Assignment 2_s1907110.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set up\n",
    "\n",
    "the first datasheet I am going to analyze is some details about tumour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Necessary import\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apple\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3057: DtypeWarning: Columns (5,6,8,9,17,18,20,22,26,32) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TUMOURID</th>\n",
       "      <th>PATIENTID</th>\n",
       "      <th>DIAGNOSISDATEBEST</th>\n",
       "      <th>SITE_ICD10_O2</th>\n",
       "      <th>SITE_ICD10_O2_3CHAR</th>\n",
       "      <th>MORPH_ICD10_O2</th>\n",
       "      <th>BEHAVIOUR_ICD10_O2</th>\n",
       "      <th>T_BEST</th>\n",
       "      <th>N_BEST</th>\n",
       "      <th>M_BEST</th>\n",
       "      <th>...</th>\n",
       "      <th>PERFORMANCESTATUS</th>\n",
       "      <th>CNS</th>\n",
       "      <th>ACE27</th>\n",
       "      <th>GLEASON_PRIMARY</th>\n",
       "      <th>GLEASON_SECONDARY</th>\n",
       "      <th>GLEASON_TERTIARY</th>\n",
       "      <th>GLEASON_COMBINED</th>\n",
       "      <th>DATE_FIRST_SURGERY</th>\n",
       "      <th>LATERALITY</th>\n",
       "      <th>QUINTILE_2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000001</td>\n",
       "      <td>10000001</td>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>C443</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000002</td>\n",
       "      <td>10000002</td>\n",
       "      <td>2015-10-06</td>\n",
       "      <td>C449</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-10-06</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000003</td>\n",
       "      <td>10000003</td>\n",
       "      <td>2013-01-30</td>\n",
       "      <td>C449</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>1 - least deprived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000004</td>\n",
       "      <td>10000004</td>\n",
       "      <td>2015-09-15</td>\n",
       "      <td>C449</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000005</td>\n",
       "      <td>10000005</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>C449</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TUMOURID  PATIENTID DIAGNOSISDATEBEST SITE_ICD10_O2 SITE_ICD10_O2_3CHAR  \\\n",
       "0  10000001   10000001        2015-11-06          C443                 C44   \n",
       "1  10000002   10000002        2015-10-06          C449                 C44   \n",
       "2  10000003   10000003        2013-01-30          C449                 C44   \n",
       "3  10000004   10000004        2015-09-15          C449                 C44   \n",
       "4  10000005   10000005        2014-11-01          C449                 C44   \n",
       "\n",
       "  MORPH_ICD10_O2 BEHAVIOUR_ICD10_O2 T_BEST N_BEST M_BEST  ...  \\\n",
       "0           8090                  3    NaN    NaN    NaN  ...   \n",
       "1           8090                  3    NaN    NaN    NaN  ...   \n",
       "2           8090                  3    NaN    NaN    NaN  ...   \n",
       "3           8090                  3    NaN    NaN    NaN  ...   \n",
       "4           8090                  3    NaN    NaN    NaN  ...   \n",
       "\n",
       "  PERFORMANCESTATUS  CNS ACE27  GLEASON_PRIMARY  GLEASON_SECONDARY  \\\n",
       "0               NaN  NaN   NaN              NaN                NaN   \n",
       "1               NaN  NaN   NaN              NaN                NaN   \n",
       "2               NaN  NaN   NaN              NaN                NaN   \n",
       "3               NaN  NaN   NaN              NaN                NaN   \n",
       "4               NaN  NaN   NaN              NaN                NaN   \n",
       "\n",
       "  GLEASON_TERTIARY  GLEASON_COMBINED DATE_FIRST_SURGERY LATERALITY  \\\n",
       "0              NaN               NaN         2015-11-06          L   \n",
       "1              NaN               NaN         2015-10-06          8   \n",
       "2              NaN               NaN                NaN          8   \n",
       "3              NaN               NaN                NaN          8   \n",
       "4              NaN               NaN         2014-11-01          8   \n",
       "\n",
       "        QUINTILE_2015  \n",
       "0                   3  \n",
       "1                   2  \n",
       "2  1 - least deprived  \n",
       "3                   2  \n",
       "4                   2  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 986,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data\n",
    "tumour= pd.read_csv(\"sim_av_tumour.csv\")\n",
    "tumour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# about this table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1402817, 34)"
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table contains 34 columns and 1402817 rows. And have a look at more information about this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1402817 entries, 0 to 1402816\n",
      "Data columns (total 34 columns):\n",
      "TUMOURID                    1402817 non-null int64\n",
      "PATIENTID                   1402817 non-null int64\n",
      "DIAGNOSISDATEBEST           1402817 non-null object\n",
      "SITE_ICD10_O2               1402303 non-null object\n",
      "SITE_ICD10_O2_3CHAR         1402356 non-null object\n",
      "MORPH_ICD10_O2              1402304 non-null object\n",
      "BEHAVIOUR_ICD10_O2          1402410 non-null object\n",
      "T_BEST                      653053 non-null object\n",
      "N_BEST                      546397 non-null object\n",
      "M_BEST                      535682 non-null object\n",
      "STAGE_BEST                  1402801 non-null object\n",
      "STAGE_BEST_SYSTEM           671840 non-null float64\n",
      "GRADE                       1340030 non-null object\n",
      "AGE                         1402817 non-null int64\n",
      "SEX                         1402817 non-null int64\n",
      "CREG_CODE                   1402817 non-null object\n",
      "LINKNUMBER                  1402817 non-null int64\n",
      "SCREENINGSTATUSFULL_CODE    124561 non-null object\n",
      "ER_STATUS                   123700 non-null object\n",
      "ER_SCORE                    57910 non-null float64\n",
      "PR_STATUS                   79216 non-null object\n",
      "PR_SCORE                    32785 non-null float64\n",
      "HER2_STATUS                 108034 non-null object\n",
      "CANCERCAREPLANINTENT        415863 non-null object\n",
      "PERFORMANCESTATUS           381280 non-null object\n",
      "CNS                         369922 non-null object\n",
      "ACE27                       106324 non-null object\n",
      "GLEASON_PRIMARY             58602 non-null float64\n",
      "GLEASON_SECONDARY           34517 non-null float64\n",
      "GLEASON_TERTIARY            784 non-null float64\n",
      "GLEASON_COMBINED            34577 non-null float64\n",
      "DATE_FIRST_SURGERY          872714 non-null object\n",
      "LATERALITY                  1402782 non-null object\n",
