Part 2 - Grouping, Plotting, & Merging.ipynb

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        "<a href=\"https://colab.research.google.com/github/mkitti/pandas-workshop/blob/master/Part%202%20-%20Grouping%2C%20Plotting%2C%20%26%20Merging.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "## Grouping\n",
        "\n",
        "The pandas `groupby` function allows us to group our data on the values in a column or column to look at summary measures for records sharing the same values.\n",
        "\n",
        "For example, let's load the speed camera dataset again and ask which camera locations or days of the week have produced the most violations."
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nuitrcs/pandas-workshop/master/data/Speed_Camera_Violations.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
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          "data": {
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              "\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>ADDRESS</th>\n",
              "      <th>CAMERA ID</th>\n",
              "      <th>VIOLATION DATE</th>\n",
              "      <th>VIOLATIONS</th>\n",
              "      <th>X COORDINATE</th>\n",
              "      <th>Y COORDINATE</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>LOCATION</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7738 S WESTERN</td>\n",
              "      <td>CHI065</td>\n",
              "      <td>07/08/2014</td>\n",
              "      <td>65</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111 N HUMBOLDT</td>\n",
              "      <td>CHI010</td>\n",
              "      <td>07/16/2014</td>\n",
              "      <td>56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5520 S WESTERN</td>\n",
              "      <td>CHI069</td>\n",
              "      <td>07/08/2014</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1111 N HUMBOLDT</td>\n",
              "      <td>CHI010</td>\n",
              "      <td>07/26/2014</td>\n",
              "      <td>101</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1111 N HUMBOLDT</td>\n",
              "      <td>CHI010</td>\n",
              "      <td>07/27/2014</td>\n",
              "      <td>92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ADDRESS CAMERA ID VIOLATION DATE  ...  LATITUDE  LONGITUDE  LOCATION\n",
              "0   7738 S WESTERN    CHI065     07/08/2014  ...       NaN        NaN       NaN\n",
              "1  1111 N HUMBOLDT    CHI010     07/16/2014  ...       NaN        NaN       NaN\n",
              "2   5520 S WESTERN    CHI069     07/08/2014  ...       NaN        NaN       NaN\n",
              "3  1111 N HUMBOLDT    CHI010     07/26/2014  ...       NaN        NaN       NaN\n",
              "4  1111 N HUMBOLDT    CHI010     07/27/2014  ...       NaN        NaN       NaN\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
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      "source": [
        "Now that the data is loaded, let's find the 10 locations with the most total violations recorded.\n",
        "\n",
        "To do this, we need to group by the ADDRESS column, then examine the VIOLATIONS column of the resulting grouped dataframe."
      ]
    },
    {
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      },
      "source": [
        "# first let's group by address and look at descriptive statistics for the first 10 records\n",
        "df.groupby([\"ADDRESS\"])[\"VIOLATIONS\"].describe().head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADDRESS</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10318 S INDIANAPOLIS</th>\n",
              "      <td>1084.0</td>\n",
              "      <td>99.974170</td>\n",
              "      <td>39.412342</td>\n",
              "      <td>4.0</td>\n",
              "      <td>72.00</td>\n",
              "      <td>96.0</td>\n",
              "      <td>120.25</td>\n",
              "      <td>259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1110 S PULASKI RD</th>\n",
              "      <td>499.0</td>\n",
              "      <td>22.412826</td>\n",
              "      <td>15.078174</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.00</td>\n",
              "      <td>18.0</td>\n",
              "      <td>31.00</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1111 N HUMBOLDT</th>\n",
              "      <td>1096.0</td>\n",
              "      <td>58.250000</td>\n",
              "      <td>18.067744</td>\n",
              "      <td>6.0</td>\n",
              "      <td>46.00</td>\n",
              "      <td>57.0</td>\n",
              "      <td>70.00</td>\n",
              "      <td>117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11144 S VINCENNES</th>\n",
              "      <td>526.0</td>\n",
              "      <td>19.612167</td>\n",
              "      <td>10.058579</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.25</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.00</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11153 S VINCENNES</th>\n",
              "      <td>517.0</td>\n",
              "      <td>9.698259</td>\n",
              "      <td>5.182134</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1117 S PULASKI RD</th>\n",
              "      <td>492.0</td>\n",
              "      <td>19.483740</td>\n",
