US_Dog_Bites.ipynb

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        "import pandas as pd\n",
        "\n",
        "years = list(range(2010, 2023))\n",
        "dog_bites = [38, 34, 34, 35, 36, 36, 31, 36, 35, 48, 62, 81, 98]\n",
        "uspop_json = '''\n",
        "[{\"year\":1960,\"population\":180671000.0},{\"year\":1961,\"population\":183691000.0},{\"year\":1962,\"population\":186538000.0},{\"year\":1963,\"population\":189242000.0},{\"year\":1964,\"population\":191889000.0},{\"year\":1965,\"population\":194303000.0},{\"year\":1966,\"population\":196560000.0},{\"year\":1967,\"population\":198712000.0},{\"year\":1968,\"population\":200706000.0},{\"year\":1969,\"population\":202677000.0},{\"year\":1970,\"population\":205052000.0},{\"year\":1971,\"population\":207661000.0},{\"year\":1972,\"population\":209896000.0},{\"year\":1973,\"population\":211909000.0},{\"year\":1974,\"population\":213854000.0},{\"year\":1975,\"population\":215973000.0},{\"year\":1976,\"population\":218035000.0},{\"year\":1977,\"population\":220239000.0},{\"year\":1978,\"population\":222585000.0},{\"year\":1979,\"population\":225055000.0},{\"year\":1980,\"population\":227225000.0},{\"year\":1981,\"population\":229466000.0},{\"year\":1982,\"population\":231664000.0},{\"year\":1983,\"population\":233792000.0},{\"year\":1984,\"population\":235825000.0},{\"year\":1985,\"population\":237924000.0},{\"year\":1986,\"population\":240133000.0},{\"year\":1987,\"population\":242289000.0},{\"year\":1988,\"population\":244499000.0},{\"year\":1989,\"population\":246819000.0},{\"year\":1990,\"population\":249623000.0},{\"year\":1991,\"population\":252981000.0},{\"year\":1992,\"population\":256514000.0},{\"year\":1993,\"population\":259919000.0},{\"year\":1994,\"population\":263126000.0},{\"year\":1995,\"population\":266278000.0},{\"year\":1996,\"population\":269394000.0},{\"year\":1997,\"population\":272657000.0},{\"year\":1998,\"population\":275854000.0},{\"year\":1999,\"population\":279040000.0},{\"year\":2000,\"population\":282162411.0},{\"year\":2001,\"population\":284968955.0},{\"year\":2002,\"population\":287625193.0},{\"year\":2003,\"population\":290107933.0},{\"year\":2004,\"population\":292805298.0},{\"year\":2005,\"population\":295516599.0},{\"year\":2006,\"population\":298379912.0},{\"year\":2007,\"population\":301231207.0},{\"year\":2008,\"population\":304093966.0},{\"year\":2009,\"population\":306771529.0},{\"year\":2010,\"population\":309327143.0},{\"year\":2011,\"population\":311583481.0},{\"year\":2012,\"population\":313877662.0},{\"year\":2013,\"population\":316059947.0},{\"year\":2014,\"population\":318386329.0},{\"year\":2015,\"population\":320738994.0},{\"year\":2016,\"population\":323071755.0},{\"year\":2017,\"population\":325122128.0},{\"year\":2018,\"population\":326838199.0},{\"year\":2019,\"population\":328329953.0},{\"year\":2020,\"population\":331526933.0},{\"year\":2021,\"population\":332048977.0},{\"year\":2022,\"population\":333271411.0},{\"year\":2023,\"population\":334914895.0}]\n",
        "'''\n",
        "dogbites_df = pd.DataFrame({'year': years, 'bites': dog_bites})\n",
        "uspop_df = pd.DataFrame(eval(uspop_json))\n",
        "\n"
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      "source": [
        "alldata = pd.merge(dogbites_df, uspop_df, left_on='year', right_on='year')\n",
        "alldata['bites_per_million'] = alldata.bites / alldata.population * 1e6\n",
        "alldata"
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              "    year  bites   population  bites_per_million\n",
              "0   2010     38  309327143.0           0.122847\n",
              "1   2011     34  311583481.0           0.109120\n",
              "2   2012     34  313877662.0           0.108322\n",
              "3   2013     35  316059947.0           0.110738\n",
              "4   2014     36  318386329.0           0.113070\n",
              "5   2015     36  320738994.0           0.112241\n",
              "6   2016     31  323071755.0           0.095954\n",
              "7   2017     36  325122128.0           0.110728\n",
              "8   2018     35  326838199.0           0.107087\n",
              "9   2019     48  328329953.0           0.146194\n",
              "10  2020     62  331526933.0           0.187013\n",
              "11  2021     81  332048977.0           0.243940\n",
              "12  2022     98  333271411.0           0.294055"
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('alldata');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "alldata",
              "summary": "{\n  \"name\": \"alldata\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2010,\n        \"max\": 2022,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          2021,\n          2019,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bites\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 31,\n        \"max\": 98,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          81,\n          34,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8045840.232970177,\n        \"min\": 309327143.0,\n        \"max\": 333271411.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          332048977.0,\n          328329953.0,\n          309327143.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bites_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06133052549077315,\n        \"min\": 0.09595391587234235,\n        \"max\": 0.2940546256456423,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.24393991733333967,\n          0.14619439853542696,\n          0.12284728598809061\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alldata.plot(x='year', y='bites_per_million',\n",
        "             title='US dog bites per million people')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "YFNvNPR1ThNH",
        "outputId": "ff377057-98dd-412f-ed13-48220c23f5f0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'US dog bites per million people'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}