      "QUINTILE_2015               1402817 non-null object\n",
      "dtypes: float64(7), int64(5), object(22)\n",
      "memory usage: 363.9+ MB\n"
     ]
    }
   ],
   "source": [
    "tumour.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to know the exact bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1607800047"
      ]
     },
     "execution_count": 989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.memory_usage(deep=True).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many missing values are there per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TUMOURID                          0\n",
       "PATIENTID                         0\n",
       "DIAGNOSISDATEBEST                 0\n",
       "SITE_ICD10_O2                   514\n",
       "SITE_ICD10_O2_3CHAR             461\n",
       "MORPH_ICD10_O2                  513\n",
       "BEHAVIOUR_ICD10_O2              407\n",
       "T_BEST                       749764\n",
       "N_BEST                       856420\n",
       "M_BEST                       867135\n",
       "STAGE_BEST                       16\n",
       "STAGE_BEST_SYSTEM            730977\n",
       "GRADE                         62787\n",
       "AGE                               0\n",
       "SEX                               0\n",
       "CREG_CODE                         0\n",
       "LINKNUMBER                        0\n",
       "SCREENINGSTATUSFULL_CODE    1278256\n",
       "ER_STATUS                   1279117\n",
       "ER_SCORE                    1344907\n",
       "PR_STATUS                   1323601\n",
       "PR_SCORE                    1370032\n",
       "HER2_STATUS                 1294783\n",
       "CANCERCAREPLANINTENT         986954\n",
       "PERFORMANCESTATUS           1021537\n",
       "CNS                         1032895\n",
       "ACE27                       1296493\n",
       "GLEASON_PRIMARY             1344215\n",
       "GLEASON_SECONDARY           1368300\n",
       "GLEASON_TERTIARY            1402033\n",
       "GLEASON_COMBINED            1368240\n",
       "DATE_FIRST_SURGERY           530103\n",
       "LATERALITY                       35\n",
       "QUINTILE_2015                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 990,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the missing values of \"STAGE_BEST\" and \"LATERALITY\" are much smaller than others,so I drop rows that have any missing value of them first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1402766, 34)"
      ]
     },
     "execution_count": 991,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.dropna(subset=['STAGE_BEST','LATERALITY'],how='any',inplace=True)\n",
    "tumour.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THEN LOOK AT EACH COLUMN SEPARATELY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEE WHAT THEY MEAN,IF I CAN DROP THEIR MISSING VALUES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"DIAGNOSISDATEBEST\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"DIAGNOSISDATEBEST\"column which means the date on which the patient was first diagnosed with cancer (whether at its primary site or as a metastasis), expressed as DDMMYYYY."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        1402766\n",
       "unique          1095\n",
       "top       2013-05-07\n",
       "freq            2205\n",
       "Name: DIAGNOSISDATEBEST, dtype: object"
      ]
     },
     "execution_count": 992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.DIAGNOSISDATEBEST.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change object to exclusive type (datatime 64[ns]) for later statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['DIAGNOSISDATEBEST']=pd.to_datetime(tumour.DIAGNOSISDATEBEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28ac63ea7b8>"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "tumour['year_diagnosis']=tumour.DIAGNOSISDATEBEST.dt.year\n",
    "tumour.year_diagnosis.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to see the frequency of per year.And we can see that being diagnosed with cancer increases every year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"SITE_ICD10_O2\"    \"SITE_ICD10_O2_3CHAR\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"SITE_ICD10_O2\"and \"SITE_ICD10_O2_3CHAR\":ICD defines the universe of diseases, disorders, injuries and other related health conditions\n",
    " \"SITE_ICD10_O2_3CHAR\" :general categories                                                               \n",
    " \"SITE_ICD10_O2\":breakdown of general categories(\"SITE_ICD10_O2_3CHAR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so I drop rows that have any missing value of them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1401957, 35)"
      ]
     },
     "execution_count": 995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.dropna(subset=['SITE_ICD10_O2','SITE_ICD10_O2_3CHAR'],how='any',inplace=True)\n",
    "tumour.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"T_BEST\"                     \"N_BEST\"                      \"M_BEST\"                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the TNM system:\n",
    "\n",
    "The T refers to the size and extent of the main tumor. The main tumor is usually called the primary tumor.\n",
    "The N refers to the the number of nearby lymph nodes that have cancer.\n",
    "The M refers to whether the cancer has metastasized. This means that the cancer has spread from the primary tumor to other parts of the body.\n",
    "When your cancer is described by the TNM system, there will be numbers after each letter that give more details about the cancer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IS is not cancer, but it may become cancer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so I drop rows that have any missing value of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(446144, 35)"
      ]
     },
     "execution_count": 996,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.dropna(subset=['T_BEST','N_BEST','M_BEST'],how='any',inplace=True)\n",
    "tumour.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primary tumor (T)\n",
    "\n",
    "TX: Main tumor cannot be measured.\n",
    "T0: Main tumor cannot be found.\n",
    "T1, T2, T3, T4: Refers to the size and/or extent of the main tumor. The higher the number after the T, the larger the tumor or the more it has grown into nearby tissues. T's may be further divided to provide more detail, such as T3a and T3b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      90250\n",
       "2      75082\n",
       "4      50269\n",
       "1      38405\n",
       "1c     30017\n",
       "2a     27931\n",
       "1a     27499\n",
       "1b     23711\n",
       "3a     16486\n",
       "2c     15762\n",
       "2b     11430\n",
       "3b     10911\n",
       "4b      9947\n",
       "4a      8460\n",
       "is      3163\n",
       "a       2363\n",
       "3c      1447\n",
       "4d       529\n",
       "1mi      524\n",
       "1b1      496\n",
       "0        408\n",
       "1a1      267\n",
       "4c       175\n",
       "3d       148\n",
       "1b2       88\n",
