Donation_Data_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/wcrowley342/Donordata/blob/main/Donation_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2i7CvHTZdGMC"
      },
      "source": [
        "# Data import & Prep"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ro-rl-PD_Q_"
      },
      "source": [
        "Any kind of data analysis starts with getting hold of some data; lets go ahead and import our data"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "d1a85743-2b02-46a3-84de-2689c5565226"
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        {
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          "data": {
            "text/plain": [
              "                                  campaign  reference#  donorID state country  \\\n",
              "0                     Donate to Unscripted  7043548105  NAME001    NY     USA   \n",
              "1  Unscripted Back to School 2021 Campaign  9247396696  NAME003   NaN     NaN   \n",
              "2  Unscripted Back to School 2021 Campaign  4595351851  NAME004   NaN     NaN   \n",
              "3  Unscripted Back to School 2021 Campaign  5975702880  NAME005    NY     USA   \n",
              "4  Unscripted Back to School 2021 Campaign  1221321935  NAME006    NJ     USA   \n",
              "\n",
              "   method    amount        donation_time   subtype  \\\n",
              "0   check      5.00   2022-01-12 3:58:39  donation   \n",
              "1   venmo  1,000.00  2022-01-11 20:33:54  donation   \n",
              "2  paypal     10.00  2022-01-10 16:50:57  donation   \n",
              "3   venmo    200.00   2022-01-03 2:13:19  donation   \n",
              "4    card     25.00  2022-01-01 16:44:50  donation   \n",
              "\n",
              "                          description  quantity  \n",
              "0  Donation to The Unscripted Project         1  \n",
              "1  Donation to The Unscripted Project         1  \n",
              "2  Donation to The Unscripted Project         1  \n",
              "3  Donation to The Unscripted Project         1  \n",
              "4  Donation to The Unscripted Project         1  "
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              "      <th></th>\n",
              "      <th>campaign</th>\n",
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              "      <th>0</th>\n",
              "      <td>Donate to Unscripted</td>\n",
              "      <td>7043548105</td>\n",
              "      <td>NAME001</td>\n",
              "      <td>NY</td>\n",
              "      <td>USA</td>\n",
              "      <td>check</td>\n",
              "      <td>5.00</td>\n",
              "      <td>2022-01-12 3:58:39</td>\n",
              "      <td>donation</td>\n",
              "      <td>Donation to The Unscripted Project</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>Unscripted Back to School 2021 Campaign</td>\n",
              "      <td>9247396696</td>\n",
              "      <td>NAME003</td>\n",
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              "      <td>Unscripted Back to School 2021 Campaign</td>\n",
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              "      <td>donation</td>\n",
              "      <td>Donation to The Unscripted Project</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Unscripted Back to School 2021 Campaign</td>\n",
              "      <td>5975702880</td>\n",
              "      <td>NAME005</td>\n",
              "      <td>NY</td>\n",
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              "      <td>venmo</td>\n",
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              "      <td>2022-01-03 2:13:19</td>\n",
              "      <td>donation</td>\n",
              "      <td>Donation to The Unscripted Project</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Unscripted Back to School 2021 Campaign</td>\n",
              "      <td>1221321935</td>\n",
              "      <td>NAME006</td>\n",
              "      <td>NJ</td>\n",
              "      <td>USA</td>\n",
              "      <td>card</td>\n",
              "      <td>25.00</td>\n",
              "      <td>2022-01-01 16:44:50</td>\n",
              "      <td>donation</td>\n",
              "      <td>Donation to The Unscripted Project</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 1
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      ],
      "source": [
        "#import necessary packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#use FiveThirtyEight Style\n",
        "import matplotlib.style as style\n",
        "#plt.style.use('fivethirtyeight')\n",
        "\n",
        "#import data\n",
        "donations = pd.read_csv('https://raw.githubusercontent.com/wcrowley342/Donordata/main/Copy%20of%20Donation%20data%20unscripted%20(V7%20cleaned%20anonymous)%20-%20transactions-2022-02-16-641663456.csv')\n",
        "\n",
        "#preview/inspect data\n",
        "donations.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here's a data dictionary of the donations/transaction tables from the fundraising vendor used by Unscripted: https://help.givebutter.com/en/articles/2219206-export-download-transaction-details"
      ],
      "metadata": {
        "id": "FO43IyuMkv01"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that \"quantity\", while is mostly 1, would be greater than 1 for any ticket related events/fundraisers. "
      ],
      "metadata": {
        "id": "Dlbd8dDYqIWU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b8f1a0d-798b-43b6-c9da-b7ec3fb9548e"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 352 entries, 0 to 351\n",
            "Data columns (total 11 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   campaign       352 non-null    object\n",
            " 1   reference#     352 non-null    int64 \n",
            " 2   donorID        352 non-null    object\n",
            " 3   state          301 non-null    object\n",
            " 4   country        312 non-null    object\n",
            " 5   method         341 non-null    object\n",
            " 6   amount         352 non-null    object\n",
            " 7   donation_time  352 non-null    object\n",
            " 8   subtype        352 non-null    object\n",
            " 9   description    352 non-null    object\n",
            " 10  quantity       352 non-null    int64 \n",
            "dtypes: int64(2), object(9)\n",
            "memory usage: 30.4+ KB\n"
          ]
        }
      ],
      "source": [
        "#.info() function: Explore data types and nulls/ NaNs in your data\n",
        "donations.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations.describe()"
      ],
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          "height": 301
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      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "         reference#    quantity\n",
              "count  3.520000e+02  352.000000\n",
              "mean   5.661996e+09    1.068182\n",
              "std    2.543574e+09    0.303651\n",
              "min    1.016118e+09    1.000000\n",
              "25%    3.422276e+09    1.000000\n",
              "50%    6.024192e+09    1.000000\n",
              "75%    7.716132e+09    1.000000\n",
              "max    9.958781e+09    4.000000"
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              "      <td>3.520000e+02</td>\n",
              "      <td>352.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.661996e+09</td>\n",
              "      <td>1.068182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.543574e+09</td>\n",
              "      <td>0.303651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.016118e+09</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.422276e+09</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>50%</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhsJP2UidJSq"
      },
      "source": [
        "# Data cleanup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "gXth9rmsvpKu"
      },
      "outputs": [],
      "source": [
        "#clean up currency formatting \n",
        "donations['amount']=donations['amount'].str.replace(',','')\n",
        "donations['amount'] = donations['amount'].astype('float')\n",
        "#donations['amount']=donations['amount'].str.replace(',','')"
      ]
    },
    {
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            "text/plain": [
              "                                  campaign  donorID state country  method  \\\n",
              "0                     Donate to Unscripted  NAME001    NY     USA   check   \n",
              "1  Unscripted Back to School 2021 Campaign  NAME003   NaN     NaN   venmo   \n",
              "2  Unscripted Back to School 2021 Campaign  NAME004   NaN     NaN  paypal   \n",