              "      <td>12.954320</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>17.0</td>\n",
              "      <td>27.00</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142 W IRVING PARK</th>\n",
              "      <td>1091.0</td>\n",
              "      <td>84.210816</td>\n",
              "      <td>29.672455</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.00</td>\n",
              "      <td>81.0</td>\n",
              "      <td>101.00</td>\n",
              "      <td>248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115 N OGDEN</th>\n",
              "      <td>1094.0</td>\n",
              "      <td>41.311700</td>\n",
              "      <td>35.887579</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>28.0</td>\n",
              "      <td>61.00</td>\n",
              "      <td>197.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226 N WESTERN AVE</th>\n",
              "      <td>531.0</td>\n",
              "      <td>18.617702</td>\n",
              "      <td>15.435526</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1229 N WESTERN AVE</th>\n",
              "      <td>530.0</td>\n",
              "      <td>39.500000</td>\n",
              "      <td>21.222848</td>\n",
              "      <td>3.0</td>\n",
              "      <td>24.00</td>\n",
              "      <td>34.0</td>\n",
              "      <td>51.75</td>\n",
              "      <td>119.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       count       mean        std  ...   50%     75%    max\n",
              "ADDRESS                                             ...                     \n",
              "10318 S INDIANAPOLIS  1084.0  99.974170  39.412342  ...  96.0  120.25  259.0\n",
              "1110 S PULASKI RD      499.0  22.412826  15.078174  ...  18.0   31.00   83.0\n",
              "1111 N HUMBOLDT       1096.0  58.250000  18.067744  ...  57.0   70.00  117.0\n",
              "11144 S VINCENNES      526.0  19.612167  10.058579  ...  18.0   25.00   75.0\n",
              "11153 S VINCENNES      517.0   9.698259   5.182134  ...   9.0   13.00   31.0\n",
              "1117 S PULASKI RD      492.0  19.483740  12.954320  ...  17.0   27.00   61.0\n",
              "1142 W IRVING PARK    1091.0  84.210816  29.672455  ...  81.0  101.00  248.0\n",
              "115 N OGDEN           1094.0  41.311700  35.887579  ...  28.0   61.00  197.0\n",
              "1226 N WESTERN AVE     531.0  18.617702  15.435526  ...  14.0   28.00   88.0\n",
              "1229 N WESTERN AVE     530.0  39.500000  21.222848  ...  34.0   51.75  119.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUHruDpKygWp",
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      "source": [
        "The above records aren't sorted in any meaningful way, but the first thing to note is that the Index is no longer just an integer, it is now the Address. This is because the `groupby` method returns a special object with a new index made up of the \n",
        "values of the column being grouped on.\n",
        "\n",
        "We can still use the `loc` indexer with this new grouped object to, for example, find the count for a given address:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "# `count` returns the number of rows for this address, not the total violation count.\n",
        "# IE this tells us the number of observation (in case of our example data, Speed_Camera_Violations,\n",
        "# this corresponds to the number of different days with at least one violation).\n",
        "df.groupby([\"ADDRESS\"])[\"VIOLATIONS\"].count().loc[\"19 W CHICAGO AVE\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "432"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fULygWVygWs",
        "colab_type": "code",
        "outputId": "ab322100-6600-4f40-b4ba-c4f6664b9573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# to get the total violation count, we want the `sum` method:\n",
        "df.groupby([\"ADDRESS\"])[\"VIOLATIONS\"].sum().loc[\"19 W CHICAGO AVE\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1618"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plhK-1nTygWv",
        "colab_type": "code",
        "outputId": "452d608a-c38d-45c1-8e2c-ec2b7562b443",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "# Now let's get the top 10 camera locations by total violation count:\n",
        "df.groupby([\"ADDRESS\"])[\"VIOLATIONS\"].sum().sort_values(ascending=False).head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ADDRESS\n",
              "4909 N CICERO AVE       220704\n",
              "445 W 127TH             169337\n",
              "2900 W OGDEN            139183\n",
              "4124 W FOSTER AVE       127071\n",
              "10318 S INDIANAPOLIS    108372\n",
              "2705 W IRVING PARK      107599\n",
              "1142 W IRVING PARK       91874\n",
              "536 E MORGAN DR          82331\n",
              "5816 W JACKSON           80174\n",
              "4831 W LAWRENCE AVE      69538\n",
              "Name: VIOLATIONS, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xv4mqknLygWx",
        "colab_type": "text"
      },
      "source": [
        "It's possible that some locations just have more observations than others, so a more meaningful measure is probably the mean violation count per observation. To get this we just need to use the `mean` function rather than `sum`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pihrhx6hygWy",