       "1d        60\n",
       "2a2       58\n",
       "3s        49\n",
       "1a2       44\n",
       "X         33\n",
       "2s        32\n",
       "2d        28\n",
       "2a1       24\n",
       "IS        24\n",
       "2A         9\n",
       "T2         5\n",
       "1B         3\n",
       "4e         3\n",
       "3/4        3\n",
       "T3         1\n",
       "Name: T_BEST, dtype: int64"
      ]
     },
     "execution_count": 997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['T_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so:1、drop some meaningless values{a , 1b1 ,1a1 ,1b2 ,2a2 , 1a2 , 2a1 }(maybe human error)                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  2、change {2A , T2 ,1B, T3,is}to{2a , 2 , 1b ,3,IS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.T_BEST=='a'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='1b1'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='1a1'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='1b2'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='2a2'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='1a2'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.T_BEST=='2a1'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.replace('2A','2a',inplace=True)\n",
    "tumour.replace('T2','2',inplace=True)\n",
    "tumour.replace('1B','2b',inplace=True)\n",
    "tumour.replace('T3','3',inplace=True)\n",
    "tumour.replace('is','IS',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      90251\n",
       "2      75087\n",
       "4      50269\n",
       "1      38405\n",
       "1c     30017\n",
       "2a     27940\n",
       "1a     27499\n",
       "1b     23711\n",
       "3a     16486\n",
       "2c     15762\n",
       "2b     11433\n",
       "3b     10911\n",
       "4b      9947\n",
       "4a      8460\n",
       "IS      3187\n",
       "3c      1447\n",
       "4d       529\n",
       "1mi      524\n",
       "0        408\n",
       "4c       175\n",
       "3d       148\n",
       "1d        60\n",
       "3s        49\n",
       "X         33\n",
       "2s        32\n",
       "2d        28\n",
       "3/4        3\n",
       "4e         3\n",
       "Name: T_BEST, dtype: int64"
      ]
     },
     "execution_count": 1000,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['T_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regional lymph nodes (N)\n",
    "\n",
    "NX: Cancer in nearby lymph nodes cannot be measured.\n",
    "N0: There is no cancer in nearby lymph nodes.\n",
    "N1, N2, N3: Refers to the number and location of lymph nodes that contain cancer. The higher the number after the N, the more lymph nodes that contain cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      278951\n",
       "1       71754\n",
       "2       48756\n",
       "3       22345\n",
       "1a       5459\n",
       "2b       4362\n",
       "2a       3062\n",
       "1mi      2209\n",
       "2c       1893\n",
       "3a       1356\n",
       "1b       1126\n",
       "0.0       808\n",
       "1c        284\n",
       "3b        167\n",
       "3c        125\n",
       "X         124\n",
       "N0          8\n",
       "2B          5\n",
       "1.0         5\n",
       "4           2\n",
       "2.0         2\n",
       "+           1\n",
       "Name: N_BEST, dtype: int64"
      ]
     },
     "execution_count": 1001,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['N_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so:1、drop some meaningless values{+, 4}(maybe human error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2、change {0.0 , 1.0 ,2B, 2.0, NO }to{0 , 1 , 2b ,2, 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.N_BEST=='4'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.N_BEST=='+'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.replace(0.0,'0',inplace=True)\n",
    "tumour.replace(1.0,'1',inplace=True)\n",
    "tumour.replace('2B','2b',inplace=True)\n",
    "tumour.replace(2.0,'2',inplace=True)\n",
    "tumour.replace('N0','0',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1004,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      279767\n",
       "1       71759\n",
       "2       48758\n",
       "3       22345\n",
       "1a       5459\n",
       "2b       4367\n",
       "2a       3062\n",
       "1mi      2209\n",
       "2c       1893\n",
       "3a       1356\n",
       "1b       1126\n",
       "1c        284\n",
       "3b        167\n",
       "3c        125\n",
       "X         124\n",
       "Name: N_BEST, dtype: int64"
      ]
     },
     "execution_count": 1004,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['N_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distant metastasis (M)\n",
    "\n",
    "MX: Metastasis cannot be measured.\n",
    "M0: Cancer has not spread to other parts of the body.\n",
    "M1: Cancer has spread to other parts of the body."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     372909\n",
       "1      35091\n",
       "1b     23938\n",
       "1a     10580\n",
       "X        181\n",
       "1c       101\n",
       "2          1\n",
       "Name: M_BEST, dtype: int64"
      ]
     },
     "execution_count": 1005,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['M_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so:1、drop some meaningless values{2}(maybe human error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.M_BEST=='2'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     372909\n",
       "1      35091\n",
       "1b     23938\n",
       "1a     10580\n",
       "X        181\n",
       "1c       101\n",
       "Name: M_BEST, dtype: int64"
      ]
     },
     "execution_count": 1007,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['M_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"STAGE_BEST\"                      \n",
    "\"STAGE_BEST_SYSTEM\"            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      74289\n",
       "1      72102\n",
       "3      57038\n",
       "2      56295\n",
       "1A     46942\n",
       "2b     37102\n",
       "2a     31075\n",
       "3A     24163\n",
       "3B     15619\n",
       "4A      9445\n",
       "3C      5735\n",
       "?       3420\n",
       "0       3281\n",
       "U       2257\n",
       "4B      1917\n",
       "2C       820\n",
       "4C       627\n",
       "1C       285\n",
       "0A        64\n",
       "4S        58\n",
       "1A1       41\n",
       "3S        34\n",
       "1B1       32\n",
       "1S        29\n",
       "1E        28\n",
       "2S        23\n",
       "2A1       16\n",
       "2A2       15\n",
       "1A2       15\n",
       "0IS       13\n",
       "2E         9\n",
       "3E         6\n",
       "1B2        5\n",
       "Name: STAGE_BEST, dtype: int64"
      ]
     },
     "execution_count": 1008,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['STAGE_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so:1、drop some meaningless values{?, U ,1A1,1B1,1A2,2A2,2A1,1B2}(maybe human error)\n",
    "\n",
