Data-Preparation.ipynb

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   "source": [
    "# Data Preparation"
   ]
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   "source": [
    "In this part, we outline the data preparation process that we followed for our project. To begin, our team undertook an extensive exploration of the cancer-related dataset for US counties. This process involved analyzing the dataset to gain a better understanding of its structure, format, and content.\n",
    "\n",
    "Our dataset was obtained from the data.world website, which aggregated data from various sources, including the American Community Survey (census.gov), clinicaltrials.gov, and cancer.gov. We carefully curated and cleaned the dataset to ensure that it was suitable for our analysis. This involved removing any missing or inconsistent data points, standardizing the format of the data, and addressing any potential data quality issues.\n",
    "\n",
    "Overall, our data preparation process was a critical step in ensuring the accuracy and validity of our analysis. By carefully selecting and curating our dataset, we were able to generate meaningful insights and recommendations to address the issue of cancer in US counties."
   ]
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   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shapefile as shp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from cancerolstools import preprocess as pp"
   ]
  },
  {
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   "source": [
    "# Data cleaning and exploring "
   ]
  },
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   "source": [
    "Data cleaning and exploration are crucial steps in any data analysis project. In our project, firstly, our team try to enhance the geographic information available for each county in the United States, which required us to start by cleaning the Federal Information Processing Standards (FIPS) dataset.\n",
    "\n",
    "To accomplish this, we clean the data set at the beginning. We used the pandas library in Python to read in the FIPS dataset as a pandas dataframe. We specified the file location, the tab separator, and the file encoding. We also set the data types for the columns to be strings to ensure consistency."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StateFIPS</th>\n",
       "      <th>CountyFIPS_3</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>StateName</th>\n",
       "      <th>CountyFIPS</th>\n",
       "      <th>StateAbbr</th>\n",
       "      <th>STATE_COUNTY</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>01001</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL | AUTAUGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>01003</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL | BALDWIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>005</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>01005</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL | BARBOUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>007</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>01007</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL | BIBB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>009</td>\n",
       "      <td>Blount</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>01009</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL | BLOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>56</td>\n",
       "      <td>037</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56037</td>\n",
       "      <td>WY</td>\n",
       "      <td>WY | SWEETWATER</td>\n",
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       "      <th>3139</th>\n",
       "      <td>56</td>\n",
       "      <td>039</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56039</td>\n",
       "      <td>WY</td>\n",
       "      <td>WY | TETON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3140</th>\n",
       "      <td>56</td>\n",
       "      <td>041</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56041</td>\n",
       "      <td>WY</td>\n",
       "      <td>WY | UINTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3141</th>\n",
       "      <td>56</td>\n",
       "      <td>043</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56043</td>\n",
       "      <td>WY</td>\n",
       "      <td>WY | WASHAKIE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3142</th>\n",
       "      <td>56</td>\n",
       "      <td>045</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56045</td>\n",
       "      <td>WY</td>\n",
       "      <td>WY | WESTON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>3143 rows × 7 columns</p>\n",
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       "     StateFIPS CountyFIPS_3  CountyName StateName CountyFIPS StateAbbr  \\\n",
       "0           01          001     Autauga   Alabama      01001        AL   \n",
       "1           01          003     Baldwin   Alabama      01003        AL   \n",
       "2           01          005     Barbour   Alabama      01005        AL   \n",
       "3           01          007        Bibb   Alabama      01007        AL   \n",
       "4           01          009      Blount   Alabama      01009        AL   \n",
       "...        ...          ...         ...       ...        ...       ...   \n",
       "3138        56          037  Sweetwater   Wyoming      56037        WY   \n",
       "3139        56          039       Teton   Wyoming      56039        WY   \n",
       "3140        56          041       Uinta   Wyoming      56041        WY   \n",
       "3141        56          043    Washakie   Wyoming      56043        WY   \n",
       "3142        56          045      Weston   Wyoming      56045        WY   \n",
       "\n",
       "         STATE_COUNTY  \n",
       "0        AL | AUTAUGA  \n",
       "1        AL | BALDWIN  \n",
       "2        AL | BARBOUR  \n",
       "3           AL | BIBB  \n",
       "4         AL | BLOUNT  \n",
       "...               ...  \n",
       "3138  WY | SWEETWATER  \n",
       "3139       WY | TETON  \n",
       "3140       WY | UINTA  \n",
       "3141    WY | WASHAKIE  \n",
       "3142      WY | WESTON  \n",
       "\n",
       "[3143 rows x 7 columns]"
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   "source": [
    "fips_df = pp.import_fips()\n",
    "fips_df"
   ]
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   "source": [
    "Our team also explored the dataset to gain insights into its structure, format, and contents. This process involved using various pandas dataframe methods to examine the distribution of data across different columns, identify any trends or patterns in the data, and generate visualizations to help understand the relationships between variables.\n",
    "\n",
    "To start, we used the describe() method to generate descriptive statistics for the dataset. This allowed us to quickly get an overview of the data, including the number of observations, mean, standard deviation, minimum, and maximum values for each column."