              "3  Unscripted Back to School 2021 Campaign  NAME005    NY     USA   venmo   \n",
              "4  Unscripted Back to School 2021 Campaign  NAME006    NJ     USA    card   \n",
              "\n",
              "   amount        donation_time   subtype                         description  \\\n",
              "0     5.0   2022-01-12 3:58:39  donation  Donation to The Unscripted Project   \n",
              "1  1000.0  2022-01-11 20:33:54  donation  Donation to The Unscripted Project   \n",
              "2    10.0  2022-01-10 16:50:57  donation  Donation to The Unscripted Project   \n",
              "3   200.0   2022-01-03 2:13:19  donation  Donation to The Unscripted Project   \n",
              "4    25.0  2022-01-01 16:44:50  donation  Donation to The Unscripted Project   \n",
              "\n",
              "   quantity  \n",
              "0         1  \n",
              "1         1  \n",
              "2         1  \n",
              "3         1  \n",
              "4         1  "
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          "metadata": {},
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      ],
      "source": [
        "#dropping some columns\n",
        "\n",
        "donations = donations.drop(columns=['reference#'])\n",
        "\n",
        "donations.head(5)"
      ]
    },
    {
      "cell_type": "code",
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      "execution_count": 6,
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        {
          "output_type": "stream",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 352 entries, 0 to 351\n",
            "Data columns (total 10 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   campaign       352 non-null    object \n",
            " 1   donorID        352 non-null    object \n",
            " 2   state          301 non-null    object \n",
            " 3   country        312 non-null    object \n",
            " 4   method         341 non-null    object \n",
            " 5   amount         352 non-null    float64\n",
            " 6   donation_time  352 non-null    object \n",
            " 7   subtype        352 non-null    object \n",
            " 8   description    352 non-null    object \n",
            " 9   quantity       352 non-null    int64  \n",
            "dtypes: float64(1), int64(1), object(8)\n",
            "memory usage: 27.6+ KB\n"
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            "text/plain": [
              "            amount    quantity\n",
              "count   352.000000  352.000000\n",
              "mean    177.346591    1.068182\n",
              "std     643.825063    0.303651\n",
              "min       0.000000    1.000000\n",
              "25%      25.000000    1.000000\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 352 entries, 0 to 351\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype         \n",
            "---  ------          --------------  -----         \n",
            " 0   campaign        352 non-null    object        \n",
            " 1   donorID         352 non-null    object        \n",
            " 2   state           301 non-null    object        \n",
            " 3   country         312 non-null    object        \n",
            " 4   method          341 non-null    object        \n",
            " 5   amount          352 non-null    float64       \n",
            " 6   subtype         352 non-null    object        \n",
            " 7   description     352 non-null    object        \n",
            " 8   quantity        352 non-null    int64         \n",
            " 9   donation_times  352 non-null    datetime64[ns]\n",
            " 10  donation_day    352 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2), object(7)\n",
            "memory usage: 30.4+ KB\n"
          ]
        }
      ],
      "source": [
        "#https://www.kite.com/python/answers/how-to-convert-a-pandas-dataframe-column-to-datetime-in-python\n",
        "\n",
        "#conversion efforts\n",
        "donations['donation_times'] = pd.to_datetime(donations[\"donation_time\"]) \n",
        "\n",
        "#adding day of week \n",
        "donations['donation_day'] = donations['donation_times'].dt.dayofweek\n",
        "\n",
        "#dropping old donation_date\n",
        "donations.drop('donation_time', axis=1, inplace=True)\n",
        "\n",
        "donations.head()\n",
        "\n",
        "donations.info()"
      ]
    },
    {
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      "execution_count": 9,
      "metadata": {
        "id": "Vddc5yFKv3Rj",
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "            amount    quantity  donation_day\n",
              "count   352.000000  352.000000    352.000000\n",
              "mean    177.346591    1.068182      2.738636\n",
              "std     643.825063    0.303651      1.811608\n",
              "min       0.000000    1.000000      0.000000\n",
              "25%      25.000000    1.000000      1.000000\n",
              "50%      50.000000    1.000000      2.000000\n",
              "75%      84.750000    1.000000      4.000000\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "#get the summary statistics for the numerical varaibles in your dataset \n",
        "\n",
        "donations.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations.head()"
      ],
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        "id": "JvZZimIYmR7C",
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  campaign  donorID state country  method  \\\n",
              "0                     Donate to Unscripted  NAME001    NY     USA   check   \n",
              "1  Unscripted Back to School 2021 Campaign  NAME003   NaN     NaN   venmo   \n",
              "2  Unscripted Back to School 2021 Campaign  NAME004   NaN     NaN  paypal   \n",
              "3  Unscripted Back to School 2021 Campaign  NAME005    NY     USA   venmo   \n",
              "4  Unscripted Back to School 2021 Campaign  NAME006    NJ     USA    card   \n",
              "\n",
              "   amount   subtype                         description  quantity  \\\n",
              "0     5.0  donation  Donation to The Unscripted Project         1   \n",
              "1  1000.0  donation  Donation to The Unscripted Project         1   \n",
              "2    10.0  donation  Donation to The Unscripted Project         1   \n",
              "3   200.0  donation  Donation to The Unscripted Project         1   \n",
              "4    25.0  donation  Donation to The Unscripted Project         1   \n",
              "\n",
              "       donation_times  donation_day  \n",
              "0 2022-01-12 03:58:39             2  \n",
              "1 2022-01-11 20:33:54             1  \n",
              "2 2022-01-10 16:50:57             0  \n",
              "3 2022-01-03 02:13:19             0  \n",
              "4 2022-01-01 16:44:50             5  "
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    {
      "cell_type": "markdown",
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        "id": "c_F56sqMiWLr"
      },
      "source": [
        "\n",
        "# (Who) most frequent / profitable donors"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "under_1k = donations[donations['amount'] < 1000]\n",
        "sns.histplot(data=under_1k, x='amount', binwidth=30)"
      ],
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      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7facf8479610>"
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        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.histogram(donations, x=\"amount\", marginal=\"rug\", nbins=20,\n",
        "                   labels={\"amount\":\"$\"},\n",
        "             histnorm='percent', width=550, height=400)\n",
        "fig.data[0].marker.line =  dict( color = 'black',width = 1)\n",
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tkO64IzdFiW8",
        "outputId": "fc379d75-5abb-4169-af2a-8fd48bf49baa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "code",
      "source": [
        "donations[['amount']].describe()"
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            "text/plain": [
              "            amount\n",
              "count   352.000000\n",
              "mean    177.346591\n",
              "std     643.825063\n",
              "min       0.000000\n",
              "25%      25.000000\n",
              "50%      50.000000\n",
              "75%      84.750000\n",
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              "      <th>amount</th>\n",
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              "      <th>count</th>\n",
