaN  ...                NaN          NaN                 NaN   \n",
            "141            NaN  ...                NaN          NaN                 NaN   \n",
            "142            NaN  ...                NaN          NaN                 NaN   \n",
            "\n",
            "     UV Index  Weather Ozone  Jump Count  Total Grit  Avg Flow  Flagged  \\\n",
            "0         NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "1         NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "2         NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "3         NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "4         NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "..        ...            ...         ...         ...       ...      ...   \n",
            "138       NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "139       NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "140       NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "141       NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "142       NaN            NaN         NaN         NaN       NaN      NaN   \n",
            "\n",
            "     Avg Elapsed Speed  \n",
            "0                  NaN  \n",
            "1                  NaN  \n",
            "2                  NaN  \n",
            "3                  NaN  \n",
            "4                  NaN  \n",
            "..                 ...  \n",
            "138           3.178053  \n",
            "139           2.999044  \n",
            "140           0.341325  \n",
            "141           3.102654  \n",
            "142           5.650805  \n",
            "\n",
            "[143 rows x 79 columns]>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swaH7DB9WoSu",
        "outputId": "71002751-2a00-44cf-e946-78569eb4f231"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Activity ID', 'Activity Date', 'Activity Name', 'Activity Type',\n",
              "       'Activity Description', 'Elapsed Time', 'Distance', 'Relative Effort',\n",
              "       'Commute', 'Activity Gear', 'Filename', 'Athlete Weight', 'Bike Weight',\n",
              "       'Elapsed Time.1', 'Moving Time', 'Distance.1', 'Max Speed',\n",
              "       'Average Speed', 'Elevation Gain', 'Elevation Loss', 'Elevation Low',\n",
              "       'Elevation High', 'Max Grade', 'Average Grade',\n",
              "       'Average Positive Grade', 'Average Negative Grade', 'Max Cadence',\n",
              "       'Average Cadence', 'Max Heart Rate', 'Average Heart Rate', 'Max Watts',\n",
              "       'Average Watts', 'Calories', 'Max Temperature', 'Average Temperature',\n",
              "       'Relative Effort.1', 'Total Work', 'Number of Runs', 'Uphill Time',\n",
              "       'Downhill Time', 'Other Time', 'Perceived Exertion', 'Type',\n",
              "       'Start Time', 'Weighted Average Power', 'Power Count',\n",
              "       'Prefer Perceived Exertion', 'Perceived Relative Effort', 'Commute.1',\n",
              "       'Total Weight Lifted', 'From Upload', 'Grade Adjusted Distance',\n",
              "       'Weather Observation Time', 'Weather Condition', 'Weather Temperature',\n",
              "       'Apparent Temperature', 'Dewpoint', 'Humidity', 'Weather Pressure',\n",
              "       'Wind Speed', 'Wind Gust', 'Wind Bearing', 'Precipitation Intensity',\n",
              "       'Sunrise Time', 'Sunset Time', 'Moon Phase', 'Bike', 'Gear',\n",
              "       'Precipitation Probability', 'Precipitation Type', 'Cloud Cover',\n",
              "       'Weather Visibility', 'UV Index', 'Weather Ozone', 'Jump Count',\n",
              "       'Total Grit', 'Avg Flow', 'Flagged', 'Avg Elapsed Speed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try making some graphs which show Distance on the x-axis, Time elapsed on the y-axis and the plots are seperate for each month."