   ]
  },
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    {
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     "text": [
      "       StateFIPS CountyFIPS_3  CountyName StateName CountyFIPS StateAbbr  \\\n",
      "count       3143         3143        3143      3143       3143      3143   \n",
      "unique        51          325        1878        51       3143        51   \n",
      "top           48          001  Washington     Texas      01001        TX   \n",
      "freq         254           49          30       254          1       254   \n",
      "\n",
      "        STATE_COUNTY  \n",
      "count           3143  \n",
      "unique          3143  \n",
      "top     AL | AUTAUGA  \n",
      "freq               1  \n"
     ]
    }
   ],
   "source": [
    "print(fips_df.describe())"
   ]
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   "source": [
    "Next, our team also examined the FIPS dataset to understand the total count of counties in each state and obtain a comprehensive overview of the differences in counties across the country. We printed the subset of the data where the count is greater than one, indicating potential discrepancies between FIPS codes and county names."
   ]
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    {
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     "text": [
      "         StateName StateFIPS  Count\n",
      "0          Alabama        01     67\n",
      "1           Alaska        02     30\n",
      "2          Arizona        04     15\n",
      "3         Arkansas        05     75\n",
      "4       California        06     58\n",
      "5         Colorado        08     64\n",
      "6      Connecticut        09      8\n",
      "7         Delaware        10      3\n",
      "9          Florida        12     67\n",
      "10         Georgia        13    159\n",
      "11          Hawaii        15      5\n",
      "12           Idaho        16     44\n",
      "13        Illinois        17    102\n",
      "14         Indiana        18     92\n",
      "15            Iowa        19     99\n",
      "16          Kansas        20    105\n",
      "17        Kentucky        21    120\n",
      "18       Louisiana        22     64\n",
      "19           Maine        23     16\n",
      "20        Maryland        24     24\n",
      "21   Massachusetts        25     14\n",
      "22        Michigan        26     83\n",
      "23       Minnesota        27     87\n",
      "24     Mississippi        28     82\n",
      "25        Missouri        29    115\n",
      "26         Montana        30     56\n",
      "27        Nebraska        31     93\n",
      "28          Nevada        32     17\n",
      "29   New Hampshire        33     10\n",
      "30      New Jersey        34     21\n",
      "31      New Mexico        35     33\n",
      "32        New York        36     62\n",
      "33  North Carolina        37    100\n",
      "34    North Dakota        38     53\n",
      "35            Ohio        39     88\n",
      "36        Oklahoma        40     77\n",
      "37          Oregon        41     36\n",
      "38    Pennsylvania        42     67\n",
      "39    Rhode Island        44      5\n",
      "40  South Carolina        45     46\n",
      "41    South Dakota        46     66\n",
      "42       Tennessee        47     95\n",
      "43           Texas        48    254\n",
      "44            Utah        49     29\n",
      "45         Vermont        50     14\n",
      "46        Virginia        51    133\n",
      "47      Washington        53     39\n",
      "48   West Virginia        54     55\n",
      "49       Wisconsin        55     72\n",
      "50         Wyoming        56     23\n"
     ]
    }
   ],
   "source": [
    "fips_county = fips_df.groupby(['StateName', 'StateFIPS']).size().reset_index()\n",
    "fips_county.columns = ['StateName', 'StateFIPS', 'Count']\n",
    "print(fips_county[fips_county['Count'] > 1])"
   ]
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   "source": [
    "Moreover, the FIPS dataset can be a useful tool for filtering locations based on specific areas of interest across the country. For example, we can use the condition ['STATE COUNTY'] == 'CA | ALAMEDA' to filter the dataset and view information specifically for Alameda County. Based on the results below, we can see that Alameda has county FIPS of 06001. "
   ]
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       "      <th>187</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>California</td>\n",
       "      <td>06001</td>\n",
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       "    StateFIPS CountyFIPS_3 CountyName   StateName CountyFIPS StateAbbr  \\\n",
       "187        06          001    Alameda  California      06001        CA   \n",
       "\n",
       "     STATE_COUNTY  \n",
       "187  CA | ALAMEDA  "
      ]
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   "source": [
    "alameda_info = fips_df[fips_df['STATE_COUNTY'] == 'CA | ALAMEDA']\n",
    "alameda_info"
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    "Secondly, our team try to explore the Cancer Regression dataset which is to predict cancer death rates based on various demographic and health-related factors for each county in the United States. Our team conducted data exploration and cleaning on this dataset to better understand the variables related to cancer death rates and to prepare the data for further analysis."