              "      <td>352.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>643.825063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>84.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5319.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d78422c3-51ef-44d0-b43a-b70965cda13d')\"\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",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </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-d78422c3-51ef-44d0-b43a-b70965cda13d 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-d78422c3-51ef-44d0-b43a-b70965cda13d');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations.amount.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oraElumWvRvC",
        "outputId": "94f3c52b-c6b1-4af7-a43c-03d8eb7ebf5b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "62426.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGGAZpnfXBP9",
        "outputId": "37cc99fe-5354-4f10-ad26-878efbea88a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NAME018    7\n",
              "NAME056    5\n",
              "NAME025    5\n",
              "NAME036    5\n",
              "NAME030    4\n",
              "Name: donorID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#top 5 donors based on numbers of donations\n",
        "donations['donorID'].value_counts()[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nQm51EAkYXp1",
        "outputId": "1787d743-49f3-418a-b7fd-0e608aa492d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "donors_graph = donations['donorID'].value_counts()[:5].plot(kind='bar')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#top 5 donors\n",
        "\n",
        "top_five_donors = donations.groupby(['donorID']).sum().sort_values('amount',ascending=False)\n",
        "top_five_donors = top_five_donors.drop(columns=['donation_day'])\n",
        "top_five_donors.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vYJurkTELtnv",
        "outputId": "1cf7123e-5a97-452e-8d45-b24c25dc1414"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         amount  quantity\n",
              "donorID                  \n",
              "NAME059  7130.0         5\n",
              "NAME018  6738.0         7\n",
              "NAME257  5319.0         1\n",
              "NAME092  5000.0         1\n",
              "NAME136  5000.0         1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c26cbca-aee3-4f1f-9ada-0f0bb3afb692\">\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>amount</th>\n",
              "      <th>quantity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>donorID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NAME059</th>\n",
              "      <td>7130.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAME018</th>\n",
              "      <td>6738.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAME257</th>\n",
              "      <td>5319.0</td>\n",
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              "    <tr>\n",
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              "      <th>NAME136</th>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "unstacked_top_five_donors = top_five_donors.reset_index()"
      ],
      "metadata": {
        "id": "t51uBd03N-ST"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "unstacked_top_five_donors.head()"
      ],
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        "colab": {
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        "id": "-V9kGHcLOGOD",
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      "execution_count": 19,
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        {
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          "data": {
            "text/plain": [
              "   donorID  amount  quantity\n",
              "0  NAME059  7130.0         5\n",
              "1  NAME018  6738.0         7\n",
              "2  NAME257  5319.0         1\n",
              "3  NAME092  5000.0         1\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "top_five_donors_graph = unstacked_top_five_donors.drop(columns=['quantity'])"
      ],
      "metadata": {
        "id": "mUoaYggkPFcE"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_graph = top_five_donors_graph.head(5)\n",
        "final_graph"
      ],
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          "height": 0
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        "id": "SPO9I4fTPLnw",
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      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   donorID  amount\n",
              "0  NAME059  7130.0\n",
              "1  NAME018  6738.0\n",
              "2  NAME257  5319.0\n",
              "3  NAME092  5000.0\n",
              "4  NAME136  5000.0"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_graph.set_index('donorID')"
      ],
      "metadata": {
        "colab": {
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          "height": 0
        },
        "id": "FDeIaS_nQK_k",
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      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         amount\n",
              "donorID        \n",
              "NAME059  7130.0\n",
              "NAME018  6738.0\n",
              "NAME257  5319.0\n",
              "NAME092  5000.0\n",
              "NAME136  5000.0"
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2edb05d4-c479-4d9e-9561-4396fdb9cb83')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "          document.querySelector('#df-2edb05d4-c479-4d9e-9561-4396fdb9cb83 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-2edb05d4-c479-4d9e-9561-4396fdb9cb83');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_graph.reset_index()\n",
        "final_graph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6Iftw9_YQbGH",
        "outputId": "a5af9d3b-760b-43a7-dd2e-345a3cedcb97"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   donorID  amount\n",
              "0  NAME059  7130.0\n",
              "1  NAME018  6738.0\n",
              "2  NAME257  5319.0\n",
              "3  NAME092  5000.0\n",
              "4  NAME136  5000.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b3081d6-73f4-4734-a1d6-83f5e266324f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>donorID</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NAME059</td>\n",
              "      <td>7130.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NAME018</td>\n",
              "      <td>6738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NAME257</td>\n",
              "      <td>5319.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NAME092</td>\n",
              "      <td>5000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NAME136</td>\n",
              "      <td>5000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b3081d6-73f4-4734-a1d6-83f5e266324f')\"\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",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </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",
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              "      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-9b3081d6-73f4-4734-a1d6-83f5e266324f 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-9b3081d6-73f4-4734-a1d6-83f5e266324f');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_graph.plot(kind='bar')\n",
        "plt.hlines(177, xmin=0, xmax=4, linestyles='dashed', color = 'red')\n",
        "plt.title(\"Top Donors\")\n",
        "plt.legend(['avg. amount donated'], loc=1)\n",
        "plt.xticks([0,1,2,3,4], [\"NAME059\",'NAME018','NAME257','NAME092','NAME136'])\n",
        "plt.xticks(rotation=45)\n",
        "plt.ylabel(\"Donations($)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "03A_cIg0MwwN",
        "outputId": "4ba7f63f-baef-478e-ce34-485ebfdbb2da"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Donations($)')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(x=\"amount\", kind=\"swarm\", data=donations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fwKJ5kgM3FTA",
        "outputId": "f5b81c72-baeb-4a08-e289-cc43cf8d021d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning:\n",
            "\n",
            "62.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7facf4c6f8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TAdWQb080Dj8"
      },
      "source": [
        "# (Where) Map of states"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations['state'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoLyE7wvW665",
        "outputId": "c71b4fbf-b96b-4176-fa1f-dee0e4a515a7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NY           124\n",
              "PA            67\n",
              "CA            41\n",
              "NJ            16\n",
              "DC             7\n",
              "MD             4\n",
              "TX             3\n",
              "VIC            3\n",
              "CT             3\n",