        "colab_type": "code",
        "outputId": "646b0612-a8a9-4dff-bf43-8d8913d2a0a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "df.groupby([\"ADDRESS\"])[\"VIOLATIONS\"].mean().sort_values(ascending=False).head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ADDRESS\n",
              "4909 N CICERO AVE       226.595483\n",
              "445 W 127TH             154.645662\n",
              "2900 W OGDEN            126.876026\n",
              "4124 W FOSTER AVE       120.332386\n",
              "10318 S INDIANAPOLIS     99.974170\n",
              "2705 W IRVING PARK       99.078269\n",
              "215 E 63RD ST            84.689008\n",
              "1142 W IRVING PARK       84.210816\n",
              "536 E MORGAN DR          75.188128\n",
              "2549 W ADDISON           73.668488\n",
              "Name: VIOLATIONS, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZheiAu7bygW0",
        "colab_type": "text"
      },
      "source": [
        "How about days of the week? *When* are people most likely to be caught speeding?\n",
        "\n",
        "The simplest way to do this is to create a new weekday column and group on that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmXfeN1_ygW0",
        "colab_type": "code",
        "outputId": "c97e5799-d762-4dac-90d7-7dc587914a42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# datetime series have a special `dt` property that exposes the date/time-specific functionality.\n",
        "# In this case, dayofweek is a 0-based index where 0 = Monday, 6 = Sunday.\n",
        "df[\"VIOLATION DATE\"] = pd.to_datetime(df[\"VIOLATION DATE\"], format=\"%m/%d/%Y\")\n",
        "df[\"VIOLATION DATE\"].dt.dayofweek.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    1\n",
              "3    5\n",
              "4    6\n",
              "Name: VIOLATION DATE, dtype: int64"
            ]
          },
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            "tags": []
          },
          "execution_count": 9
        }
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        }
      },
      "source": [
        "df[\"DAY OF WEEK\"] = df[\"VIOLATION DATE\"].dt.dayofweek\n",
        "df.groupby([\"DAY OF WEEK\"])[\"VIOLATIONS\"].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DAY OF WEEK\n",
              "0    27.446626\n",
              "1    26.745172\n",
              "2    27.110899\n",
              "3    28.371709\n",
              "4    30.219523\n",
              "5    42.380211\n",
              "6    41.233922\n",
              "Name: VIOLATIONS, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xKIrcgqygW5",
        "colab_type": "text"
      },
      "source": [
        "## Plotting\n",
        "\n",
        "It's not easy to understand at a glance the distribution of speeding violations by day of the week above, so let's produce a simple plot to visualize and help understand it.\n",
        "\n",
        "Pandas has some basic plotting functions, but I prefer how it interacts with a different visualization package called Seaborn\n",
        "\n",
        "If you do not have seaborn, you can use pip to install it pip install seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K17Zn-JkygW6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns #The cannonical way to import seaborn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcqdi4UUygW9",
        "colab_type": "code",
        "outputId": "6b19c968-338d-4672-b39d-ed69ec483f9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "# The beauty of pandas with seaborn is how cleanly they interact with eachother\n",
        "\n",
        "sns.lineplot(x = 'DAY OF WEEK', y = 'VIOLATIONS', data = df)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe38fa2d9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AJ5eifMaYlWloskAmEWMkV2Tf0BQfeO2myrQZ+bJPb5PkXiwXy2dWK2OMOQNF12cy75JN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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X4Uc2lX3ygXA",
        "colab_type": "text"
      },
      "source": [
        "What if time of year is a factor here? Seaborn has a wonderful feature called hue, which allows for a quick comparison of different types of data in one graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOtV0kk1ygXB",
        "colab_type": "code",
        "outputId": "b2abb8fc-d166-4ccd-f728-8919ea4640c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "#first lets create a month column\n",
        "df['MONTH'] = df['VIOLATION DATE'].dt.month\n",
        "\n",
        "#then lets recreate that same plot but with the months separated out\n",
        "sns.lineplot(x = 'DAY OF WEEK', y = 'VIOLATIONS', hue = 'MONTH', data = df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe38f97aa58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MV96kdmjyoYKvzcUG5//l9wm9CHPCoTY7lhnuYVf5TYyMfxQThpnxF0LDMPS7jP9mRqUi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\n4XCsc5wjcDgcjnXO/wfsBraiDN+bbwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQe8ORqiygXD",
        "colab_type": "code",
        "outputId": "6fc7049c-d3fe-4487-e3ea-f66760621392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "#thats a little chaotic, lets try flipping the hue with the x axis\n",
        "\n",