   ]
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       "      <td>41.6</td>\n",
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       "      <td>18.6</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>53.8</td>\n",
       "      <td>43.6</td>\n",
       "      <td>31.1</td>\n",
       "      <td>15.3</td>\n",
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       "      <td>0.969102</td>\n",
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       "      <td>45.0</td>\n",
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       "      <td>43.5</td>\n",
       "      <td>34.9</td>\n",
       "      <td>42.1</td>\n",
       "      <td>21.1</td>\n",
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       "      <td>0.739673</td>\n",
       "      <td>0.465898</td>\n",
       "      <td>2.747358</td>\n",
       "      <td>54.444868</td>\n",
       "      <td>3.729488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>427.000000</td>\n",
       "      <td>202</td>\n",
       "      <td>194.8</td>\n",
       "      <td>430.400000</td>\n",
       "      <td>44243</td>\n",
       "      <td>75882</td>\n",
       "      <td>17.1</td>\n",
       "      <td>342.637253</td>\n",
       "      <td>(42724.4, 45201]</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>40.3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.744686</td>\n",
       "      <td>0.782626</td>\n",
       "      <td>1.161359</td>\n",
       "      <td>1.362643</td>\n",
       "      <td>51.021514</td>\n",
       "      <td>4.603841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>144.4</td>\n",
       "      <td>350.100000</td>\n",
       "      <td>49955</td>\n",
       "      <td>10321</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>48.3</td>\n",
       "      <td>...</td>\n",
       "      <td>43.9</td>\n",
       "      <td>35.1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>94.104024</td>\n",
       "      <td>0.270192</td>\n",
       "      <td>0.665830</td>\n",
       "      <td>0.492135</td>\n",
       "      <td>54.027460</td>\n",
       "      <td>6.796657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>1962.667684</td>\n",
       "      <td>15</td>\n",
       "      <td>149.6</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>46961</td>\n",
       "      <td>6343</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(45201, 48021.6]</td>\n",
       "      <td>44.2</td>\n",
       "      <td>...</td>\n",
       "      <td>54.9</td>\n",
       "      <td>44.6</td>\n",
       "      <td>31.7</td>\n",
       "      <td>13.2</td>\n",
       "      <td>90.280811</td>\n",
       "      <td>3.837754</td>\n",
       "      <td>0.327613</td>\n",
       "      <td>1.700468</td>\n",
       "      <td>51.063830</td>\n",
       "      <td>7.773512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043</th>\n",
       "      <td>1962.667684</td>\n",
       "      <td>43</td>\n",
       "      <td>150.1</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>48609</td>\n",
       "      <td>37118</td>\n",
       "      <td>18.8</td>\n",
       "      <td>377.175494</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>30.4</td>\n",
       "      <td>...</td>\n",
       "      <td>53.3</td>\n",
       "      <td>48.6</td>\n",
       "      <td>28.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>75.706245</td>\n",
       "      <td>2.326771</td>\n",
       "      <td>4.044920</td>\n",
       "      <td>14.130288</td>\n",
       "      <td>52.007937</td>\n",
       "      <td>8.186470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>1962.667684</td>\n",
       "      <td>46</td>\n",
       "      <td>153.9</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>51144</td>\n",
       "      <td>34536</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1968.959926</td>\n",
       "      <td>(51046.4, 54545.6]</td>\n",
       "      <td>30.9</td>\n",
       "      <td>...</td>\n",
       "      <td>52.6</td>\n",
       "      <td>47.8</td>\n",
       "      <td>26.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>87.961629</td>\n",
       "      <td>2.313188</td>\n",
       "      <td>1.316472</td>\n",
       "      <td>5.680705</td>\n",
       "      <td>55.153949</td>\n",
       "      <td>7.809192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>1962.667684</td>\n",
       "      <td>52</td>\n",
       "      <td>175.0</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>50745</td>\n",
       "      <td>25609</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.3</td>\n",
       "      <td>49.6</td>\n",
       "      <td>29.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>92.905681</td>\n",
       "      <td>1.176562</td>\n",
       "      <td>0.244632</td>\n",
       "      <td>2.131790</td>\n",
       "      <td>58.484232</td>\n",
       "      <td>7.582938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>1962.667684</td>\n",
       "      <td>48</td>\n",