              "GA             3\n",
              "MN             3\n",
              "WA             2\n",
              "VA             2\n",
              "Victoria       2\n",
              "London         2\n",
              "FL             2\n",
              "NSW            2\n",
              "Hong Kong      1\n",
              "ND             1\n",
              "OK             1\n",
              "AZ             1\n",
              "Shanghai       1\n",
              "IN             1\n",
              "MA             1\n",
              "ON             1\n",
              "INDIANA        1\n",
              "NC             1\n",
              "England        1\n",
              "DE             1\n",
              "MI             1\n",
              "AK             1\n",
              "RI             1\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations['country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXhgtmU8XGfm",
        "outputId": "a5751f34-02c9-403e-bc96-152215e850e5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA    297\n",
              "AUS      7\n",
              "CHN      3\n",
              "GBR      3\n",
              "CAN      1\n",
              "HKG      1\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#querying top countries\n",
        "top_countries_sorted = donations.groupby(['country']).sum().sort_values('amount',ascending=False)\n",
        "print(top_countries_sorted[['amount', 'quantity']])                                                          \n",
        "                                                                         "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw6a-_ZuZq4r",
        "outputId": "f26ca1bd-d795-45f5-8fcc-d9c0d3ad4747"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          amount  quantity\n",
            "country                   \n",
            "USA      54571.0       313\n",
            "CHN       4200.0         3\n",
            "AUS        680.0         7\n",
            "GBR        280.0         3\n",
            "HKG        100.0         1\n",
            "CAN         50.0         1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#querying top states\n",
        "top_states_sorted = donations.groupby(['state']).sum().sort_values('amount',ascending=False)\n",
        "print(top_states_sorted[['amount', 'quantity']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcYreQ13niVD",
        "outputId": "d33c6e3e-7b07-45bf-fef9-98f7454b981f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            amount  quantity\n",
            "state                       \n",
            "NY         19045.0       134\n",
            "PA         14566.0        67\n",
            "DE          5000.0         1\n",
            "CA          2910.0        42\n",
            "NJ          1925.0        17\n",
            "AK          1000.0         1\n",
            "DC           440.0         7\n",
            "VIC          305.0         3\n",
            "MD           285.0         4\n",
            "NSW          260.0         2\n",
            "London       260.0         2\n",
            "Shanghai     250.0         1\n",
            "TX           225.0         4\n",
            "GA           205.0         3\n",
            "MN           175.0         3\n",
            "Victoria     115.0         2\n",
            "Hong Kong    100.0         1\n",
            "WA            85.0         5\n",
            "CT            70.0         3\n",
            "VA            50.0         2\n",
            "FL            50.0         2\n",
            "RI            50.0         1\n",
            "ON            50.0         1\n",
            "MI            35.0         1\n",
            "AZ            25.0         1\n",
            "IN            25.0         1\n",
            "INDIANA       25.0         1\n",
            "MA            25.0         1\n",
            "ND            25.0         1\n",
            "England       20.0         1\n",
            "NC            15.0         1\n",
            "OK            10.0         1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEJ9SmvOV3Fe",
        "outputId": "4aec52ad-ba43-4468-81d0-7548111deaf7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NY    124\n",
              "PA     67\n",
              "CA     41\n",
              "NJ     16\n",
              "DC      7\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "#top 5 states based on numbers of donations\n",
        "donations['state'].value_counts()[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ncoY3TJKtdw9"
      },
      "outputs": [],
      "source": [
        "map_data = pd.DataFrame(donations['state'].value_counts())"
      ]
    },
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      "execution_count": 32,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    state\n",
              "NY    124\n",
              "PA     67\n",
              "CA     41\n",
              "NJ     16\n",
              "DC      7"
            ],
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      "metadata": {
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        "map_data = map_data.reset_index()\n"
      ]
    },
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      "execution_count": 34,
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        {
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32 entries, 0 to 31\n",
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            " 1   state   32 non-null     int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 640.0+ bytes\n"
          ]
        }
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      "source": [
        "map_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
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      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  index  state\n",
              "0    NY    124\n",
              "1    PA     67\n",
              "2    CA     41\n",
              "3    NJ     16\n",
              "4    DC      7"
            ],
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              "                                                     [key], {});\n",
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            " 0   state   32 non-null     object\n",
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            "dtypes: int64(1), object(1)\n",
            "memory usage: 640.0+ bytes\n"
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    {
      "cell_type": "code",
      "execution_count": 38,
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              "  state  count\n",
              "0    NY    124\n",
              "1    PA     67\n",
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              "4    DC      7"
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              "      <th>4</th>\n",
              "      <td>DC</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-7ecd4d15-f7a7-4ab0-b363-936594c67abe');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "map_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIsG39b2wx7_",
        "outputId": "ecfc2970-7a4d-4247-f16c-afccdeef05f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting plotly-geo\n",
            "  Downloading plotly_geo-1.0.0-py3-none-any.whl (23.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 23.7 MB 1.5 MB/s \n",
            "\u001b[?25hInstalling collected packages: plotly-geo\n",
            "Successfully installed plotly-geo-1.0.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting geopandas\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 5.0 MB/s \n",
            "\u001b[?25hCollecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 16.7 MB 42.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.2)\n",
            "Collecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 47.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2022.5.18.1)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyshp==1.2.10\n",
            "  Downloading pyshp-1.2.10.tar.gz (176 kB)\n",
            "\u001b[K     |████████████████████████████████| 176 kB 4.9 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyshp\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-1.2.10-py3-none-any.whl size=20493 sha256=55d0f5a1fd7e435cb1cfa54cf705ebe94a6ac9a96b3224d4c2b974d292b20983\n",
            "  Stored in directory: /root/.cache/pip/wheels/aa/cb/ed/c4af42e7718157f9a76dea841c585e0577742ffed5a5b17d32\n",
            "Successfully built pyshp\n",
            "Installing collected packages: pyshp\n",
            "Successfully installed pyshp-1.2.10\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shapely==1.6.3\n",
            "  Downloading Shapely-1.6.3.tar.gz (223 kB)\n",
            "\u001b[K     |████████████████████████████████| 223 kB 5.2 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: shapely\n",