      ],
      "metadata": {
        "id": "L1mXScCCWeUf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# first lets keep only the columns we would need ->\n",
        "# Activity Date, Activity Type,Elapsed Time,Distance\n",
        "\n",
        "df_cut = df[['Activity Date' , 'Activity Type' , 'Elapsed Time' , 'Distance']]  # why the double brackets\n",
        "df_cut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mlc7guRWXMT1",
        "outputId": "596fc062-9b9c-447b-d459-efd4140d0995"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                 Activity Date Activity Type  Elapsed Time Distance\n",
              "0      Jan 4, 2020, 2:57:37 PM           Run          1509        5\n",
              "1      Jan 6, 2020, 4:07:35 PM           Run           721      2.5\n",
              "2    Jan 12, 2020, 10:06:24 AM           Run          1364        5\n",
              "3     Jan 12, 2020, 2:26:39 PM       Workout          5360     4.29\n",
              "4     Jan 23, 2020, 5:02:40 PM          Ride          1807     0.37\n",
              "..                         ...           ...           ...      ...\n",
              "138  Jul 13, 2021, 11:39:15 AM           Run          1572     4.99\n",
              "139  Jul 15, 2021, 10:00:36 AM           Run          3346    10.03\n",
              "140   Jul 15, 2021, 5:38:30 PM          Swim          3003    1,025\n",
              "141   Jul 17, 2021, 7:19:33 AM           Run          1620     5.02\n",
              "142   Jul 18, 2021, 7:59:34 AM          Ride         15526    87.73\n",
              "\n",
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              "      <td>Jan 4, 2020, 2:57:37 PM</td>\n",
              "      <td>Run</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jan 6, 2020, 4:07:35 PM</td>\n",
              "      <td>Run</td>\n",
              "      <td>721</td>\n",
              "      <td>2.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jan 12, 2020, 10:06:24 AM</td>\n",
              "      <td>Run</td>\n",
              "      <td>1364</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jan 12, 2020, 2:26:39 PM</td>\n",
              "      <td>Workout</td>\n",
              "      <td>5360</td>\n",
              "      <td>4.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jan 23, 2020, 5:02:40 PM</td>\n",
              "      <td>Ride</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Jul 13, 2021, 11:39:15 AM</td>\n",
              "      <td>Run</td>\n",
              "      <td>1572</td>\n",
              "      <td>4.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Jul 15, 2021, 10:00:36 AM</td>\n",
              "      <td>Run</td>\n",
              "      <td>3346</td>\n",
              "      <td>10.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Jul 15, 2021, 5:38:30 PM</td>\n",
              "      <td>Swim</td>\n",
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              "      <td>Jul 17, 2021, 7:19:33 AM</td>\n",
              "      <td>Run</td>\n",
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              "      <th>142</th>\n",
              "      <td>Jul 18, 2021, 7:59:34 AM</td>\n",
              "      <td>Ride</td>\n",
              "      <td>15526</td>\n",
              "      <td>87.73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>143 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets keep only those activities which were run\n",
        "df_new = df_cut[df['Activity Type'] == 'Run']\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3Y4jERn-Xj0g",
        "outputId": "6ae5bd9b-ea4c-4d02-b319-6f0b158d7c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Activity Date Activity Type  Elapsed Time Distance\n",
              "0      Jan 4, 2020, 2:57:37 PM           Run          1509        5\n",
              "1      Jan 6, 2020, 4:07:35 PM           Run           721      2.5\n",
              "2    Jan 12, 2020, 10:06:24 AM           Run          1364        5\n",
              "5      Feb 2, 2020, 6:01:03 PM           Run          1513     4.87\n",
              "7    Apr 14, 2020, 12:49:59 PM           Run          1449        5\n",
              "..                         ...           ...           ...      ...\n",
              "134    Jul 8, 2021, 8:43:07 AM           Run          3054     9.97\n",
              "136  Jul 11, 2021, 12:26:05 PM           Run          3176       10\n",
              "138  Jul 13, 2021, 11:39:15 AM           Run          1572     4.99\n",
              "139  Jul 15, 2021, 10:00:36 AM           Run          3346    10.03\n",
              "141   Jul 17, 2021, 7:19:33 AM           Run          1620     5.02\n",
              "\n",
              "[127 rows x 4 columns]"
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              "      <td>Jan 6, 2020, 4:07:35 PM</td>\n",