    "2、change {2b , 2a ,0IS}to{2B , 2A , 0 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.STAGE_BEST=='?'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='U'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='1A1'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='1B1'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='1A2'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='2A2'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='2A1'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.STAGE_BEST=='1B2'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.replace('2b','2B',inplace=True)\n",
    "tumour.replace('2a','2A',inplace=True)\n",
    "tumour.replace('0IS','0',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     74289\n",
       "1     72102\n",
       "3     57038\n",
       "2     56295\n",
       "1A    46942\n",
       "2B    37102\n",
       "2A    31075\n",
       "3A    24163\n",
       "3B    15619\n",
       "4A     9445\n",
       "3C     5735\n",
       "0      3294\n",
       "4B     1917\n",
       "2C      820\n",
       "4C      627\n",
       "1C      285\n",
       "0A       64\n",
       "4S       58\n",
       "3S       34\n",
       "1S       29\n",
       "1E       28\n",
       "2S       23\n",
       "2E        9\n",
       "3E        6\n",
       "Name: STAGE_BEST, dtype: int64"
      ]
     },
     "execution_count": 1011,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['STAGE_BEST'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0    302804\n",
       "20.0     63690\n",
       "23.0     15267\n",
       "21.0      1279\n",
       "24.0       198\n",
       "7.0         63\n",
       "5.0         30\n",
       "Name: STAGE_BEST_SYSTEM, dtype: int64"
      ]
     },
     "execution_count": 1012,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['STAGE_BEST_SYSTEM'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"GRADE\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a grading system for a tumor type is not specified, the following system is generally used :\n",
    "\n",
    "GX: Grade cannot be assessed (undetermined grade)\n",
    "G1: Well differentiated (low grade)\n",
    "G2: Moderately differentiated (intermediate grade)\n",
    "G3: Poorly differentiated (high grade)\n",
    "G4: Undifferentiated (high grade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GX    157113\n",
       "G2    130563\n",
       "G3    107278\n",
       "G1     25607\n",
       "G4      8811\n",
       "Name: GRADE, dtype: int64"
      ]
     },
     "execution_count": 1013,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['GRADE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"ER_STATUS\"                   \n",
    "\"ER_SCORE\"                    \n",
    "\"PR_STATUS\"                   \n",
    "\"PR_SCORE\"                    \n",
    "\"HER2_STATUS\"                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hormone receptor studies such as ER, PR, and HER2/neu are routinely done in breast carcinoma. It not only helps in the prognosis of the tumor but also helps in deciding its treatment. The goal of doing this receptor status is to provide right treatment to the right patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P     49233\n",
       "N      8526\n",
       "U      6690\n",
       "Pm      241\n",
       "X       103\n",
       "B        39\n",
       "Name: ER_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['ER_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0    21779\n",
       "0       3681\n",
       "7.0     2775\n",
       "6.0      846\n",
       "5.0      478\n",
       "4.0      419\n",
       "3.0      384\n",
       "2        340\n",
       "1          7\n",
       "Name: ER_SCORE, dtype: int64"
      ]
     },
     "execution_count": 1015,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['ER_SCORE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([8.0, '0', 7.0, 6.0, 5.0, 4.0, 3.0, '2', '1'], dtype='object')"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.ER_SCORE.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['ER_SCORE'] = tumour['ER_SCORE'].map(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([8.0, 0.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0], dtype='float64')"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.ER_SCORE.value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I changed value type in \"ER_SCORE\" ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P     25591\n",
       "N     11474\n",
       "U      4891\n",
       "X      1310\n",
       "Pm      265\n",
       "B        61\n",
       "Name: PR_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['PR_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0    6479\n",
       "0      4707\n",
       "7.0    2277\n",
       "6.0    1442\n",
       "5.0    1136\n",
       "4.0     899\n",
       "3.0     568\n",
       "2       395\n",
       "1        18\n",
       "Name: PR_SCORE, dtype: int64"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['PR_SCORE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([8.0, '0', 7.0, 6.0, 5.0, 4.0, 3.0, '2', '1'], dtype='object')"
      ]
     },
     "execution_count": 1021,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.PR_SCORE.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['PR_SCORE'] = tumour['PR_SCORE'].map(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([8.0, 0.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0], dtype='float64')"
      ]
     },
     "execution_count": 1023,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.PR_SCORE.value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have changed value type in \"ER_SCORE\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N     47857\n",
       "P      7969\n",
       "B      2415\n",
       "X       186\n",
       "Pm      170\n",
       "Name: HER2_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1024,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['HER2_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"CANCERCAREPLANINTENT\"        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intention of a Cancer Care Plan developed within a Cancer Care Spell.\n",
    "National Codes:\n",
    "\n",
    "C\tCurative\n",
    "P\tPalliative anti-cancer\n",
    "S\tSupportive \n",
    "N\tNo specific cancer treatment \n",
    "Z\tNon-Curative\n",
    "X\tNo active treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C     115032\n",
       "Z      41248\n",
       "9      28976\n",
       "X       8229\n",
       "Cm      2401\n",
       "Zm       461\n",
       "Name: CANCERCAREPLANINTENT, dtype: int64"
      ]
     },
     "execution_count": 1025,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['CANCERCAREPLANINTENT'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"PERFORMANCESTATUS\"          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A World Health Organisation classification indicating a PERSON's status relating to activity/DISABILITY."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     69880\n",
       "9     40231\n",
       "1     38656\n",
       "2     18415\n",
       "3     13294\n",