            "  Building wheel for shapely (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shapely: filename=Shapely-1.6.3-cp37-cp37m-linux_x86_64.whl size=637627 sha256=67668cd07c3910316b89356c087fc11c1b255d070a83f2291b77500d3f2a6776\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/9f/1e/de1b1c770313c6a9838cc9c4da4c59f5b2db78b34078d29d01\n",
            "Successfully built shapely\n",
            "Installing collected packages: shapely\n",
            "  Attempting uninstall: shapely\n",
            "    Found existing installation: Shapely 1.8.2\n",
            "    Uninstalling Shapely-1.8.2:\n",
            "      Successfully uninstalled Shapely-1.8.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed shapely-1.6.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting chart_studio\n",
            "  Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\n",
            "\u001b[K     |████████████████████████████████| 64 kB 2.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from chart_studio) (1.15.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from chart_studio) (5.5.0)\n",
            "Collecting retrying>=1.3.3\n",
            "  Downloading retrying-1.3.3.tar.gz (10 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->chart_studio) (8.0.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2022.5.18.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2.10)\n",
            "Building wheels for collected packages: retrying\n",
            "  Building wheel for retrying (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11447 sha256=057fe4cb5f37099a132d0971969408f55017a5edbe34591ca167b1109df79047\n",
            "  Stored in directory: /root/.cache/pip/wheels/f9/8d/8d/f6af3f7f9eea3553bc2fe6d53e4b287dad18b06a861ac56ddf\n",
            "Successfully built retrying\n",
            "Installing collected packages: retrying, chart-studio\n",
            "Successfully installed chart-studio-1.1.0 retrying-1.3.3\n"
          ]
        }
      ],
      "source": [
        "!pip install plotly\n",
        "!pip install plotly-geo\n",
        "!pip install geopandas --upgrade\n",
        "!pip install pyshp==1.2.10\n",
        "!pip install shapely==1.6.3\n",
        "!pip install chart_studio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import chart_studio\n",
        "username='wcrowley'\n",
        "api_key='dAgMw0Z9Yycu8PpYy2zZ'\n",
        "chart_studio.tools.set_credentials_file(username=username,\n",
        "                                        api_key=api_key)"
      ],
      "metadata": {
        "id": "7s9B4GiD5krb"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "T0eS2wA6uxLw"
      },
      "outputs": [],
      "source": [
        "import plotly as py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "PRV6XujF4leF"
      },
      "outputs": [],
      "source": [
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "bl7eosjV0bIZ"
      },
      "outputs": [],
      "source": [
        "data = dict(type='choropleth', \n",
        "            colorscale = 'Blues',\n",
        "            z = map_data['count'],\n",
        "            locations = map_data['state'],\n",
        "            locationmode = 'USA-states',\n",
        "            text=map_data['state'],\n",
        "            marker = dict(line = dict(color = 'rgb(255,255,255)', width=1)),\n",
        "            colorbar = {'title' : 'count of donations'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "sl10xxt_1XkD"
      },
      "outputs": [],
      "source": [
        "layout = dict(title = 'US Donations Maps',\n",
        "              geo = dict(scope = 'usa',\n",
        "                         showlakes = True,\n",
        "                         lakecolor = 'rgb(85,173,240)'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "joUrp4W01ugV"
      },
      "outputs": [],
      "source": [
        "choromap = go.Figure(data = [data], layout=layout)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TQ4f41dZ4wlD",
        "outputId": "6e404667-240b-449c-8773-6371383c10e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"93442479-f73e-48d4-b7bb-1e532a82061e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"93442479-f73e-48d4-b7bb-1e532a82061e\")) {                    Plotly.newPlot(                        \"93442479-f73e-48d4-b7bb-1e532a82061e\",                        [{\"colorbar\":{\"title\":{\"text\":\"count of 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          },
          "metadata": {}
        }
      ],
      "source": [
        "choromap.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import chart_studio.plotly as cs\n",
        "cs.plot(choromap, filename=\"donations_map\", auto_open = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pIqTrzm56ptg",
        "outputId": "670c00be-2728-47c0-a3ec-b90e1deeb557"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://plotly.com/~wcrowley/77/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SbFoKYd-3xM_",
        "outputId": "3f32e5a4-04b3-41d9-dc92-9dad60d55ce1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('df414b37-88b1-4ac0-ad48-c09a052671ba');\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#instructions https://plotly.com/python/county-choropleth/\n",
        "\n",
        "fig = px.choropleth(locations = map_data['state'], locationmode=\"USA-states\", color = map_data['count'], scope=\"usa\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PP29RzmGU66"
      },
      "source": [
        "# (How) Preferred Donation Method \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations.boxplot(column='amount', by='method', vert=False, grid=False,\n",
        "                    sym='k.')\n",
        "plt.suptitle('')\n",
        "plt.title('donations by type')\n",
        "plt.xlabel('amount')\n",
        "plt.ylabel('method')\n",
        "plt.box"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "E3CIopuOFIal",
        "outputId": "03b9efab-7fbd-45e9-9fe6-74863d94a348"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning:\n",
            "\n",
            "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.box>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "px.strip(donations, x=\"method\", y=\"amount\", )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Pw6esEbyGqkE",
        "outputId": "c3af157a-7e58-42c1-c811-328bc5f2ee75"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "The strip plot above is a preferrable visualization. We can still compare the groups, but we can also see the exact values in each group. We can see there are only three donations in the check group, and the impression of a skewed distribution based on the corresponding box plot above would be reading too much into the appearance of the box"
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            "text/plain": [
              "                     amount  quantity  donation_day\n",
              "method                                             \n",
              "check           1673.333333  1.000000      3.000000\n",
              "ach              500.000000  1.000000      3.000000\n",
              "venmo            251.196850  1.007874      2.220472\n",
              "card             157.455224  1.074627      3.104478\n",
              "digital_wallet    53.690476  1.142857      2.952381\n",
              "paypal            48.529412  1.117647      3.058824"
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              "      <th></th>\n",
              "      <th>amount</th>\n",
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              "      <td>1673.333333</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>ach</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>venmo</th>\n",
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              "      <td>1.007874</td>\n",
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              "      <th>card</th>\n",
              "      <td>157.455224</td>\n",
              "      <td>1.074627</td>\n",
              "      <td>3.104478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>digital_wallet</th>\n",
              "      <td>53.690476</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>2.952381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paypal</th>\n",
              "      <td>48.529412</td>\n",
              "      <td>1.117647</td>\n",
              "      <td>3.058824</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "#group by method, averages\n",
        "\n",
        "donations.groupby(['method']).mean().sort_values('amount',ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#group by method, sums\n",
        "\n",
        "donations.groupby(['method']).sum().sort_values('amount',ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "pweuQbYEot6P",