              "      <td>Run</td>\n",
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              "      <td>3054</td>\n",
              "      <td>9.97</td>\n",
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              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Jul 11, 2021, 12:26:05 PM</td>\n",
              "      <td>Run</td>\n",
              "      <td>3176</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Jul 13, 2021, 11:39:15 AM</td>\n",
              "      <td>Run</td>\n",
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              "      <th>141</th>\n",
              "      <td>Jul 17, 2021, 7:19:33 AM</td>\n",
              "      <td>Run</td>\n",
              "      <td>1620</td>\n",
              "      <td>5.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127 rows × 4 columns</p>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets drop the Activity Type now\n",
        "df_new = df_new.drop(columns = \"Activity Type\")\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PCjB-c3SsZnh",
        "outputId": "ad94e250-e889-4474-f8bb-28bf829027d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Activity Date  Elapsed Time Distance\n",
              "0      Jan 4, 2020, 2:57:37 PM          1509        5\n",
              "1      Jan 6, 2020, 4:07:35 PM           721      2.5\n",
              "2    Jan 12, 2020, 10:06:24 AM          1364        5\n",
              "5      Feb 2, 2020, 6:01:03 PM          1513     4.87\n",
              "7    Apr 14, 2020, 12:49:59 PM          1449        5\n",
              "..                         ...           ...      ...\n",
              "134    Jul 8, 2021, 8:43:07 AM          3054     9.97\n",
              "136  Jul 11, 2021, 12:26:05 PM          3176       10\n",
              "138  Jul 13, 2021, 11:39:15 AM          1572     4.99\n",
              "139  Jul 15, 2021, 10:00:36 AM          3346    10.03\n",
              "141   Jul 17, 2021, 7:19:33 AM          1620     5.02\n",
              "\n",
              "[127 rows x 3 columns]"
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              "      <th>Activity Date</th>\n",
              "      <th>Elapsed Time</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jan 4, 2020, 2:57:37 PM</td>\n",
              "      <td>1509</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jan 6, 2020, 4:07:35 PM</td>\n",
              "      <td>721</td>\n",
              "      <td>2.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jan 12, 2020, 10:06:24 AM</td>\n",
              "      <td>1364</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Feb 2, 2020, 6:01:03 PM</td>\n",
              "      <td>1513</td>\n",
              "      <td>4.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Apr 14, 2020, 12:49:59 PM</td>\n",
              "      <td>1449</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Jul 8, 2021, 8:43:07 AM</td>\n",
              "      <td>3054</td>\n",
              "      <td>9.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Jul 11, 2021, 12:26:05 PM</td>\n",
              "      <td>3176</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Jul 13, 2021, 11:39:15 AM</td>\n",
              "      <td>1572</td>\n",
              "      <td>4.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Jul 15, 2021, 10:00:36 AM</td>\n",
              "      <td>3346</td>\n",
              "      <td>10.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Jul 17, 2021, 7:19:33 AM</td>\n",
              "      <td>1620</td>\n",
              "      <td>5.02</td>\n",
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              "<p>127 rows × 3 columns</p>\n",
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          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets try extracting the month from Activity Date\n",
        "df_new[\"Activity Date\"].str.split(' ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XYN54U1tH1J",
        "outputId": "42a9c8ee-be51-4cea-9e69-38137f11bcd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        [Jan, 4,, 2020,, 2:57:37, PM]\n",
              "1        [Jan, 6,, 2020,, 4:07:35, PM]\n",
              "2      [Jan, 12,, 2020,, 10:06:24, AM]\n",
              "5        [Feb, 2,, 2020,, 6:01:03, PM]\n",
              "7      [Apr, 14,, 2020,, 12:49:59, PM]\n",
              "                    ...               \n",
              "134      [Jul, 8,, 2021,, 8:43:07, AM]\n",
              "136    [Jul, 11,, 2021,, 12:26:05, PM]\n",
              "138    [Jul, 13,, 2021,, 11:39:15, AM]\n",
              "139    [Jul, 15,, 2021,, 10:00:36, AM]\n",
              "141     [Jul, 17,, 2021,, 7:19:33, AM]\n",
              "Name: Activity Date, Length: 127, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new[[\"Month\" , \"Rest\"]] = df_new[\"Activity Date\"].str.split(' ' , expand = True , n=1)\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