       "      <td>213.6</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>41193</td>\n",
       "      <td>37030</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(40362.7, 42724.4]</td>\n",
       "      <td>26.2</td>\n",
       "      <td>...</td>\n",
       "      <td>60.3</td>\n",
       "      <td>22.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>70.098132</td>\n",
       "      <td>16.590100</td>\n",
       "      <td>3.177753</td>\n",
       "      <td>1.356457</td>\n",
       "      <td>56.040242</td>\n",
       "      <td>8.981723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3047 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avgAnnCount  avgDeathsPerYear  TARGET_deathRate  incidenceRate  \\\n",
       "0     1397.000000               469             164.9     489.800000   \n",
       "1      173.000000                70             161.3     411.600000   \n",
       "2      102.000000                50             174.7     349.700000   \n",
       "3      427.000000               202             194.8     430.400000   \n",
       "4       57.000000                26             144.4     350.100000   \n",
       "...           ...               ...               ...            ...   \n",
       "3042  1962.667684                15             149.6     453.549422   \n",
       "3043  1962.667684                43             150.1     453.549422   \n",
       "3044  1962.667684                46             153.9     453.549422   \n",
       "3045  1962.667684                52             175.0     453.549422   \n",
       "3046  1962.667684                48             213.6     453.549422   \n",
       "\n",
       "      medIncome  popEst2015  povertyPercent  studyPerCap           binnedInc  \\\n",
       "0         61898      260131            11.2   499.748204   (61494.5, 125635]   \n",
       "1         48127       43269            18.6    23.111234  (48021.6, 51046.4]   \n",
       "2         49348       21026            14.6    47.560164  (48021.6, 51046.4]   \n",
       "3         44243       75882            17.1   342.637253    (42724.4, 45201]   \n",
       "4         49955       10321            12.5     0.000000  (48021.6, 51046.4]   \n",
       "...         ...         ...             ...          ...                 ...   \n",
       "3042      46961        6343            12.4     0.000000    (45201, 48021.6]   \n",
       "3043      48609       37118            18.8   377.175494  (48021.6, 51046.4]   \n",
       "3044      51144       34536            15.0  1968.959926  (51046.4, 54545.6]   \n",
       "3045      50745       25609            13.3     0.000000  (48021.6, 51046.4]   \n",
       "3046      41193       37030            13.9     0.000000  (40362.7, 42724.4]   \n",
       "\n",
       "      MedianAge  ...  PctPrivateCoverageAlone  PctEmpPrivCoverage  \\\n",
       "0          39.3  ...                      NaN                41.6   \n",
       "1          33.0  ...                     53.8                43.6   \n",
       "2          45.0  ...                     43.5                34.9   \n",
       "3          42.8  ...                     40.3                35.0   \n",
       "4          48.3  ...                     43.9                35.1   \n",
       "...         ...  ...                      ...                 ...   \n",
       "3042       44.2  ...                     54.9                44.6   \n",
       "3043       30.4  ...                     53.3                48.6   \n",
       "3044       30.9  ...                     52.6                47.8   \n",
       "3045       39.0  ...                     56.3                49.6   \n",
       "3046       26.2  ...                     60.3                22.9   \n",
       "\n",
       "     PctPublicCoverage  PctPublicCoverageAlone   PctWhite   PctBlack  \\\n",
       "0                 32.9                    14.0  81.780529   2.594728   \n",
       "1                 31.1                    15.3  89.228509   0.969102   \n",
       "2                 42.1                    21.1  90.922190   0.739673   \n",
       "3                 45.3                    25.0  91.744686   0.782626   \n",
       "4                 44.0                    22.7  94.104024   0.270192   \n",
       "...                ...                     ...        ...        ...   \n",
       "3042              31.7                    13.2  90.280811   3.837754   \n",
       "3043              28.8                    17.7  75.706245   2.326771   \n",
       "3044              26.6                    16.8  87.961629   2.313188   \n",
       "3045              29.5                    14.0  92.905681   1.176562   \n",
       "3046              25.1                    12.6  70.098132  16.590100   \n",
       "\n",
       "      PctAsian  PctOtherRace  PctMarriedHouseholds  BirthRate  \n",
       "0     4.821857      1.843479             52.856076   6.118831  \n",