       "4      3835\n",
       "1m     1906\n",
       "2m     1141\n",
       "3m      632\n",
       "4m      150\n",
       "Name: PERFORMANCESTATUS, dtype: int64"
      ]
     },
     "execution_count": 1026,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['PERFORMANCESTATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"ACE27\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PERSON SCORE recorded during a Cancer Care Spell, where the ASSESSMENT TOOL TYPE is 'Adult Comorbidity Evaluation - 27'.\n",
    "\n",
    "National Codes:\n",
    "\n",
    "0\tNone\n",
    "1\tMild\n",
    "2\tModerate\n",
    "3\tSevere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     23535\n",
       "0     10152\n",
       "1      6162\n",
       "2      4022\n",
       "3      3946\n",
       "3m      160\n",
       "1m      108\n",
       "2m       83\n",
       "Name: ACE27, dtype: int64"
      ]
     },
     "execution_count": 1027,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['ACE27'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"GLEASON_PRIMARY\"            \n",
    "\"GLEASON_SECONDARY\"           \n",
    "\"GLEASON_TERTIARY\"            \n",
    "\"GLEASON_COMBINED\"           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Gleason Grading System is used to help evaluate the prognosis of men with prostate cancer.\n",
    "\n",
    "A pathologist assigns a GLEASON GRADE to the most common Tumour pattern in a Biopsy specimen (Primary Grade) then the second most common (Secondary Grade). The grades are added together to give the Gleason Score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    21672\n",
       "4.0    12994\n",
       "5.0     1642\n",
       "2         15\n",
       "1          1\n",
       "Name: GLEASON_PRIMARY, dtype: int64"
      ]
     },
     "execution_count": 1028,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['GLEASON_PRIMARY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    8765\n",
       "3.0    8759\n",
       "2      2150\n",
       "5.0    1581\n",
       "Name: GLEASON_SECONDARY, dtype: int64"
      ]
     },
     "execution_count": 1029,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['GLEASON_SECONDARY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0     9997\n",
       "6.0     7246\n",
       "8.0     2313\n",
       "9.0     1386\n",
       "5.0      310\n",
       "10.0      47\n",
       "4.0        7\n",
       "Name: GLEASON_COMBINED, dtype: int64"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['GLEASON_COMBINED'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes pathologists will also give a grade to a third component of the specimen (Tertiary Grade) although this is recorded separately and is not added to the score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    330\n",
       "4.0     90\n",
       "3.0     65\n",
       "Name: GLEASON_TERTIARY, dtype: int64"
      ]
     },
     "execution_count": 1031,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['GLEASON_TERTIARY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data are relatively small ,and considering that it is  recorded separately and is not added to the score,I am going to drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop('GLEASON_TERTIARY',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"DATE_FIRST_SURGERY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         289227\n",
       "unique          1478\n",
       "top       2014-04-29\n",
       "freq             409\n",
       "Name: DATE_FIRST_SURGERY, dtype: object"
      ]
     },
     "execution_count": 1033,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.DATE_FIRST_SURGERY.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change object to exclusive type (datatime 64[ns]) for later statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28a9ee61eb8>"
      ]
     },
     "execution_count": 1034,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tumour['DATE_FIRST_SURGERY']=pd.to_datetime(tumour.DATE_FIRST_SURGERY)\n",
    "tumour['year_firstsurgery']=tumour.DATE_FIRST_SURGERY.dt.year\n",
    "tumour.year_firstsurgery.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to see the frequency of per year.And we can see that 2014 saw the most surgeries(just as describe() shows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"LATERALITY\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An indication of the:\n",
    "\n",
    "side of the body for a Tumour relating to paired organs within a PATIENT or\n",
    "radiologically determined laterality of the Lesion(s).\n",
    "National Codes:\n",
    "\n",
    "L\tLeft\n",
    "R\tRight\n",
    "M\tMidline\n",
    "B\tBilateral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    188848\n",
       "R     97359\n",
       "L     89186\n",
       "8     52946\n",
       "9      7847\n",
       "B       629\n",
       "M       184\n",
       "Name: LATERALITY, dtype: int64"
      ]
     },
     "execution_count": 1035,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['LATERALITY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are two '8',let's see why"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['8', 'R', 'L', 8, '9', 'B', 'M'], dtype='object')"
      ]
     },
     "execution_count": 1036,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['LATERALITY'].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so I am going to drop value{8} (btw:all value{9 or 99} in this table means not known)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.LATERALITY=='8'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.LATERALITY==8].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R    97359\n",
       "L    89186\n",
       "9     7847\n",
       "B      629\n",
       "M      184\n",
       "Name: LATERALITY, dtype: int64"
      ]
     },
     "execution_count": 1038,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['LATERALITY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"QUINTILE_2015\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2                     40670\n",
       "3                     39705\n",
       "1 - least deprived    38432\n",
       "5 - most deprived     38403\n",
       "4                     37995\n",
       "Name: QUINTILE_2015, dtype: int64"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['QUINTILE_2015'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if there any duplicate rows(I guess none,but let's check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1040,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at this table after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TUMOURID</th>\n",
       "      <th>PATIENTID</th>\n",
       "      <th>DIAGNOSISDATEBEST</th>\n",
       "      <th>SITE_ICD10_O2</th>\n",
       "      <th>SITE_ICD10_O2_3CHAR</th>\n",
       "      <th>MORPH_ICD10_O2</th>\n",
       "      <th>BEHAVIOUR_ICD10_O2</th>\n",
       "      <th>T_BEST</th>\n",
       "      <th>N_BEST</th>\n",
       "      <th>M_BEST</th>\n",
       "      <th>...</th>\n",
       "      <th>CNS</th>\n",
       "      <th>ACE27</th>\n",