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        "id": "oJinluGNtcEA",
        "outputId": "c7845623-5654-4ea9-c804-d85f5f97b23e"
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      "execution_count": null,
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              "                 Activity Date  Elapsed Time Distance Month  \\\n",
              "0      Jan 4, 2020, 2:57:37 PM          1509        5   Jan   \n",
              "1      Jan 6, 2020, 4:07:35 PM           721      2.5   Jan   \n",
              "2    Jan 12, 2020, 10:06:24 AM          1364        5   Jan   \n",
              "5      Feb 2, 2020, 6:01:03 PM          1513     4.87   Feb   \n",
              "7    Apr 14, 2020, 12:49:59 PM          1449        5   Apr   \n",
              "..                         ...           ...      ...   ...   \n",
              "134    Jul 8, 2021, 8:43:07 AM          3054     9.97   Jul   \n",
              "136  Jul 11, 2021, 12:26:05 PM          3176       10   Jul   \n",
              "138  Jul 13, 2021, 11:39:15 AM          1572     4.99   Jul   \n",
              "139  Jul 15, 2021, 10:00:36 AM          3346    10.03   Jul   \n",
              "141   Jul 17, 2021, 7:19:33 AM          1620     5.02   Jul   \n",
              "\n",
              "                      Rest  \n",
              "0      4, 2020, 2:57:37 PM  \n",
              "1      6, 2020, 4:07:35 PM  \n",
              "2    12, 2020, 10:06:24 AM  \n",
              "5      2, 2020, 6:01:03 PM  \n",
              "7    14, 2020, 12:49:59 PM  \n",
              "..                     ...  \n",
              "134    8, 2021, 8:43:07 AM  \n",
              "136  11, 2021, 12:26:05 PM  \n",
              "138  13, 2021, 11:39:15 AM  \n",
              "139  15, 2021, 10:00:36 AM  \n",
              "141   17, 2021, 7:19:33 AM  \n",
              "\n",
              "[127 rows x 5 columns]"
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              "      <td>Jan 6, 2020, 4:07:35 PM</td>\n",
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              "      <td>15, 2021, 10:00:36 AM</td>\n",
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              "      <td>Jul</td>\n",
              "      <td>17, 2021, 7:19:33 AM</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Let's drop Rest and Activity Date\n",
        "df_new.drop(columns = [\"Activity Date\" , \"Rest\"] , inplace = True)\n",
        "df_new"
      ],
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        "colab": {
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      },
      "execution_count": null,
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        {
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              "     Elapsed Time Distance Month\n",
              "0            1509        5   Jan\n",
              "1             721      2.5   Jan\n",
              "2            1364        5   Jan\n",
              "5            1513     4.87   Feb\n",
              "7            1449        5   Apr\n",
              "..            ...      ...   ...\n",
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              "136          3176       10   Jul\n",
              "138          1572     4.99   Jul\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Elapsed Time</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1509</td>\n",
              "      <td>5</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>721</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1364</td>\n",
              "      <td>5</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1513</td>\n",
              "      <td>4.87</td>\n",
              "      <td>Feb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1449</td>\n",
              "      <td>5</td>\n",
              "      <td>Apr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>3054</td>\n",
              "      <td>9.97</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>3176</td>\n",
              "      <td>10</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>1572</td>\n",
              "      <td>4.99</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>3346</td>\n",
              "      <td>10.03</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1620</td>\n",
              "      <td>5.02</td>\n",
              "      <td>Jul</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daa9881e-3a36-457e-abc8-49d7a0797604')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-daa9881e-3a36-457e-abc8-49d7a0797604');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# there might be one potential issue while convertingb from csv-> dtypes\n",
        "df_new.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96FwGKMBy7QD",