       "1     2.246233      3.741352             45.372500   4.333096  \n",
       "2     0.465898      2.747358             54.444868   3.729488  \n",
       "3     1.161359      1.362643             51.021514   4.603841  \n",
       "4     0.665830      0.492135             54.027460   6.796657  \n",
       "...        ...           ...                   ...        ...  \n",
       "3042  0.327613      1.700468             51.063830   7.773512  \n",
       "3043  4.044920     14.130288             52.007937   8.186470  \n",
       "3044  1.316472      5.680705             55.153949   7.809192  \n",
       "3045  0.244632      2.131790             58.484232   7.582938  \n",
       "3046  3.177753      1.356457             56.040242   8.981723  \n",
       "\n",
       "[3047 rows x 34 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df = pp.import_cancer()\n",
    "cancer_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61874e1-6589-43ba-886b-a760fd571452",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We used the describe() method to generate descriptive statistics for the cancer dataset. This allowed us to quickly get an overview of the data, including the number of observations, mean, standard deviation, minimum, and maximum values for each features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33d908f6-d805-4e57-853c-eac94c91df16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        avgAnnCount  avgDeathsPerYear  TARGET_deathRate  incidenceRate  \\\n",
      "count   3047.000000       3047.000000       3047.000000    3047.000000   \n",
      "mean     606.338544        185.965868        178.664063     448.268586   \n",
      "std     1416.356223        504.134286         27.751511      54.560733   \n",
      "min        6.000000          3.000000         59.700000     201.300000   \n",
      "25%       76.000000         28.000000        161.200000     420.300000   \n",
      "50%      171.000000         61.000000        178.100000     453.549422   \n",
      "75%      518.000000        149.000000        195.200000     480.850000   \n",
      "max    38150.000000      14010.000000        362.800000    1206.900000   \n",
      "\n",
      "           medIncome    popEst2015  povertyPercent  studyPerCap    MedianAge  \\\n",
      "count    3047.000000  3.047000e+03     3047.000000  3047.000000  3047.000000   \n",
      "mean    47063.281917  1.026374e+05       16.878175   155.399415    45.272333   \n",
      "std     12040.090836  3.290592e+05        6.409087   529.628366    45.304480   \n",
      "min     22640.000000  8.270000e+02        3.200000     0.000000    22.300000   \n",
      "25%     38882.500000  1.168400e+04       12.150000     0.000000    37.700000   \n",
      "50%     45207.000000  2.664300e+04       15.900000     0.000000    41.000000   \n",
      "75%     52492.000000  6.867100e+04       20.400000    83.650776    44.000000   \n",
      "max    125635.000000  1.017029e+07       47.400000  9762.308998   624.000000   \n",
      "\n",
      "       MedianAgeMale  ...  PctPrivateCoverageAlone  PctEmpPrivCoverage  \\\n",
      "count    3047.000000  ...              2438.000000         3047.000000   \n",
      "mean       39.570725  ...                48.453774           41.196324   \n",
      "std         5.226017  ...                10.083006            9.447687   \n",
      "min        22.400000  ...                15.700000           13.500000   \n",
      "25%        36.350000  ...                41.000000           34.500000   \n",
      "50%        39.600000  ...                48.700000           41.100000   \n",
      "75%        42.500000  ...                55.600000           47.700000   \n",
      "max        64.700000  ...                78.900000           70.700000   \n",
      "\n",
      "       PctPublicCoverage  PctPublicCoverageAlone     PctWhite     PctBlack  \\\n",
      "count        3047.000000             3047.000000  3047.000000  3047.000000   \n",
      "mean           36.252642               19.240072    83.645286     9.107978   \n",
      "std             7.841741                6.113041    16.380025    14.534538   \n",
      "min            11.200000                2.600000    10.199155     0.000000   \n",
      "25%            30.900000               14.850000    77.296180     0.620675   \n",
      "50%            36.300000               18.800000    90.059774     2.247576   \n",
      "75%            41.550000               23.100000    95.451693    10.509732   \n",
      "max            65.100000               46.600000   100.000000    85.947799   \n",
      "\n",
      "          PctAsian  PctOtherRace  PctMarriedHouseholds    BirthRate  \n",
      "count  3047.000000   3047.000000           3047.000000  3047.000000  \n",
      "mean      1.253965      1.983523             51.243872     5.640306  \n",
      "std       2.610276      3.517710              6.572814     1.985816  \n",
      "min       0.000000      0.000000             22.992490     0.000000  \n",