        "outputId": "edfad38f-c91a-474a-ce1a-500badad172b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 amount  quantity  donation_day\n",
              "method                                         \n",
              "venmo           31902.0       128           282\n",
              "card            21099.0       144           416\n",
              "check            5020.0         3             9\n",
              "digital_wallet   2255.0        48           124\n",
              "paypal           1650.0        38           104\n",
              "ach               500.0         1             3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96499be1-73af-4a80-b7ea-62a188eca1a1\">\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",
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              "    }\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>amount</th>\n",
              "      <th>quantity</th>\n",
              "      <th>donation_day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>method</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>venmo</th>\n",
              "      <td>31902.0</td>\n",
              "      <td>128</td>\n",
              "      <td>282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>card</th>\n",
              "      <td>21099.0</td>\n",
              "      <td>144</td>\n",
              "      <td>416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>check</th>\n",
              "      <td>5020.0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>digital_wallet</th>\n",
              "      <td>2255.0</td>\n",
              "      <td>48</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paypal</th>\n",
              "      <td>1650.0</td>\n",
              "      <td>38</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ach</th>\n",
              "      <td>500.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96499be1-73af-4a80-b7ea-62a188eca1a1');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "160eKB90WfN8",
        "outputId": "7e25b75b-96c2-42f7-b7e8-3ae3081282ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "method\n",
              "ach                 1\n",
              "card              134\n",
              "check               3\n",
              "digital_wallet     42\n",
              "paypal             34\n",
              "venmo             127\n",
              "Name: campaign, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "#breakdown of donations by type\n",
        "donations_by_method = donations.groupby('method').campaign.count()\n",
        "donations_by_method"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(Counter(donations['method']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EryQ_hBcJ03D",
        "outputId": "89b3e126-c74c-4e2b-e41f-4bfa5c3d6c61"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'card': 134, 'venmo': 127, 'digital_wallet': 42, 'paypal': 34, nan: 11, 'check': 3, 'ach': 1})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "method_type = donations.groupby('method').agg('count')\n",
        "print(method_type)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05x0DxyqKKhA",
        "outputId": "2dd14fd3-945b-4901-f934-f38cd8c93379"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                campaign  donorID  state  country  amount  subtype  \\\n",
            "method                                                               \n",
            "ach                    1        1      0        0       1        1   \n",
            "card                 134      134    132      134     134      134   \n",
            "check                  3        3      2        3       3        3   \n",
            "digital_wallet        42       42     42       42      42       42   \n",
            "paypal                34       34     18       18      34       34   \n",
            "venmo                127      127    107      115     127      127   \n",
            "\n",
            "                description  quantity  donation_times  donation_day  \n",
            "method                                                               \n",
            "ach                       1         1               1             1  \n",
            "card                    134       134             134           134  \n",
            "check                     3         3               3             3  \n",
            "digital_wallet           42        42              42            42  \n",
            "paypal                   34        34              34            34  \n",
            "venmo                   127       127             127           127  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.ticker as ticker\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib as mpl\n",
        "from matplotlib.gridspec import GridSpec\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "tFF2l9COKg1r"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "method_labels = method_type.campaign.sort_values().index \n",
        "method_counts = method_type.campaign.sort_values()"
      ],
      "metadata": {
        "id": "IRsmQ9qbKiug"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.figure(1, figsize=(20,10)) \n",
        "#the_grid = GridSpec(2, 2)"
      ],
      "metadata": {
        "id": "CRXpD3kaKxQR"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cmap = plt.get_cmap('Spectral')\n",
        "colors = [cmap(i) for i in np.linspace(0, 1, 8)]"
      ],
      "metadata": {
        "id": "KWsEPqkOKznQ"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1, figsize=(40,30)) \n",
        "the_grid = GridSpec(2, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "47-CWfZRLE-C",
        "outputId": "0342d7c4-f571-45e1-94f2-d51181a89962"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x2160 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_labels = '','Check','Venmo', 'Paypal', 'Digital_Wallet', 'Card'\n",
        "#my_explode = (0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)\n",
        "plt.pie(method_counts, shadow=True, labels=my_labels,autopct='%1.1f%%')\n",
        "plt.title('Donation Methods')\n",
        "#plt.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "4d3r0C03s3Kc",
        "outputId": "87d06eb4-b287-44cf-ed9a-358c24d69660"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "two_br = donations.groupby('method').mean().reset_index() \n",
        "fig = px.bar(two_br.sort_values('method', ascending=False),\n",
        "            x='amount', y='method')\n",
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qeTX5AEAKYoN",
        "outputId": "ca15d1bb-0d01-47fa-a02d-cc6861e4fc9b"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0WgOr27r2l4F"
      },
      "source": [
        "# (When) Day of Week + Campaign Chart"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(donations['amount'], donations['donation_day'])\n",
        "plt.title('Scatter Plot Example')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ct2EFnuVHXuv",
        "outputId": "b5d10465-be6c-4f8b-af1d-36f1a97d10c1"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYGpGa02TNxF",
        "outputId": "b931622e-be4b-4655-8db8-bd323dcbffeb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "donation_day\n",
            "1    325.0\n",
            "2     20.0\n",
            "3    155.0\n",
            "4     50.0\n",
            "5      5.0\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "Donate_to_Unscripted = donations[donations['campaign'] == 'Donate to Unscripted'].groupby(['donation_day'])['amount'].sum()\n",
        "print(Donate_to_Unscripted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9UD079uVB_F",
        "outputId": "e8b61563-b309-4caa-d94f-05570d2ad301"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "donation_day\n",
            "0    155.0\n",
            "1     95.0\n",
            "2    190.0\n",
            "3    220.0\n",
            "5    190.0\n",
            "6    280.0\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "Trivia_Night = donations[donations['campaign'] == 'The Unscripted Project Trivia Night'].groupby(['donation_day'])['amount'].sum()\n",
        "print(Trivia_Night)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgPXAyzwU02Y",
        "outputId": "c70fe75b-495d-4dc5-cc67-b202ca445cdf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "donation_day\n",
            "0    9767.0\n",
            "1    3170.0\n",
            "2    2232.0\n",
            "3    2288.0\n",
            "4     830.0\n",
            "5     460.0\n",
            "6    5975.0\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "Fall_Campaign_2020 = donations[donations['campaign'] == 'Unscripted 20/2020 Fall Campaign'].groupby(['donation_day'])['amount'].sum()\n",
        "print(Fall_Campaign_2020)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KeruwkH_VrF_",