        "sns.lineplot(x = 'MONTH', y = 'VIOLATIONS', hue = 'DAY OF WEEK', data = df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe38d041390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R51jQ2MAnPn0v7gnMGoUu6O8MYXc4zpkyehYGqLkcSOAr8eEcI930dOLxBK/+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\nvGwK+bLnp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sOslLFKhc3doVKKLIqRmcSplnEH91NC7yYITcMeqPXlTgzMNCNsdgGBbhub\n37eHLidvpRQyikh7Xu53kCSoJEbGCbLnX9X2rBkGwrL6EUWfKMx8MRZaXrCWcfSmyUAIgV0tYVdL\nDN18iKjV7ZPCGisr6whNozg+SGlimMLoAFo/9am/SQLaDfaJ4HWg+rtnv+PTWWuTRHkYars2xere\nJoE3jnf+hbM887Wn6ay1GT8ywT3/+JOMzO6uE+hKpHFC5OedQcVqgfbcMp3Feu4loBRxt5cvyAqi\nTg+/0eXs06cJuyEH7zrMyJiLiHooy8EYqF6KAFKF35EksSJqdektNsjCCN0y0S0D3TI2d1bC0HPx\nLr2AJgP00MtTUXoBtXERb4jTk3erKhQq7KKZJkYh7zwSUuU3mpaTltB0kJBGMd1z84QrDYSuUZga\nwx4ZwDBNdFJU3EMvlXEnJtGNflvgllSAjBM0p4BZLGy2FuYEoLBLBeyBcj5VvI93HYSmYdWq0O6Q\nRRHF8SGido+kF+TX4DZpPCFEHnFaFjLOvSc2FnClFCpNkUmCjGNUHCPjEJnEJH5wVQfbZhRhmrli\naaGAXihsNim84d9LCJyBCs5AheFbjxA22nTmV/EW1vAW6whDpzQxjDtUwRkdYG/5gb1jnwi2gZKS\nKIjptXt06x3SJEGI3JO3uIf2zyuxfCbvBFq7sEJtfICP/+OHmb555g2Psoe9vMd68vgBTFPQPL9A\nr97FrpZQWUri9RBCIw0iYj8kaPmceeoUMpPc8MBxKkUQcYAoFimMljEMjSxV9DoZaQxxp4c3v0rq\nBWimQWliCJVmpFFM0gvJ1rvIdPupR6EJdFPHKLq5hkqfQDSzTyCmDpqVC4apDM2y8rSU0nKSyBRZ\nkhAuN/AuLKKkpDw9xsBNBzEdK68FpCne6dNotk1xZvoyA5oNyCRFGEY+NAakYZQTQLmAXdsngPcC\nNsgg7nTIggB3qIJVcggaHZIg2raYvPE8zXKQcS5CmPfmC4Rp5qZKfcVcJSWCEKUyZGagYrlJFHk0\nkZD4eSSx0Qynuy5GqYRRKqEXCm9qCl0IgTtcwx2uMXrbUfx6i+7cKt5ine7cCtZAmcrkG9so7hb7\nRNCHkpLQj+g1PTqNTm6vp+eta7uRdL4WWitNfvLVH3PxpfMUqkUe+MwvcfTuG3Y0qHg9xEFEHMYU\nB0oMjFbRZUp7rknQ8nGqRWSckPT83K7R90FKuvUuZ586hWEb3PTATbh6jEhT7OEq9kCRNMkJIIkU\nSdent7BG0vURhs7g8Wkq06P5rmojlBaXum2yKCGLEtL+xyyKSTyfuOORBSFpEJHF6Wbn0JXQbRPd\ntjCLLmbBwXBtNNOgdWqOxPMpHRhl8NgB7IHqZiOQUgr/4kVUllE8dGh7Euh7Ixul4qUIoFLAru4T\nwHsNQgisSoWEfIhQty1KE0PEHZ+w6/XVR7e4jfWl0gGUFPnuX8i8ZVn0ZyjhUlpSGOi6QtczpG0i\nbIcNdXPRt69UmSSLI6QfkAUB0doa0doaaBpGsZgTQ7mMZm1PTLv6PTWN4uggxdFB1Psl7XNLlKfe\nWhKAt5AIhBDTwB8DY+RJ3j9USv1vQoh/BfwTYK3/0P9GKfW1t+o8rgWZScJeSK/VpdvoIqVC17Xc\nb3cP0g87odfu8dw3nuG1H5/AsE3u+pUPcsuDt17TZvJaSKKEyA9xijYTh0exLB2VJXTW2oTt3GQ+\ni2KSdt5poaREMw1Wz69y8fnzFGpFbrj/GFbqgVS4k4MIy6bXzkjivJDlL6yReAHC0CnPjjN4wxSa\nBppuYBQLZHFMFoSbrW+arqMVdMzC1QNYaRgSLi6hkgRrdASjWCZLM2SSkUYpMopJo5isL1iXBiFx\nxyPtn7s9UGb24/diFvJW1633Vri8TOb7uFNT6M7Vr61kngLQXYcsirErxX0CeI9DCIFZKecKsV4P\nzbaxa6VcOHCjpiQ2lEgvfY7o+2nEuZCiphmXJFG2XFRKSuJ2A5HGmOUSurN9H7+SiizJ9YDilkfa\n7ZIFPmm3C0tLCNPELJc3I4btNim7+n01jcLIwNvSxfZWRgQp8C+UUj8VQpSBZ4UQ3+7/7H9RSv1P\nb+Fr74gszQh7Id56F6/poaRCN3Xs4s4WcntFHMb87LvP89JjP0NJyc0P3ModH//Armwjt0MSJYSe\nj2XpjB6o4RQthJZb+HVWO4TdMFcQ7fUIGm1klv9OQjeY+9kFVl5bojY5wA33zUI3H8e3x4aIU53E\nz4jbHr2FOmkvTwG5E8PUDo/jVot5scq0MIqF/vi9k08mez1UKq/Klaq+SBcKNNOiODtDsLhEvLoG\nQxJnYmLH3ZKUMu/sKeX6QpqZy2BkQbTpcgWQdDrE9TrW4CDWwNXdFFIq0p6PUShg16rY1euvNb+P\ndwZCCMxSPpwZdz102+4vlK+zWOqgGQVk2Ncx0rapLWgaVm2IpN0g6TZRSmG4VzeBCE1g2Da6afbd\n/EaRUhC3OkTtNtLvEbdaxOvr+UsXCpenkd4GWem94i27O5RSS8BS//OuEOJV4I0Zb75JZElK2Avp\nNDr47R5K5k5K7i79SHf9OmnGiSdf4flvPUvYCzn8gaPc+fA9VIb33o2SSzTEBO0euq4YnhikWCui\n6QYyzfBbPn6jg5K5eFy01iBse2imgeGYZGnG2adO0VpYZ+KmCY7cMUZc7yBMAyo1/EAjbnXpLayR\n+iGaZVKcHsWslCmPVbFsK/clLhSu6uwRhoFVrpD4PbI4QehGvwKsQGhoupHvgkSeky0ePky4tETc\naCCjCHd6ettim6ZpKEOQeR6iWEQKyIIAYV4afpNxjD83h+66OBMTV71nWZSSxRGF0UGKE2P7BPBz\niA0yQGgknW6eitnFfSyEhua4EEfINNnWXlUIDbM6zP/f3pnHWJbd9f3zO3e/b6291+rqGWY8tgdj\n4xExOIoQBgUlBBMpQqCEgEByImUBBGHLP0gRYCkIQRSJyAJiUCxIYhxBnA2HLVgKTowzeMaeGc94\neqneu6q7a3nL3c7JH+e+11XdVTPVM/W6uqvORyq99+7bzu1+9/zO+S3fX7G2Srl5B6tMu3NSiCiF\nYOtRlGjShSnSY7NURUm+0SdfW6fq99CDPtmNG2Q3blg3UrM53jGo8NGQOn8oV4mILAHvAz4HfBD4\nxyLy94HPY3cNDyIK+ECsXl7h9rXbYAxBFJA0032f/K++doULL57j4ovn6a/1Of7USb7hb32A2dNz\nD/RZVu62ospy+uubKKWYPTFNc6aNUopimLN54xbDtT4IeFGIzjLWl69iitLqsyihGOa8+tlX6N3a\n5KlvOsux022ylXUIQqqkTbbSo3/lJmU/Q0UBrbPH8Zopnu/TmmujlGBMRdhqWsNx30DBoK1gm2To\nvMALQ8QP6sl/+8tFhOTECbw4ZnDlCr2vfpX0zJkdXTqiBPyQstfHbkHutjQ0WtO7cAERIV2821B+\nZAAwBi/yaZ44STyzd4lgx+NJ0EhBhGJtrRabq8UH1e6VxSKCqqvldVnAjsZACDozFOu3KDfXwBj8\ntLXrOEQpMIKuSjAlnh/QmJ8ine3axIpNK71eDfqYQZ+y16NcXwdAhSH+yI3UaLxlN9LbZeIFZSLS\nBP4U+DljzKdEZAFYwcYN/gVw3Bjzgzu87yPARwAWFxfff+HChbf0/csvXbSKmOH+2bysn7H80gUu\nvnCeSy8vU2QFfuhz8h2neeYb38nJWt9nL4wmf1OWlLmV4BUlzJycpT1jdxJZb0BvZYMysxWWfhTY\nytrbG3VHMca+78FGn1f/7GWKYcGz3/o0jdij6g3QQUQ2FHpXV6gGGV4Ukp6cJZpuUw5LgiSgOdOy\nZfWeh9+4pw7AgKlK6ugZ4tnUPfE8W9Szbsvo30y1s+z1bJBXa9LTpwnaO++WrDad3uau61+6RHH7\nNumZMwTtdm0ACmvkWw2COECFIfF01/USOEJUeWGzfIoCU5U2k208rZl6cbLdSBhj0EVupdd3MAZg\nr81i4zY6G+CnLfzGm+/s7fVs4xUqCOtqYau3ZbPtBjYGVubowYCq36Ps9cbB7ZEbKWi1UHFMsTGg\ncXqe5sKDLSpHPBKVxSISAJ8G/ocx5pd2eH4J+LQx5tk3+py3U1m8X4ZgY3WdCy+e5+KL57n2+lWM\nNiStlMVnz7D47iVOPH1yz5ISWyd/XZY2Y2mQgRGmjk3RmetgtGG4bhtYGK3xowAv8K1G+TBjuLpu\ni1w8ZXPuBfp31nn5j19GeYp3f+szBFWByTOGQ6G3skE1zPHikMaJOaIZO5GWg4Kka4W6TFnhhaHN\n6x/tmrQdKyJIEOJ5vs3zv+fC0ZWm3OzZCysId21MDrV75+JFqsGAaGGBaG5ncbit5LdvM7h0iWhu\njmhhgWpYAIaw3SBs2viFqTTx7PS+BPodjy9mpHBbVRit0aOisrGRuCs7oosCXRa2f/IOGkVbjYGX\nNvHTvfUdsLEy2ylPBXa3PM6022IUqmFhVXjzzAacNzfRQ6uFJJ6HihJaTz9B98mlt/RvceCVxWLP\n+teBl7YaARE5XscPAP428OKkxvB2MNqwcunmePK/fdUGfrrHpnjPt7yXxWeXmBulVO7l8+6Z/OuD\nZMMCXWk6c11rAMqKjWu3GW7YRh1BHIxXxUZrsvUe+XoPdInyPZSvCBPh1oWbvPSnr5G0E5764FN4\neZ/+nR792zZ100uibZ25qrKiKkqac22iJEKXJV6a4EeRvUa0tvnVykNF8baU0Z1QniJoN6n6Q8pB\n37p0dlmVqzCk8cQTDC5dIrt+HT0ckpw6tevrq+GQweXLeI0GXnuKapiPDYDyPXuxFznR9IwzAg77\nOxUZ/568LdnfOxmJcjCg7G9iinrHO04ttYVoftqhRKj6m9ZN1Oi8qTEQEUQ8u2vNc6QoaoNgd9Kh\nb6VOdFVRDq1RUFFMMDUDRqOHA8rNTYqNDR6GDNAkYwQfBL4PeEFEnq+P/QzwvSLyXuw/93ngH0xw\nDA9EVVZcefUyF184z8UvW3+/iLDwxDH+yoe/kcVnl2jP7l0C4u7kX4wLrwTrU8yHOWVe0pxu0Zlt\nY4qKO5dWKPPCNnNvxNt+bFVeMry9TpXblY0f+USJIojh3OeXOff5ZdoLHZ54bolyZYX1W310ofHT\niPbiMaKp1vjzyqzAIHSPTdnCLFMRtJoo37erf2MQz8eL4/EqyTbkyGoNt3pHUDfPGD8WwW8kSOBT\nbGzaINouFZiiFMnp03hJYlNBs4zGmTP3Bc9MVdG/cMFmdMwvEHdsuuAopW50oUXdjksNdbwpOxmJ\noJFSFS3Kfm/cCMZUur52K7tIihtgDNWghy4K/LRlJSf2YhC8kUEYIkUtf167o5TnETbuNwoghEkD\n1eziP4RueZPMGvos96vIABxIzcBuDHtDlr98kYsvnufyK3f9/aeeWWTx2SVOv2uReIfGJrvxRpO/\niJAPc/JsQKPTYPbkDDovWVtesalqcXBfiqkxUGz2ydb7KKmIE0OYRLUMRMWLn3mNG1+9yczSHAsn\n26y9chFdWgPQWpon3NIHwRhDMSwIooDmrI0HiFJ4aWIVIKoS8QO8IBzHB3RZUWRWYyjsNK3hqIxN\n9ax7uJpKo+sV1ggVRhS9PmV/aDVbkLEO6DigLILfnSLxAwZXLrP52mski4s2IwRbrNa/uIzOc1pP\nPUV6Yv6+Fb/OM4Jm08pbOxxvES8IkYayxkCpHRcwupoiv7NKfucWxfoqKohRcYpSfi2JsntcSuq4\nmtGGcjhAlIcXRtuCwzsZhezOxmNfR/DIsr6yxsUXz3PhxfNcP3cNow1pO+XJr3+Kxa9d4vjXnHgg\nCemd3D5bJ3+wvXDzQU6cxsydmKXKctav3EL5HkGyc/qbKUryjU2UFLS64PneWAZ69dImr/3562zc\nWGd+aZbUL+ldXsGPA1pLxwmntvsyjTbkg5yknZB2G1YBNPBRcYQIqCAay+AaY9vx6bLCD0PS+SmC\ndG99FUaNvjF6HDcoNnpW9bTWG9LVqKes/VOdLhJGDJYv0D93jnBugaDToVi7Q7W5QfPsGZqnj9/3\nXVVe4EUxgesv7NgHlO8TNJrWTVSZ+zJ4lOcRz8wTdqbJb6+Qr91Glxl+2kKIsaVT2ODzLj0RRAmC\nj9GacthHeX4tZqfu+66wkeD5npVtnzBHwhAYbbhx4fp48r9zzWarTh2f5us+9D4Wn11i9tTcA6WV\nGmNAa3RVoIsSMAiybfIHKIuKrDfEj3y6s22qYc7g1jpe4BO3dvgPNgZMhcn7iM5J7Y6UsjBkG5rV\ni3e49spV1q7dQZQwt9AkocD3fRrHuwQLc1asbQu6rKwbaqZF1Ijs1jaO8JLEKi7W/n+jNUU/B6MJ\nmg2iTgMverByebvyEUChfPCjEL+RkK9t1AZnN/fNFK2Tc9z+0svkN6+jqMhXVolmZ2guLd73al3Y\nXsdhZ2/BO4djL4jn4TealP0epqp2TOdUvk88d4ywO022epNicx3UJmFnGi9tYPLS6hSZeuL37t8t\nbK1BKAc9lF8rnh5QttuhNgQXXjjHn/3On3D1tStk/QwRYW5xnvd/+3Ocetci7dkOqm58boypO1a9\nMUZX1g1S5GOhwnsnf7hbwYyBtBlCYSh6Q4IoIIjvKSKpJ3/RGVJlCBoDVAb66yVZv2J1+RbXv3KV\nwVofP/KZnm+QRoq4FdOcivE7TaTR2a7DAJR5idGG9kIH5St0URC0mgSN5njVUhUlOrcTazzdqoOw\n+/fT8OMYFQTkd9aohhlqF+OigoDp9zzLxldfp7d8GS+O6b7zHfdnclQVxmji7rRLE3XsO6I8/NQa\nA12Wu14LKghJjp0kzGbIVm+Q315BNu4QTc8RTk9Bpa0kyzDDFKVdKvpq23yxtQbBZi/VSqcP+Xd9\nqA3BVz73MpdeWmb+zDwnvuYEC0vzBGFofdl5wfqV1e1vEEF5ymrYe1Za2fM9u1MwGmNKayqUwvO8\nHV0lutIMNvpUZUUcBvihQiEErXsCS1snf50jprKTf2H7vBQFDO9ssnJ+hRuvX6ccFiSdlJPPHMfL\n+iilaB1rk6QeJm5A3LjPCBTDAuUpWgstlAGlfMKZLl4YWL2ULLcSG1FI4/isVXGc0A9QeR7R9BTF\nZs/qxATBrv0K2k89STQzbXcs97jojDa2iczM1L4aK4djK6IUftqgGvTRVYl6g857XhSTnlikHPTI\nVm4wvHEVFawSzczjN5r4jRRTVeiipMoyu5vFVuHb1O+7GUa6rNNZ6xqEh8WhvpL+6vd8M/OnZlFK\nESR7KOWudXJ0panyEl2VVEVpg6jczTYYpSF7nof4ghKPqqrq44Yo8Gg2E4Ik2l6/sG3yzxBjk0y6\nOQAAFRdJREFUV/5GAopSMVwvEN+nt7rOtZeWWb24gtGGzvEu808sYDY3KNZ6BK2U9lyC54FJ2xDd\nG2A25P2cMAlIp5so5eHHCWG7aYW1ekMQiNpNolYD70E7hb1FRISw1UQFPvnaOqJlV1dRNH2/hpAx\nBp3nBJ3Wtp7LDscksIkUKQwGb7gzGOEnDbxTS5S9DbLVmwyuXcKLE2sQErvL9pO4Tlu1ootVlqHr\n5kvKswtQaxBs0ZnNdn2800cPnKRpgy2jdnN7QbD5+pgSJaAiH+T+AqmyKMmHObqv6++KSRoRYRxa\nAbvRaneXyR8J0H6C1j7DtT5FntNf2eDKly6O/f+zS3MsPH0cpSvWX7+CqTTNEzMkab2lbHZsQ+Et\nVEVFMcxpTDdJu7Ys3k8TxPcpBzaDJ52bImjEB5Zz78e2LiGvm43sVbZXZzl+IyFId1aFdDj2GxGF\nl6QwHNhiSe+N62msDlIbv9GiWL9DdmuF/uUL+GmDaGYeL4qtgQlDW7zZtMKOVVGgs4wqsx0PxFMY\npdDZ0PZknjCH2hDsGW3Q2q78bYRH7LZtq8KCMVRFRVlYn7sf+rTnOnbyj+7J+jEGdLnL5J9iVGgF\nswYZg9U73Lp4k+uvXKZ/p4cfBZx89jRzTy7g+x6by9cZ3LiNl0R0zk4RUGA83zaKV3dz6TFWXM9o\nTffUHGESYkqNRCEGRRCFRPMtvF2aeDxslO/XrqJNys2+7QT1Bm6pKs9tz4LW7povDscksB3PEkCo\nisyKKu6hfiDsTBG0OuRrt8lur1AunyNotYmm51BBOH6dBLZhE2liU7HL2oWU3Y1DTpqjawjq6kJd\nlXV/3FqHZMsi2WhDkRdUhZVYiJKQqakponSHBvW7BHzvnfxHn7t5/RZXXjjPzdeuUgwLkk7C0vuf\nYGZpDuUpit6AW69coBrmJAtTNKciVFVgwhjSNtR6KaOevmVeoYKQ7vEuVJpymBO2WiSzXYJm+kiq\ncFpXUasOJK8jamdXkS5LW1DmMoQcB8So/SUK9DCDHTKBdnyfUkRTM4TtLtntVfK1WxQbG4SdLuH0\n7H2xB/EUnhfiRaFdfA6tLtikefRmh0kyLi2vMFVVr/xlW4qYrjR5VtjWdghJO6HRSgiTEG+nwo7d\nVv7e9sl/xMb121z4P6+wcu4GptJ0Tkwzf3aO9rFuXeVr6F1ZoXf5Bsr36T51klAVUBWYpGUNgQHM\nyHh5lFlJ0EhI2wnVICNoprRPnrKN2h+DrBo/jlGzO7uKTKXHGkIHpczocIA1Bn6UoD2faji0Utae\nN65GfsP3eh7x7Dxhd4rslq1ByNfXiKamCbszO16nIoIK/Ifyuz/UhkBXla2ANcb2La00tmOK1CXe\n9nVlUdo0SwOer2hNNUka8Tadn3s+eMvkX+268h9hjOHWxZss/9+vcHt5BfEUc08eY/7JY0SRV1cl\nii0ye/0KxUafaKpF69QMarhplT8bXfAD4G5estZQDIaEaUTaTvEjn/T0ceKZ7mO3ch67ijY2KTZ7\ndQ8EQRc54VTXZQg5HhmUHyAN3wrWZUO0KWuD8ObXnPIDkvnj4xqEkVGIpmYJOgcnnX6or66//N0/\n47U//SJ+FBCkEX4cEiQhfhwgvi0L9+OApJ3SnesQN2L8cBf/n6msy6ee/AGM+Gi/gVHRfZM/2EKu\n669cYvkvXqO3uoEfB5x671kWnj6BqpUPR+ljw5U1Ni5cBQOts8eJGj5quAHKxzQ6Vr3Q80AUVVlS\nDXLbr3emTWu+ixf5xNPT+Mne5TAeNWxf2tpVtLaO0RVhp4O/Q98Ch+MgERG80Ob86zy3BgH2bBC8\nMCI9fopyOLAppyvXye7cIp6Zw28+fBfooTYEC88ssnL+GuUwp8pLBrc32Lha7NhEXfkeYSMiaiT2\nthkTphFR6hMnQpT6RGmIF0cYr4HxQrYFFLaQ9zOufPEcl//yHHk/I+k2OPuBp5l76gQKKAe2XZ74\nHqbSrJ+/SnZrHb+Z0F46hl8NkayPCWOk1UVUAMZQZgXaGPzAI+00aC1M4achylNE3e4bVO0+XvhJ\njAp8qiy3ktgOxyOKiNh2mUFgG9vnGUZk1x4H9+LHCd7JRcp+j2z1BoPrV1B3Vomn5/HShyedcqgN\nwYn3PMG181dsw/c0ptFOidspnqcoBznZ5oC8l5H17G3eG5JtDuitrHHr/HUbJL4H5SnCRkzYiK2x\naMRE9eMgCVl9/RrXvnwRXWm6p2ZY+oan6J6ascJRpaYcDACD8j3y9R7rr19G5yXpiTnS+Q5qsA66\ngkYHSVtWr2cwRESI2ylB4OFHIfFMGyXghaENoh4y/7nyfecOcjw2iFL4cYIJQ6psiC4Km3m4h+tS\nRAgaTfy0Qbm5znD1Jv2ry3hJStid2bGT335z6K+0ZuyTqJioESFoqvVNKux/XBwHxGmEOtbB8zW+\nV6FEIwIaRVEosoEhH5Rk/SFFPyPbHFqD0RvSW13n1oUbVHk5/j7lKRaeOcXc2XmiZmyLtURsVeFg\nWAeFhI2L1xlcW8WLQrrvXCJMAmTzNiDQnqXEQw8y/DCkNdchiAN0XhI2U8J2A1OWtpNRs/HYxQMc\njsOKKA8/aaDDEj0KKKs9ZhiJELQ6+M02RZ1yOri6bHcds/MTHfehNwRB6BMEPt7WymJj1TE9TxP4\nJZ5vE4iqCrLMSjzoSgPWhRQEQtBJoJNYiVqlwBvdCqbSFEOrLpq0EnReoAIbBMZAleXoYYZ4HuVg\nyMbrVygHGfH8FK0zJ1DFADZuYTyfMu6AUcTNmLiV4EeB7UFQViSzXbzQB10RTXUf63iAw3GYUZ6P\npA1UVT54hpEIYXeaoN0hu7WK35x87cyhNwTbMfiexvc0nmfsyl9DUSrKUqGNAIIE4O3kbh91NzJA\nVVGW5ViWQgxEsc0k8KLAZiYZqAYDqrwApRjeuMXm8g3EU3SfOUs03YL1W5AN0H4EzSma3SZhGo1T\nSctBhhcFxNPTtn+AKKuz8wjWBTgcjruIiE3yeIsZRqI8wockrHgkZhM/hDAs8T29ffKvFFrbyX9P\njOoORg93e50BU7fAs4FpYeP1K+R3NginWnS+ZhHBYFav25aTzS6NYwsE8fa+plVeEnebBK0Ukxd4\ncWTjAY9BbYDD4bC83Qyjh8GhNgSD61dod00tM60pS0WxZeW/rxishTEGbQw6y1GeT9brs/7aRYzW\ntM6eJJzpUg0H+Nm6LVg7fZqw2737McZQ5QUiisaxaTzft0JrrRZ+I31kfjgOh+PBuD/DKMcIe84w\nmiSH2hCoICAbVAw3S4yWcYcs27cUqBVFGd0dPRYZHRgf3/6eu99hlQGty0Z8DzSYYQai2LhwjcH1\nVfw0pnHmBF4U4ldDGK6hooj0zBm86G5nbaM1VVbgpwnxVAu0lcCIpqcfSpm5w+GYPG8nw2hSHGpD\nMFjLWX31JlUFXuSjRCG+sppCvkKNtMDrRhHjZhHjid5q+WiwsQBA6tvRC5Xn3XUZ5RVgKPsZa69d\npBpmxPMzpCfnSToJbNyhXF/Db7dJT53a9h9f5QVGa+KZDn4SYYoS8X3ibtulUToch5CtGUbVcPBA\nGUb7zaGeYW6/cp7+lZU3fpEwlmwQz3YPYnTfU3al79kCEXzbkEbCwOa5h37dntKzGUQCm8vX6S1f\nQwKfqXc9QWdxAc+DwfIy1WBAND9PND+/raF8NczxwoB4fhrleeg8w0tTwlbTxQMcjkOOzTBqYt5C\nhtF+cagNwdJf/yaSbkpVakTZAKwuKntblnfvFyXV+Dl7vBpmmNI+vxfEU1YRtKxI5qdZ+PqnCdOY\nstej97qNEaRnzhC02+P3jAPCnSZhu2Ebvuc5QaeNnyQH7jd0OBwPh90yjODh6FBPzBCIyGngt4AF\n7Nl8zBjzKyIyDfx7YAk4D3y3Meb2hMZg1fsCwY92P1VjTN28xvYiUEohnm/1g8T2yK0KazDGf2Vl\ne/2O/yqqsqSxME379ALGGLLVVYZXrqDCkMbZs9sqBMthjigbEPajEJ0XGIFoZsp133I4jih3M4x8\ndF5QZYOH8r2T3BGUwI8ZY74gIi3gL0TkM8APAH9ojPmoiPwU8FPAT05wHPcx0vEftYATEZTvj0Xd\n7l2Iy0juIIl2+LQdPl9rBleuUNy+jd9qkZ4+PY4HGK0phwVBwwaErepohgoCom7nwLqGORyORwcR\nNc4w2u8Ex52YmCEwxlwFrtb3N0TkJeAk8GHgm+uX/SbwJ0zIEGxeWCZbuQlKUfr+uOewjQd4doL3\nfPC9cbEYUjckfovooqB/4YKNB8zNES0sjF08VW4F75LZDkEagzFUWUbQaBC0ms4V5HA4tvGwYoQP\nJUYgIkvA+4DPAQu1kQC4hnUdTYRicxM9GNi0zAfp+TYyGJ5nA8dbjMfWx4wMSv3YGMPw6lUbD1hc\nJOh0gLsBYRUGpPPTeIGPLktMVRF1O/iJU9h0OBwHx8QNgYg0gd8FfsQYs7511WuMMSKy4wwtIh8B\nPgKwuLj4lr576t3vpNQKMKggtB3EtP1D67pb2ZbHW47d+9hUFbooxvdte8v7uTceMApGR20bEBYl\nVLmND8Qz04dGOtrhcDy+TNQQiEiANQKfMMZ8qj58XUSOG2Ouishx4MZO7zXGfAz4GMBzzz33lkPn\n4gkYQSkBvH0r2jAj3aEthsFojRfH4+8ohwUikM5P48ejHqRDvDh2UhEOh+ORYZJZQwL8OvCSMeaX\ntjz1+8D3Ax+tb39vUmMAWxVcZrnV/NlWNHa3itgWEssD+ehH7xel4J6Cr7sB4Zh4qoXyPIzW6Lwg\naDbxnXS0w+F4hJjkjuCDwPcBL4jI8/Wxn8EagP8gIj8EXAC+e4JjIJ5qoQvbqN6M3T31Sl7btFFd\naYzRdcN6rP6HTSxCkPq2riyGbcbjXkOiywpTGZKZNkEjqY+VmEoTTrm2iw6H49FjkllDn2X39JsP\nTep778ULA7wg2JNLaOzu2Zpeqs09x2vDoc1dwzK6X2mU7xPPt/FqmegqzxHPI56ddlIRDofjkcTN\nTFsYu3v24bOMMegsw0sSwnbLxQMcDscjy5EwBLqsEK3HqqJjt06tJLrf/vpRhlHQbuOnTirC4XA8\n2hx6Q+CnDUxRoLFuHrTeIilR2dtxTtLW5CS55/GW42PjMZKqlnHswFQVxkA0M+2kIhwOx2PB4TcE\nSQxv0tt3JDWxNQ5gbcDWx6ZudWybz6D1jsZF+QFBp+WkIhwOx2PDoTcEe2Hsutmn+IDD4XA8TrgI\npsPhcBxxnCFwOByOI44zBA6Hw3HEcYbA4XA4jjjOEDgcDscRxxkCh8PhOOI4Q+BwOBxHHGcIHA6H\n44gj5kFaOB4QInITK1n9ODALrBz0ICbEYT43ONzn587t8eXtnN8ZY8zcm73osTAEjxMi8nljzHMH\nPY5JcJjPDQ73+blze3x5GOfnXEMOh8NxxHGGwOFwOI44zhDsPx876AFMkMN8bnC4z8+d2+PLxM/P\nxQgcDofjiON2BA6Hw3HEcYZgHxCR0yLyxyLyZRH5koj88EGPab8REU9E/p+IfPqgx7LfiEhXRD4p\nIi+LyEsi8o0HPab9QkR+tP5Nvigivy0ib9yl6RFHRH5DRG6IyItbjk2LyGdE5NX6duogx/hW2eXc\n/mX9u/yiiPwnEelO4rudIdgfSuDHjDHvAj4A/CMRedcBj2m/+WHgpYMexIT4FeC/G2OeAb6OQ3Ke\nInIS+KfAc8aYZwEP+J6DHdXb5uPAt99z7KeAPzTGPAX8Yf34ceTj3H9unwGeNca8B/gK8NOT+GJn\nCPYBY8xVY8wX6vsb2Ink5MGOav8QkVPA3wR+7aDHst+ISAf4a8CvAxhjcmPMnYMd1b7iA4mI+EAK\nXDng8bwtjDH/C7h1z+EPA79Z3/9N4Lse6qD2iZ3OzRjzB8aYsn7458CpSXy3MwT7jIgsAe8DPnew\nI9lXfhn4CUAf9EAmwFngJvBva9fXr4lI46AHtR8YYy4DvwhcBK4Ca8aYPzjYUU2EBWPM1fr+NWDh\nIAczQX4Q+G+T+GBnCPYREWkCvwv8iDFm/aDHsx+IyHcAN4wxf3HQY5kQPvD1wK8aY94H9Hh8XQvb\nqH3lH8YauxNAQ0T+3sGOarIYmwZ56FIhReSfY13Qn5jE5ztDsE+ISIA1Ap8wxnzqoMezj3wQ+E4R\nOQ/8DvAtIvLvDnZI+8ol4JIxZrSD+yTWMBwGvhU4Z4y5aYwpgE8B33TAY5oE10XkOEB9e+OAx7Ov\niMgPAN8B/F0zoXx/Zwj2ARERrI/5JWPMLx30ePYTY8xPG2NOGWOWsIHGPzLGHJpVpTHmGrAsIu+o\nD30I+PIBDmk/uQh8QETS+jf6IQ5JIPwefh/4/vr+9wO/d4Bj2VdE5NuxbtnvNMb0J/U9zhDsDx8E\nvg+7Wn6+/vsbBz0ox575J8AnROSLwHuBnz/g8ewL9S7nk8AXgBew1/tjXYUrIr8N/G/gHSJySUR+\nCPgo8G0i8ip2F/TRgxzjW2WXc/vXQAv4TD2v/JuJfLerLHY4HI6jjdsROBwOxxHHGQKHw+E44jhD\n4HA4HEccZwgcDofjiOMMgcPhcBxxnCFwHFlExGwtjhMRX0RublVYFZHvqpUfXxKRF0Tku7Y893ER\nuSwiUf14VkTOi8jXbkkjviUi5+r7/1NElraqS9bv+1kR+fGHcc4Ox074Bz0Ah+MA6QHPikhijBkA\n3wZcHj0pIl+H1er5NmPMORE5i83nft0Y88X6ZRVWA+ZXR+8zxryArUdARD4OfNoY88n68dKkT8rh\neFDcjsBx1PmvWGVVgO8FfnvLcz8O/Lwx5hxAffsLwD/b8ppfBn60Vvd0OB5LnCFwHHV+B/ieumHL\ne9iuGvtu4F6xvc/Xx0dcBD6LrSzfK09ucR09D/zDBx+2w7F/uFWM40hjjPli7a75Xuzu4K3wC1h9\nm/+yx9d/1Rjz3tEDEfnZt/i9Dse+4HYEDocVLftFtruFwIrPvf+eY+8HvrT1gDHmVeB54LsnNUCH\nY5K4HYHDAb8B3DHGvCAi37zl+C8C/1FE/sgYc77eOfwM8Hd2+IyfY+87AofjkcIZAseRxxhzCfhX\nOxx/XkR+EvjPdb+JAvgJY8zzO7z2SyLyBQ5PLwPHEcKpjzocDscRx8UIHA6H44jjDIHD4XAccZwh\ncDgcjiOOMwQOh8NxxHGGwOFwOI44zhA4HA7HEccZAofD4TjiOEPgcDgcR5z/DybrmTAtf9a5AAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQP4lIhZygXG",
        "colab_type": "text"
      },
      "source": [
        "We can continue to make this graph prettier, but I will save that for the data visualization course, this is enough to see 2 interesting trents.  First is that violations are much more likely to occur on saturday and sunday, regardless of the time of year.  The second is that that violations are much more likely to occur during the summer months."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzniHD7aygXH",
        "colab_type": "text"
      },
      "source": [
        "## Combining DataFrames\n",
        "\n",
        "Often you will need to combine data from multiple data sets together. There are three types of combinations in pandas: concatenations and merges (aka joins).\n",
        "\n",
        "**Concatenating** means taking multiple DataFrame objects and appending their rows together to make a new DataFrame. In general you will do this when your datasets contain the same columns and you are combining observations of the same type together into one dataset that contains all the rows from all the datasets.\n",
        "\n",