       "      <th>GLEASON_PRIMARY</th>\n",
       "      <th>GLEASON_SECONDARY</th>\n",
       "      <th>GLEASON_COMBINED</th>\n",
       "      <th>DATE_FIRST_SURGERY</th>\n",
       "      <th>LATERALITY</th>\n",
       "      <th>QUINTILE_2015</th>\n",
       "      <th>year_diagnosis</th>\n",
       "      <th>year_firstsurgery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>10000082</td>\n",
       "      <td>10000082</td>\n",
       "      <td>2014-11-07</td>\n",
       "      <td>C443</td>\n",
       "      <td>C44</td>\n",
       "      <td>8070</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Y1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-11-07</td>\n",
       "      <td>R</td>\n",
       "      <td>1 - least deprived</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10000089</td>\n",
       "      <td>10000089</td>\n",
       "      <td>2015-03-16</td>\n",
       "      <td>C447</td>\n",
       "      <td>C44</td>\n",
       "      <td>8070</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>L</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>10000168</td>\n",
       "      <td>10000168</td>\n",
       "      <td>2015-05-24</td>\n",
       "      <td>C442</td>\n",
       "      <td>C44</td>\n",
       "      <td>8070</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NI</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-05-27</td>\n",
       "      <td>L</td>\n",
       "      <td>1 - least deprived</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>10000340</td>\n",
       "      <td>10000340</td>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>C446</td>\n",
       "      <td>C44</td>\n",
       "      <td>8090</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>R</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>10000645</td>\n",
       "      <td>10000645</td>\n",
       "      <td>2015-02-17</td>\n",
       "      <td>C443</td>\n",
       "      <td>C44</td>\n",
       "      <td>8070</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>R</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     TUMOURID  PATIENTID DIAGNOSISDATEBEST SITE_ICD10_O2 SITE_ICD10_O2_3CHAR  \\\n",
       "79   10000082   10000082        2014-11-07          C443                 C44   \n",
       "86   10000089   10000089        2015-03-16          C447                 C44   \n",
       "165  10000168   10000168        2015-05-24          C442                 C44   \n",
       "334  10000340   10000340        2014-09-05          C446                 C44   \n",
       "635  10000645   10000645        2015-02-17          C443                 C44   \n",
       "\n",
       "    MORPH_ICD10_O2 BEHAVIOUR_ICD10_O2 T_BEST N_BEST M_BEST  ...  CNS  ACE27  \\\n",
       "79            8070                  3      2      0      0  ...   Y1    NaN   \n",
       "86            8070                  3      1      0      0  ...   99    NaN   \n",
       "165           8070                  3      2      0      0  ...   NI      1   \n",
       "334           8090                  3      1      0      0  ...  NaN    NaN   \n",
       "635           8070                  3      1      0      0  ...  NaN    NaN   \n",
       "\n",
       "    GLEASON_PRIMARY GLEASON_SECONDARY GLEASON_COMBINED DATE_FIRST_SURGERY  \\\n",
       "79              NaN               NaN              NaN         2014-11-07   \n",
       "86              NaN               NaN              NaN         2015-04-24   \n",
       "165             NaN               NaN              NaN         2015-05-27   \n",
       "334             NaN               NaN              NaN         2014-09-05   \n",
       "635             NaN               NaN              NaN                NaT   \n",
       "\n",
       "     LATERALITY       QUINTILE_2015 year_diagnosis  year_firstsurgery  \n",
       "79            R  1 - least deprived           2014             2014.0  \n",
       "86            L                   2           2015             2015.0  \n",
       "165           L  1 - least deprived           2015             2015.0  \n",
       "334           R                   3           2014             2014.0  \n",
       "635           R                   4           2015                NaN  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 1041,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195205, 35)"
      ]
     },
     "execution_count": 1042,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have dropped the column\"GLEASON_TERTIARY\" and added column \"year_diagnosis\" and \"year_firstsurgery\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyze（statistical analysis+graphs）some other missing values in specific colunmns I am going to handle in analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline(already be imputed previously)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is the latest time of first_surgery and diagnosis_best?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-03-03 00:00:00')"
      ]
     },
     "execution_count": 1044,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.DATE_FIRST_SURGERY.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-31 00:00:00')"
      ]
     },
     "execution_count": 1045,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.DIAGNOSISDATEBEST.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many days from the oldest first_surgery day and diagnosis_best?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1718"
      ]
     },
     "execution_count": 1046,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tumour.DATE_FIRST_SURGERY.max()-tumour.DATE_FIRST_SURGERY.min()).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1094"
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tumour.DIAGNOSISDATEBEST.max()-tumour.DIAGNOSISDATEBEST.min()).days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to see what is the distribution of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tumour['AGE']=tumour['AGE'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    195205.000000\n",
       "mean         66.472775\n",
       "std          14.415679\n",
       "min           0.000000\n",
       "25%          57.000000\n",
       "50%          68.000000\n",
       "75%          77.000000\n",
       "max         105.000000\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.AGE.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the oldest age is:105\n"
     ]
    }
   ],
   "source": [
    "age_max = tumour['AGE'].max()\n",
    "print(\"the oldest age is:%d\"%age_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'AGE')"
      ]
     },
     "execution_count": 1051,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1=sns.distplot(tumour['AGE'],rug=True,color='#1E90FF')\n",
    "g1.set_title('the distribution of age')\n",
    "plt.xlabel('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and what is the distribution of sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     195205\n",
       "unique         2\n",
       "top            2\n",
       "freq      130442\n",
       "Name: SEX, dtype: object"
      ]
     },