        "outputId": "504f52b1-c4d2-4172-daea-1bc101668e1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "donation_day\n",
            "0     70.0\n",
            "1    140.0\n",
            "2    140.0\n",
            "3     70.0\n",
            "4    435.0\n",
            "5    515.0\n",
            "6    505.0\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "Wrap_Party_2021 = donations[donations['campaign'] == 'Unscripted 2021 Fundraiser Wrap Party'].groupby(['donation_day'])['amount'].sum()\n",
        "print(Wrap_Party_2021)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1z6uDF3qV0Xg",
        "outputId": "0d6ea000-246d-4c44-882c-43d3bc6ae738"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "donation_day\n",
            "0     3325.0\n",
            "1    19645.0\n",
            "2     3024.0\n",
            "3     2975.0\n",
            "4     1330.0\n",
            "5      200.0\n",
            "6     3645.0\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "Back_to_School_2021 = donations[donations['campaign'] == 'Unscripted Back to School 2021 Campaign'].groupby(['donation_day'])['amount'].sum()\n",
        "print(Back_to_School_2021)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pHRMdk9RWUt4",
        "outputId": "9331baba-e3db-4d7e-eb50-6a69831bc415"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(Back_to_School_2021)\n",
        "plt.plot(Wrap_Party_2021)\n",
        "plt.plot(Fall_Campaign_2020)\n",
        "plt.plot(Trivia_Night)\n",
        "plt.title('Donations by Day of Week')\n",
        "#plt.xlabel('day of week')\n",
        "plt.xticks([0,1,2,3,4,5,6], [\"Sunday\",'Monday','Tuesday','Wednesday','Thursday','Friday','Saturday'])\n",
        "plt.xticks(rotation=45)\n",
        "str_day_list = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']\n",
        "plt.ylabel('Donations ($)')\n",
        "plt.legend(loc = 'upper right', labels=[\"Back to School 2021\",'Wrap Party 2021','Fall Campaign 2020','Trivia Night'] )\n",
        "plt.style.use('fast')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations['donation_times']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylHdartMqMHy",
        "outputId": "8c5f7688-6676-4cae-db87-d31c81bb4bbb"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2022-01-12 03:58:39\n",
              "1     2022-01-11 20:33:54\n",
              "2     2022-01-10 16:50:57\n",
              "3     2022-01-03 02:13:19\n",
              "4     2022-01-01 16:44:50\n",
              "              ...        \n",
              "347   2020-11-11 04:37:53\n",
              "348   2020-11-11 04:05:53\n",
              "349   2020-11-11 04:02:51\n",
              "350   2020-11-11 03:58:27\n",
              "351   2020-11-11 03:29:22\n",
              "Name: donation_times, Length: 352, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "donations['donation_times'].dt.year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhL2r_A6qrgj",
        "outputId": "32aef50d-327b-441b-8f22-cbbbc8710936"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2022\n",
              "1      2022\n",
              "2      2022\n",
              "3      2022\n",
              "4      2022\n",
              "       ... \n",
              "347    2020\n",
              "348    2020\n",
              "349    2020\n",
              "350    2020\n",
              "351    2020\n",
              "Name: donation_times, Length: 352, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='donation_day', data=donations)\n",
        "\n",
        "# set xticklabels to be the day of the week\n",
        "plt.gca().set_xticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ykIpklOorW6z",
        "outputId": "65c99636-f806-4fea-b462-6655d354bb9e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 4))\n",
        "sns.stripplot(x=\"donation_day\", y=\"amount\", data=donations);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UZ6c1toUTWWL",
        "outputId": "aa46a5be-d856-4d84-8c29-e17e7c89b1fc"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0VpjU-kjVzZ"
      },
      "source": [
        "# (What) Comparing Campaigns / Group by"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "1J_m114UbloB"
      },
      "outputs": [],
      "source": [
        "campaigns = donations.groupby(by=['campaign'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "wZbFyuZvbXXu",
        "outputId": "e5830bb6-9345-46b8-c610-afc63a24984a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             amount         quantity         \\\n",
              "                                               mean median      mean median   \n",
              "campaign                                                                      \n",
              "Donate to Unscripted                      55.500000   25.0  1.000000    1.0   \n",
              "The Unscripted Project Trivia Night       34.242424   30.0  1.454545    1.0   \n",
              "Unscripted 20/2020 Fall Campaign         171.680556   50.0  1.000000    1.0   \n",
              "Unscripted 2021 Fundraiser Wrap Party     37.500000   35.0  1.180000    1.0   \n",
              "Unscripted Back to School 2021 Campaign  296.904348   65.0  1.000000    1.0   \n",
              "\n",
              "                                                       donation_times  \\\n",
              "                                                                 mean   \n",
              "campaign                                                                \n",
              "Donate to Unscripted                    2021-07-22 00:24:49.400000000   \n",
              "The Unscripted Project Trivia Night     2021-04-29 16:24:42.454545408   \n",
              "Unscripted 20/2020 Fall Campaign        2020-11-22 17:51:00.402777856   \n",
              "Unscripted 2021 Fundraiser Wrap Party   2021-12-08 22:29:03.700000000   \n",
              "Unscripted Back to School 2021 Campaign 2021-11-30 22:05:41.226087168   \n",
              "\n",
              "                                                                donation_day  \\\n",
              "                                                         median         mean   \n",
              "campaign                                                                       \n",
              "Donate to Unscripted                    2021-07-29 03:07:18.500     2.600000   \n",
              "The Unscripted Project Trivia Night     2021-04-29 15:03:27.000     3.636364   \n",
              "Unscripted 20/2020 Fall Campaign        2020-11-20 08:25:32.000     2.361111   \n",
              "Unscripted 2021 Fundraiser Wrap Party   2021-12-10 01:17:14.500     3.580000   \n",
              "Unscripted Back to School 2021 Campaign 2021-11-30 17:00:27.000     2.600000   \n",
              "\n",
              "                                                \n",
              "                                        median  \n",
              "campaign                                        \n",
              "Donate to Unscripted                       2.5  \n",
              "The Unscripted Project Trivia Night        3.0  \n",
              "Unscripted 20/2020 Fall Campaign           2.0  \n",
              "Unscripted 2021 Fundraiser Wrap Party      4.0  \n",
              "Unscripted Back to School 2021 Campaign    2.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7755cdfc-2931-4354-88e3-d7b142955205\">\n",
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              "      <th colspan=\"2\" halign=\"left\">amount</th>\n",
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              "      <th>mean</th>\n",
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              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
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              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Donate to Unscripted</th>\n",
              "      <td>55.500000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2021-07-22 00:24:49.400000000</td>\n",
              "      <td>2021-07-29 03:07:18.500</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Unscripted Project Trivia Night</th>\n",
              "      <td>34.242424</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.454545</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2021-04-29 16:24:42.454545408</td>\n",
              "      <td>2021-04-29 15:03:27.000</td>\n",
              "      <td>3.636364</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted 20/2020 Fall Campaign</th>\n",
              "      <td>171.680556</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2020-11-22 17:51:00.402777856</td>\n",
              "      <td>2020-11-20 08:25:32.000</td>\n",
              "      <td>2.361111</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted 2021 Fundraiser Wrap Party</th>\n",