      "25%       0.254199      0.295172             47.763063     4.521419  \n",
      "50%       0.549812      0.826185             51.669941     5.381478  \n",
      "75%       1.221037      2.177960             55.395132     6.493677  \n",
      "max      42.619425     41.930251             78.075397    21.326165  \n",
      "\n",
      "[8 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "print(cancer_df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f362a4-fc3c-4520-bb4e-c6c10c4074e5",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We use the two scatter plots to show the relationship between incidence rate and target death rate, as well as incidence rate and median income. The first scatter plot suggests a positive correlation between incidence rate and target death rate, as higher incidence rates tend to be associated with higher target death rates. The second scatter plot suggests not clear correlation between incidence rate and median income. Based on these results, it appears that the incidence rate and target death rate are more closely related features than incidence rate and medincome. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bee9acf-cd9f-4b6f-aed5-07baf40c2761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# plot incidenceRate vs TARGET_deathRate\n",
    "ax[0].scatter(cancer_df['incidenceRate'], cancer_df['TARGET_deathRate'])\n",
    "ax[0].set_xlabel('Incidence Rate')\n",
    "ax[0].set_ylabel('TARGET_deathRate')\n",
    "ax[0].set_title('Relationship between Incidence Rate and TARGET_deathRate')\n",
    "\n",
    "# plot incidenceRate vs medIncome\n",
    "ax[1].scatter(cancer_df['incidenceRate'], cancer_df['medIncome'])\n",
    "ax[1].set_xlabel('Incidence Rate')\n",
    "ax[1].set_ylabel('Median Income')\n",
    "ax[1].set_title('Relationship between Incidence Rate and Median Income')\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('figures/Incidence_Rate.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcd4dc2-b608-4b08-bd1e-8f45bec10c0b",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "To prepare for our later visualizations and model analysis, our team wrote a series of code below to clean and transform the cancer-related dataset for US counties.\n",
    "\n",
    "The primary steps are:\n",
    "\n",
    "* Adding a new column that divides the death rate by the income of a county.\n",
    "* Extracting state and county from \"Geography\" column and create new columns for these values.\n",
    "* Manually changing 2 county names that are imported with odd characters.\n",
    "* Merge cancer and fips data\n",
    "* Add a new column that divides the death rate by the log income\n",
    "* Create a new dataframe that preserves only the FIPS code and the death rate divided by Log income for conveniences in plotting.\n",
    "\n",
    "All of these processes are implemented within the preprocess module of the cancerolstools package. In particular, `cancer_preprocess` will perform cancer-only modifications listed above while `merge_data` will merge a modified or unmodified cancer dataset with the fips dataset. For details on the implementation, the package codes are available in the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3cefdbb0-cc62-427b-9b25-bffcd336334b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_df = pp.cancer_preprocess(cancer_df)\n",
    "cancer_df = pp.merge_data(cancer_df, fips_df)\n",
    "\n",
    "graphdata = pp.graph_dataframe(cancer_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e61bde1-27a3-44a2-9730-a48c0817bdd3",
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    "We additionally create another dataframe, `data_with_anomalies`, that will aid in visualizing the particular spread across the different counties of the United States. \n",
    "\n",
    "To help symmetrize median income and thus our ratio, we first applied a log transformation, then calculated our ratios. Then, to identify any anomalies, that is, counties with an unusually high or small ratio, we standardized our ratio values, creating a new variable called “Anomalies,” representing the standard deviations away from the mean ratio. Finally, to focus on significant differences to the mean ratio, we changed any anomalies less than |1| to 0. The above code completes this processing and compiles the results in a dataframe with anomalies. For specific documentation and implementation, please visit the cancerolstools package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f237063-1071-4af1-ab13-86228856c77f",
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       "        fips  anomalies\n",
       "0      53035   0.000000\n",
       "1      53037   0.000000\n",
       "2      53039   0.000000\n",
       "3      16061   0.000000\n",
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    "data_with_anomalies = pp.graph_dataframe(cancer_df)\n",
    "data_with_anomalies"
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