     "execution_count": 1052,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.SEX.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total numbel of male:64763\n"
     ]
    }
   ],
   "source": [
    "male=tumour[tumour['SEX']=='1'].shape[0]\n",
    "print(\"total numbel of male:%d\"%male )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total numbel of female:130442\n"
     ]
    }
   ],
   "source": [
    "female=tumour[tumour['SEX']=='2'].shape[0]\n",
    "print(\"total numbel of female:%d\"%female )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000028A64C09D30>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 1055,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex=pd.DataFrame([64763,130442],index=['M','F'],columns=['sexdistribution'])\n",
    "sex.plot(kind='pie',subplots=True,figsize=(8,8),autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is the distribution of side of body for tumour(laterality)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R    97359\n",
       "L    89186\n",
       "9     7847\n",
       "B      629\n",
       "M      184\n",
       "Name: LATERALITY, dtype: int64"
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['LATERALITY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000028AA10BA940>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "laterality=pd.DataFrame([97359,89186,7847,629,184],index=['R','L','9','B','M'],columns=['laterality_distribution'])\n",
    "laterality.plot(kind='pie',subplots=True,figsize=(8,8),textprops={'fontsize': 16, 'color':'w'},autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "most of patients' tumours are in the body of right or left.few patients' tumours are in the middle or both sides of the body."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there any connection between age and sex?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(btw:I don't drop AGE==0,because I think it refers to infants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28aa11166d8>"
      ]
     },
     "execution_count": 1058,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_difference = tumour.groupby('SEX')['AGE'].agg(['min','max','mean'])\n",
    "sex_difference.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "male and female patients' age difference is not significant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is the relationship between sex and the type of tumour(grade)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     190603\n",
       "unique         5\n",
       "top           GX\n",
       "freq       78863\n",
       "Name: GRADE, dtype: object"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.GRADE.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "grading systems differ depending on the type of cancer                                                       \n",
    "so:\n",
    "sort GRADE by its meaning:GX<G1<G2<G3<G4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apple\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3325: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "tumour['GRADE']=tumour.GRADE.astype('category',categories=['GX','G1','G2','G3','G4'],ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28aa1167978>"
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3=sns.countplot(x='GRADE',hue='SEX',data=tumour)\n",
    "g3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "majority of male patients' tumours cannot be assessed and majority of female patients' tumours are moderately differentiated.Very few patients' tumours spead faster and grow rapidly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to understand the meaning of gleason system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Secondary_Grade')"
      ]
     },
     "execution_count": 1062,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "x=tumour.GLEASON_PRIMARY\n",
    "y=tumour.GLEASON_SECONDARY\n",
    "z=tumour.GLEASON_COMBINED\n",
    "fig,ax=plt.subplots(figsize=(12,10))\n",
    "bubble=ax.scatter(x,y,s=(z-np.min(z)+0.1)*1000,c=z,linewidth=0.5,alpha=0.5)\n",
    "ax.grid()\n",
    "fig.colorbar(bubble)   \n",
    "ax.set_xlabel('Primary_Grade',fontsize=15)\n",
    "ax.set_ylabel('Secondary_Grade',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Gleason score of each area is 1~5, and the Gleason score is the sum of the Gleason score of the primary and secondary areas to form the cancer grading constant. Its characteristics can be summarized as follows :1 grading principle (growth mode),2 aspects (main growth mode and secondary growth mode),5 grading system (grade 1-5 grading of gland differentiation),10 points (the least differentiated is 5+5=10)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are connections between the score of PR ER HER2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    164673\n",
       "8.0     21653\n",
       "0.0      3656\n",
       "7.0      2763\n",
       "6.0       840\n",
       "5.0       475\n",
       "4.0       419\n",
       "3.0       381\n",
       "2.0       338\n",
       "1.0         7\n",
       "Name: ER_SCORE, dtype: int64"
      ]
     },
     "execution_count": 1063,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['ER_SCORE'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['ER_SCORE'].fillna(value=9.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['PR_SCORE'].fillna(value=9.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([9.0, 8.0, 0.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0], dtype='float64')"
      ]
     },
     "execution_count": 1066,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['PR_SCORE'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'ER')"
      ]
     },
     "execution_count": 1067,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "x1=tumour.PR_SCORE\n",
    "y1=tumour.ER_SCORE\n",
    "z1=tumour.GLEASON_COMBINED\n",
    "fig,ax=plt.subplots(figsize=(12,10))\n",
    "bubble=ax.scatter(x1,y1,s=(z1-np.min(z1)+0.1)*1000,c=z1,linewidth=0.5,alpha=0.5)\n",
    "ax.grid()\n",
    "fig.colorbar(bubble)   \n",
    "ax.set_xlabel('PR',fontsize=15)\n",
    "ax.set_ylabel('ER',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there relationship between age ,sex and incidence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['9', '0', '1', '2', '3', '3m', '1m', '2m'], dtype='object')"
      ]
     },
     "execution_count": 1068,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.ACE27.value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9 means unknow,m means minus,so just take representative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour2=tumour.loc[(tumour['ACE27']=='0')|(tumour['ACE27']=='1')|(tumour['ACE27']=='2')|(tumour['ACE27']=='3')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apple\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "tumour2['ACE27']=pd.to_numeric(tumour2['ACE27'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 1071,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour2.ACE27.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28ad8060cf8>"
      ]
     },