              "      <td>37.500000</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1.180000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2021-12-08 22:29:03.700000000</td>\n",
              "      <td>2021-12-10 01:17:14.500</td>\n",
              "      <td>3.580000</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted Back to School 2021 Campaign</th>\n",
              "      <td>296.904348</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2021-11-30 22:05:41.226087168</td>\n",
              "      <td>2021-11-30 17:00:27.000</td>\n",
              "      <td>2.600000</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "campaigns.agg(['mean','median'])"
      ]
    },
    {
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            "text/plain": [
              "                                             amount  quantity  donation_day\n",
              "campaign                                                                   \n",
              "Unscripted Back to School 2021 Campaign  296.904348  1.000000      2.600000\n",
              "Unscripted 20/2020 Fall Campaign         171.680556  1.000000      2.361111\n",
              "Donate to Unscripted                      55.500000  1.000000      2.600000\n",
              "Unscripted 2021 Fundraiser Wrap Party     37.500000  1.180000      3.580000\n",
              "The Unscripted Project Trivia Night       34.242424  1.454545      3.636364"
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              "      <td>2.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <th>Unscripted 2021 Fundraiser Wrap Party</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "#group campaign, show averages \n",
        "\n",
        "donations.groupby(['campaign']).mean().sort_values('amount',ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#which campaigns generated the most \n",
        "\n",
        "best_campaigns = donations.groupby('campaign')['amount'].sum()\n",
        "best_campaigns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8RE4alAmdpk",
        "outputId": "85db6105-2acd-44c7-aaef-b3c174244dee"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "campaign\n",
              "Donate to Unscripted                         555.0\n",
              "The Unscripted Project Trivia Night         1130.0\n",
              "Unscripted 20/2020 Fall Campaign           24722.0\n",
              "Unscripted 2021 Fundraiser Wrap Party       1875.0\n",
              "Unscripted Back to School 2021 Campaign    34144.0\n",
              "Name: amount, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_campaign_sorted = donations.groupby(['campaign']).sum().sort_values('amount',ascending=False)\n",
        "\n",
        "best_campaign_sorted"
      ],
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      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          amount  quantity  donation_day\n",
              "campaign                                                                \n",
              "Unscripted Back to School 2021 Campaign  34144.0       115           299\n",
              "Unscripted 20/2020 Fall Campaign         24722.0       144           340\n",
              "Unscripted 2021 Fundraiser Wrap Party     1875.0        59           179\n",
              "The Unscripted Project Trivia Night       1130.0        48           120\n",
              "Donate to Unscripted                       555.0        10            26"
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 78
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = best_campaign_sorted.assign(avg_donation = best_campaign_sorted.amount / best_campaign_sorted.quantity)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "5HerG5GDIzSR",
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      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          amount  quantity  donation_day  \\\n",
              "campaign                                                                   \n",
              "Unscripted Back to School 2021 Campaign  34144.0       115           299   \n",
              "Unscripted 20/2020 Fall Campaign         24722.0       144           340   \n",
              "Unscripted 2021 Fundraiser Wrap Party     1875.0        59           179   \n",
              "The Unscripted Project Trivia Night       1130.0        48           120   \n",
              "Donate to Unscripted                       555.0        10            26   \n",
              "\n",
              "                                         avg_donation  \n",
              "campaign                                               \n",
              "Unscripted Back to School 2021 Campaign    296.904348  \n",
              "Unscripted 20/2020 Fall Campaign           171.680556  \n",
              "Unscripted 2021 Fundraiser Wrap Party       31.779661  \n",
              "The Unscripted Project Trivia Night         23.541667  \n",
              "Donate to Unscripted                        55.500000  "
            ],
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              "      <th>Unscripted Back to School 2021 Campaign</th>\n",
              "      <td>34144.0</td>\n",
              "      <td>115</td>\n",
              "      <td>299</td>\n",
              "      <td>296.904348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted 20/2020 Fall Campaign</th>\n",
              "      <td>24722.0</td>\n",
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              "      <td>340</td>\n",
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              "      <td>1875.0</td>\n",
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              "      <td>179</td>\n",
              "      <td>31.779661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Unscripted Project Trivia Night</th>\n",
              "      <td>1130.0</td>\n",
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              "      <td>120</td>\n",
              "      <td>23.541667</td>\n",
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              "    <tr>\n",
              "      <th>Donate to Unscripted</th>\n",
              "      <td>555.0</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "      <td>55.500000</td>\n",
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              "            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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df2.drop(columns=['quantity','donation_day'])\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "oPjRDxImC1SU",
        "outputId": "197aca33-fcb6-432d-baa9-3e748b2dddfe"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          amount  avg_donation\n",
              "campaign                                                      \n",
              "Unscripted Back to School 2021 Campaign  34144.0    296.904348\n",
              "Unscripted 20/2020 Fall Campaign         24722.0    171.680556\n",
              "Unscripted 2021 Fundraiser Wrap Party     1875.0     31.779661\n",
              "The Unscripted Project Trivia Night       1130.0     23.541667\n",
              "Donate to Unscripted                       555.0     55.500000"
            ],
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              "      <th>Unscripted Back to School 2021 Campaign</th>\n",
              "      <td>34144.0</td>\n",
              "      <td>296.904348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted 20/2020 Fall Campaign</th>\n",
              "      <td>24722.0</td>\n",
              "      <td>171.680556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unscripted 2021 Fundraiser Wrap Party</th>\n",
              "      <td>1875.0</td>\n",
              "      <td>31.779661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Unscripted Project Trivia Night</th>\n",
              "      <td>1130.0</td>\n",
              "      <td>23.541667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Donate to Unscripted</th>\n",
              "      <td>555.0</td>\n",
              "      <td>55.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
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    {
      "cell_type": "code",
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        "df3.plot(kind=\"bar\")"
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      "metadata": {
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      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7facf17d9ad0>"
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          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Donation Data Analysis.ipynb",
      "toc_visible": true,
      "provenance": [],
      "authorship_tag": "ABX9TyOuP6AThj7P07j0/y3WyB6v",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}