        "**Merging** is joining DataFrames together SQL-style by using common values. This is useful when you have multiple datasets with common keys and you want to combine them into one dataset that contains columns from all the datasets being merged."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVm8pRvLygXH",
        "colab_type": "code",
        "outputId": "f49299e2-09ff-4d0f-ee8a-75f41ad7dcc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "source": [
        "# Concatenation example\n",
        "df1 = pd.DataFrame({'Site': [1, 2, 3],\n",
        "                    'Observed Value': [8.1, 5.5, 6.9]})\n",
        "\n",
        "df2 = pd.DataFrame({'Site': [7, 8, 9],\n",
        "                    'Observed Value': [10.5, 11.5, 12.0]})\n",
        "\n",
        "print(\"df1: \")\n",
        "print(df1)\n",
        "print()\n",
        "print(\"df2: \")\n",
        "print(df2)\n",
        "print()\n",
        "print(\"concatenated along rows: \")\n",
        "print(pd.concat([df1, df2]))\n",
        "print()\n",
        "print(\"concatenated along columns: \")\n",
        "print(pd.concat([df1, df2], axis = 1))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df1: \n",
            "   Site  Observed Value\n",
            "0     1             8.1\n",
            "1     2             5.5\n",
            "2     3             6.9\n",
            "\n",
            "df2: \n",
            "   Site  Observed Value\n",
            "0     7            10.5\n",
            "1     8            11.5\n",
            "2     9            12.0\n",
            "\n",
            "concatenated along rows: \n",
            "   Site  Observed Value\n",
            "0     1             8.1\n",
            "1     2             5.5\n",
            "2     3             6.9\n",
            "0     7            10.5\n",
            "1     8            11.5\n",
            "2     9            12.0\n",
            "\n",
            "concatenated along columns: \n",
            "   Site  Observed Value  Site  Observed Value\n",
            "0     1             8.1     7            10.5\n",
            "1     2             5.5     8            11.5\n",
            "2     3             6.9     9            12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THm2cLI7ygXJ",
        "colab_type": "code",
        "outputId": "8265f6d7-c3a7-402d-a54f-7e1eafe0f60c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "# Merge example\n",
        "df1 = pd.DataFrame({'Site': [3, 1, 2],\n",
        "                    'Observed Value': [8.1, 5.5, 6.9]})\n",
        "\n",
        "df2 = pd.DataFrame({'Site': [1, 2, 3, 4],\n",
        "                    'Temperature': [27.1, 18.2, 29.8, 30.4]})\n",
        "\n",
        "print(\"df1: \")\n",
        "print(df1)\n",
        "print()\n",
        "print(\"df2: \")\n",
        "print(df2)\n",
        "print()\n",
        "print(\"merged: \")\n",
        "print(pd.merge(df1, df2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df1: \n",
            "   Site  Observed Value\n",
            "0     3             8.1\n",
            "1     1             5.5\n",
            "2     2             6.9\n",
            "\n",
            "df2: \n",
            "   Site  Temperature\n",
            "0     1         27.1\n",
            "1     2         18.2\n",
            "2     3         29.8\n",
            "3     4         30.4\n",
            "\n",
            "merged: \n",
            "   Site  Observed Value  Temperature\n",
            "0     3             8.1         29.8\n",
            "1     1             5.5         27.1\n",
            "2     2             6.9         18.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40sDEiiCygXM",
        "colab_type": "code",
        "outputId": "bd9fb1d4-8e6d-4f76-87d1-d4a5d9c4ad02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "print(\"df1: \")\n",
        "print(df1)\n",
        "print()\n",
        "print(\"df2: \")\n",
        "print(df2)\n",
        "print()\n",
        "print(\"merged: \")\n",
        "print(pd.merge(df1, df2, how = 'outer'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df1: \n",
            "   Site  Observed Value\n",
            "0     3             8.1\n",
            "1     1             5.5\n",
            "2     2             6.9\n",
            "\n",
            "df2: \n",
            "   Site  Temperature\n",
            "0     1         27.1\n",
            "1     2         18.2\n",
            "2     3         29.8\n",
            "3     4         30.4\n",
            "\n",
            "merged: \n",
            "   Site  Observed Value  Temperature\n",
            "0     3             8.1         29.8\n",
            "1     1             5.5         27.1\n",
            "2     2             6.9         18.2\n",
            "3     4             NaN         30.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO9XvWb3ygXO",
        "colab_type": "text"
      },
      "source": [
        "## Mapping and Applying\n",
        "\n",
        "As we have already seen, there are some basic ways to create a new column based on existing columns.  But what if we have a more complicated function? For that, pandas provides the `map` and the `apply` functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSOXNbsKygXO",
        "colab_type": "code",
        "outputId": "69892206-b703-4271-8ab1-474a6d914aaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "#Mapping is commonly used to apply a dictionary of values to a column. \n",