     "execution_count": 1072,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=tumour2['SEX'],y=tumour2['AGE'],hue = tumour2['ACE27'],linewidth=1,palette='Set3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there any relationship between the patients' stage sex and age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['0', '1', '9', '2', '3', '4', '1m', '2m', '3m', '4m'], dtype='object')"
      ]
     },
     "execution_count": 1073,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour.PERFORMANCESTATUS.value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0:normal activity 1:cares for self 2:occasional assistance 3:nursing care needed 4:bedridden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9 means unknow,m means minus,so just take representative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour3=tumour.loc[(tumour['PERFORMANCESTATUS']=='0')|(tumour['PERFORMANCESTATUS']=='1')|(tumour['PERFORMANCESTATUS']=='2')|(tumour['PERFORMANCESTATUS']=='3')|(tumour['PERFORMANCESTATUS']=='4')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apple\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "tumour3['PERFORMANCESTATUS']=pd.to_numeric(tumour3['PERFORMANCESTATUS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28ac24e7588>"
      ]
     },
     "execution_count": 1076,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=tumour3['SEX'],y=tumour3['AGE'],hue = tumour3['PERFORMANCESTATUS'],linewidth=1,palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "know more about status of ER PR HER2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour['PR_STATUS'].fillna(value='M',inplace=True)\n",
    "tumour['ER_STATUS'].fillna(value='M',inplace=True)\n",
    "tumour['HER2_STATUS'].fillna(value='M',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumour.drop(tumour[tumour.PR_STATUS=='U'].index,inplace=True)\n",
    "tumour.drop(tumour[tumour.ER_STATUS=='U'].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M     130852\n",
       "P      48585\n",
       "N       8397\n",
       "Pm       235\n",
       "X         91\n",
       "B         30\n",
       "Name: ER_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1079,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['ER_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M     150129\n",
       "P      25355\n",
       "N      11242\n",
       "X       1139\n",
       "Pm       264\n",
       "B         61\n",
       "Name: PR_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1080,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['PR_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M     136061\n",
       "N      42510\n",
       "P       7105\n",
       "B       2188\n",
       "X        176\n",
       "Pm       150\n",
       "Name: HER2_STATUS, dtype: int64"
      ]
     },
     "execution_count": 1081,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tumour['HER2_STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ER</th>\n",
       "      <th>PR</th>\n",
       "      <th>HER2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>130852</td>\n",
       "      <td>150129</td>\n",
       "      <td>136061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>48585</td>\n",
       "      <td>25355</td>\n",
       "      <td>7105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>8397</td>\n",
       "      <td>11242</td>\n",
       "      <td>42510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pm</th>\n",
       "      <td>235</td>\n",
       "      <td>264</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "      <td>61</td>\n",
       "      <td>2188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>91</td>\n",
       "      <td>1139</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ER      PR    HER2\n",
       "Index                        \n",
       "M      130852  150129  136061\n",
       "P       48585   25355    7105\n",
       "N        8397   11242   42510\n",
       "Pm        235     264     150\n",
       "B          30      61    2188\n",
       "X          91    1139     176"
      ]
     },
     "execution_count": 1082,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[['M',130852,150129,136061],['P',48585,25355,7105],['N',8397,11242,42510],['Pm',235,264,150],['B',30,61,2188],['X',91,1139,176]]\n",
    "tumour4=pd.DataFrame(data,columns=['Index','ER','PR','HER2'])\n",
    "tumour5=tumour4.set_index('Index')\n",
    "tumour5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28a917de748>"
      ]
     },
     "execution_count": 1083,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tumour5.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "M means missing values,so we can see there are a lot of missing values.But why?\n",
    "\n",
    "When the cells become cancerous, some and all of ER and PR are missing. \n",
    "If the cell still retains ER and/or PR, the growth and proliferation of the breast cancer cell is still regulated by endocrine; \n",
    "\n",
    "If ER and/or PR is absent, the growth of the breast cancer cell And proliferation is no longer regulated by endocrine, called non-hormone-dependent breast cancer.\n",
    "\n",
    "And if the cancer tests negative for over-expression of the her2, weekly chemotherapy is an effective option with acceptable side effects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reflect and hypothesise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every member in our group was assigned different analysis tasks in terms of drugs, types of tumours, relatinship of medical theoretical data and so on in order to avoid duplication of effort.\n",
    "\n",
    "When I was doing the above analysis,I already made comments aside about the basic information of the table,the meaning of each column,and the possible connection between each column.                                                       \n",
    " \n",
    "The number of peopleb diagnosed with cancer increases every year,and most of them were in their 60s and 70s,and women were over represented(66.82%).However,some lived to be over a hundred though they had cancer.Male patients' tumours were more likely to not be assessed while female patients' tumours are moderately differentiated. \n",
    "\n",
    "In gleason system,if only two modes are seen, the score for the first number is the primary score for the tumor and the second number is the medium score. If you see three patterns, the score for the first number will be the primary grade and the highest number of patterns for the second grade.\n",
    "\n",
    "There is no significant difference between male and female in incidence rate(ACE27) and the state at the time(PERFORMANCESTATUS),and as the age increases, these indexes will rise.But there are also some abnormal values that do not match.\n",
    "\n",
    "ER PR HER2 can reflect the control of tumors.But I still don't understand some meanings about these data.\n",
    "\n",
    "Our group already read some paper about cancer and tumour,but there still have data we cannot explain ,so we are going to email the data owner and ask some questions.And we will study some algorithms for assignment3, "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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