        "outputId": "14219ab6-1b81-4ebb-c334-c03871ab3968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elapsed Time     int64\n",
              "Distance        object\n",
              "Month           object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets change the dtype\n",
        "df_new['Distance'] = df_new['Distance'].astype(np.float64)\n",
        "print(df_new.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Urp63X_ZzQb7",
        "outputId": "4e3c01b7-8f3d-481f-9a9d-85d9da09d6d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elapsed Time      int64\n",
            "Distance        float64\n",
            "Month            object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We want the information to be grouped\n",
        "df_grouped = df_new.groupby('Month')\n",
        "df_grouped.get_group('Jan')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "3G0xLklCuScc",
        "outputId": "b24b8032-ceb4-4971-932a-604e06f86b86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Elapsed Time  Distance Month\n",
              "0           1509      5.00   Jan\n",
              "1            721      2.50   Jan\n",
              "2           1364      5.00   Jan\n",
              "35          1794      5.75   Jan\n",
              "36          2296      6.85   Jan\n",
              "37          3049     10.00   Jan\n",
              "38          1711      5.63   Jan\n",
              "39          1745      6.02   Jan\n",
              "40          2497      5.01   Jan\n",
              "41          1734      5.60   Jan\n",
              "42          3390     10.36   Jan\n",
              "43          3164     10.01   Jan\n",
              "44          1461      5.01   Jan\n",
              "45          2473      7.70   Jan\n",
              "46          1768      5.66   Jan\n",
              "47          1646      5.64   Jan\n",
              "48          3842     11.41   Jan\n",
              "49          1942      5.70   Jan\n",
              "50          1673      5.64   Jan"
            ],
            "text/html": [
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              "      <div>\n",
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              "      <td>Jan</td>\n",
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              "      <th>41</th>\n",
              "      <td>1734</td>\n",
              "      <td>5.60</td>\n",
              "      <td>Jan</td>\n",
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              "      <th>42</th>\n",
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              "      <td>Jan</td>\n",
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              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>3164</td>\n",
              "      <td>10.01</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1461</td>\n",
              "      <td>5.01</td>\n",
              "      <td>Jan</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2473</td>\n",
              "      <td>7.70</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1768</td>\n",
              "      <td>5.66</td>\n",
              "      <td>Jan</td>\n",
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              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1646</td>\n",
              "      <td>5.64</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>3842</td>\n",
              "      <td>11.41</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1942</td>\n",
              "      <td>5.70</td>\n",
              "      <td>Jan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1673</td>\n",
              "      <td>5.64</td>\n",
              "      <td>Jan</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aaf93e97-b2f4-47e8-8251-fef5459d6164 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aaf93e97-b2f4-47e8-8251-fef5459d6164');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot it \n",
        "# We had Jan, Feb, March, April , May, June, July\n",
        "months = [\"January\" , \"February\" , \"March\" , \"April\" , \"May\" , \"June\" , \"July\"]\n",
        "months_in_df = df_new['Month'].unique()\n",
        "\n",
        "\n",
        "plt.figure(figsize = (30,30))\n",
        "for i in range(len(months)):\n",
        "    plt.subplot(2,4,i+1)\n",
        "    data = df_grouped.get_group(months_in_df[i])\n",
        "    plt.xlim(2,11)\n",
        "    plt.ylim(500 , 5000)\n",
        "    plt.scatter(x = data['Distance'], y = data[\"Elapsed Time\"]) \n",
        "    plt.title(\"Month of {}\".format(months[i]))\n",
        "    plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D4G7C-sKu_l8",
        "outputId": "3414d5fc-1ec9-44ec-8606-d7c717625460"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x2160 with 7 Axes>"
            ],
            "image/png": 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}