        "#for instance lets take our lakes dataframe from before\n",
        "\n",
        "\n",
        "lakes_data = [['erie', 64, 19],\n",
        "            ['huron', 229, 59],\n",
        "            ['michigan', 281, 85],\n",
        "            ['ontario', 244, 86],\n",
        "            ['superior', 406, 149]]\n",
        "\n",
        "lakes = pd.DataFrame(lakes_data, columns = ['lake', 'Max Depth (m)', 'Avg Depth (m)'])\n",
        "lakes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lake</th>\n",
              "      <th>Max Depth (m)</th>\n",
              "      <th>Avg Depth (m)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>erie</td>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>huron</td>\n",
              "      <td>229</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>michigan</td>\n",
              "      <td>281</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ontario</td>\n",
              "      <td>244</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>superior</td>\n",
              "      <td>406</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       lake  Max Depth (m)  Avg Depth (m)\n",
              "0      erie             64             19\n",
              "1     huron            229             59\n",
              "2  michigan            281             85\n",
              "3   ontario            244             86\n",
              "4  superior            406            149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjJUmbl4ygXR",
        "colab_type": "code",
        "outputId": "6da5ac58-bc6f-4883-d282-c2d2ba8d6f1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "#lets say I want to shorten the lake names\n",
        "\n",
        "short_lake_dict = {'erie': 'ER',\n",
        "                  'huron': 'HUR',\n",
        "                  'michigan': 'MIC',\n",
        "                  'ontario': 'ONT',\n",
        "                  'superior': 'SUP'}\n",
        "\n",
        "lakes.lake = lakes.lake.map(short_lake_dict)\n",
        "lakes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lake</th>\n",
              "      <th>Max Depth (m)</th>\n",
              "      <th>Avg Depth (m)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ER</td>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HUR</td>\n",
              "      <td>229</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MIC</td>\n",
              "      <td>281</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ONT</td>\n",
              "      <td>244</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SUP</td>\n",
              "      <td>406</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  lake  Max Depth (m)  Avg Depth (m)\n",
              "0   ER             64             19\n",
              "1  HUR            229             59\n",
              "2  MIC            281             85\n",
              "3  ONT            244             86\n",
              "4  SUP            406            149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8pD31LDygXU",
        "colab_type": "text"
      },
      "source": [
        "But what if you want to do something more complicated than a dictionary? that is where the `apply` function comes into play."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mf4sRytlygXV",
        "colab_type": "code",
        "outputId": "952ed67c-863e-4b1e-f33e-cddc0881fe24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "#first lets create a fake function to apply to our dataframe\n",
        "#lets say on some lakes we care about the average depth, while for others we care about max depth \n",
        "def lake_specific_stats(lake, m, a):\n",
        "    if lake in ['ER', 'MIC']:\n",
        "        return m\n",
        "    else:\n",
        "        return a\n",
        "    \n",
        "#we will want to make a new column that captures the data (average or max depth) that we care about\n",
        "\n",
        "lakes['max/ave'] = lakes.apply(lambda x: lake_specific_stats(x.lake, x['Max Depth (m)'], x['Avg Depth (m)']), axis = 1)\n",
        "lakes\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lake</th>\n",
              "      <th>Max Depth (m)</th>\n",
              "      <th>Avg Depth (m)</th>\n",
              "      <th>max/ave</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ER</td>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HUR</td>\n",
              "      <td>229</td>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MIC</td>\n",
              "      <td>281</td>\n",
              "      <td>85</td>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ONT</td>\n",
              "      <td>244</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SUP</td>\n",
              "      <td>406</td>\n",
              "      <td>149</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  lake  Max Depth (m)  Avg Depth (m)  max/ave\n",
              "0   ER             64             19       64\n",
              "1  HUR            229             59       59\n",
              "2  MIC            281             85      281\n",
              "3  ONT            244             86       86\n",
              "4  SUP            406            149      149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    }
  ]
}