assessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assessing and Building Intuition\n",
    "Once you have your data loaded into dataframes, Pandas makes a quick investigation of the data really easy. Let's explore some helpful methods for assessing and building intuition about a dataset. We can use the cancer data from before to help us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_max</th>\n",
       "      <th>texture_max</th>\n",
       "      <th>perimeter_max</th>\n",
       "      <th>area_max</th>\n",
       "      <th>smoothness_max</th>\n",
       "      <th>compactness_max</th>\n",
       "      <th>concavity_max</th>\n",
       "      <th>concave_points_max</th>\n",
       "      <th>symmetry_max</th>\n",
       "      <th>fractal_dimension_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99           NaN          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3              NaN           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "           ...            radius_max  texture_max  perimeter_max  area_max  \\\n",
       "0          ...                 25.38          NaN         184.60    2019.0   \n",
       "1          ...                 24.99        23.41         158.80    1956.0   \n",
       "2          ...                 23.57        25.53         152.50    1709.0   \n",
       "3          ...                 14.91        26.50          98.87     567.7   \n",
       "4          ...                 22.54        16.67         152.20    1575.0   \n",
       "\n",
       "   smoothness_max  compactness_max  concavity_max  concave_points_max  \\\n",
       "0          0.1622           0.6656         0.7119              0.2654   \n",
       "1          0.1238           0.1866         0.2416              0.1860   \n",
       "2          0.1444           0.4245         0.4504              0.2430   \n",
       "3             NaN           0.8663         0.6869              0.2575   \n",
       "4          0.1374           0.2050         0.4000              0.1625   \n",
       "\n",
       "   symmetry_max  fractal_dimension_max  \n",
       "0        0.4601                0.11890  \n",
       "1        0.2750                0.08902  \n",
       "2        0.3613                0.08758  \n",
       "3        0.6638                0.17300  \n",
       "4        0.2364                0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('cancer_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns a tuple of the dimensions of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          int64\n",
       "diagnosis                  object\n",
       "radius_mean               float64\n",
       "texture_mean              float64\n",
       "perimeter_mean            float64\n",
       "area_mean                 float64\n",
       "smoothness_mean           float64\n",
       "compactness_mean          float64\n",
       "concavity_mean            float64\n",
       "concave_points_mean       float64\n",
       "symmetry_mean             float64\n",
       "fractal_dimension_mean    float64\n",
       "radius_SE                 float64\n",
       "texture_SE                float64\n",
       "perimeter_SE              float64\n",
       "area_SE                   float64\n",
       "smoothness_SE             float64\n",
       "compactness_SE            float64\n",
       "concavity_SE              float64\n",
       "concave_points_SE         float64\n",
       "symmetry_SE               float64\n",
       "fractal_dimension_SE      float64\n",
       "radius_max                float64\n",
       "texture_max               float64\n",
       "perimeter_max             float64\n",
       "area_max                  float64\n",
       "smoothness_max            float64\n",
       "compactness_max           float64\n",
       "concavity_max             float64\n",
       "concave_points_max        float64\n",
       "symmetry_max              float64\n",
       "fractal_dimension_max     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns the datatypes of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# although the datatype for diagnosis appears to be object, further\n",
    "# investigation shows it's a string\n",
    "type(df['diagnosis'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas actually stores [pointers](https://en.wikipedia.org/wiki/Pointer_(computer_programming) to strings in dataframes and series, which is why `object` instead of `str` appears as the datatype. Understanding this is not essential for data analysis - just know that strings will appear as objects in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 32 columns):\n",
      "id                        569 non-null int64\n",
      "diagnosis                 569 non-null object\n",
      "radius_mean               569 non-null float64\n",
      "texture_mean              548 non-null float64\n",
      "perimeter_mean            569 non-null float64\n",
      "area_mean                 569 non-null float64\n",
      "smoothness_mean           521 non-null float64\n",
      "compactness_mean          569 non-null float64\n",
      "concavity_mean            569 non-null float64\n",
      "concave_points_mean       569 non-null float64\n",
      "symmetry_mean             504 non-null float64\n",
      "fractal_dimension_mean    569 non-null float64\n",
      "radius_SE                 569 non-null float64\n",
      "texture_SE                548 non-null float64\n",
      "perimeter_SE              569 non-null float64\n",
      "area_SE                   569 non-null float64\n",
      "smoothness_SE             521 non-null float64\n",
      "compactness_SE            569 non-null float64\n",
      "concavity_SE              569 non-null float64\n",
      "concave_points_SE         569 non-null float64\n",
      "symmetry_SE               504 non-null float64\n",
      "fractal_dimension_SE      569 non-null float64\n",
      "radius_max                569 non-null float64\n",
      "texture_max               548 non-null float64\n",
      "perimeter_max             569 non-null float64\n",
      "area_max                  569 non-null float64\n",
      "smoothness_max            521 non-null float64\n",
      "compactness_max           569 non-null float64\n",
      "concavity_max             569 non-null float64\n",
      "concave_points_max        569 non-null float64\n",
      "symmetry_max              504 non-null float64\n",
      "fractal_dimension_max     569 non-null float64\n",
      "dtypes: float64(30), int64(1), object(1)\n",
      "memory usage: 142.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# this displays a concise summary of the dataframe,\n",
    "# including the number of non-null values in each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        562\n",
       "diagnosis                   2\n",
       "radius_mean               451\n",
       "texture_mean              459\n",
       "perimeter_mean            516\n",
       "area_mean                 532\n",
       "smoothness_mean           434\n",
       "compactness_mean          530\n",
       "concavity_mean            530\n",
       "concave_points_mean       536\n",
       "symmetry_mean             394\n",
       "fractal_dimension_mean    494\n",
       "radius_SE                 535\n",
       "texture_SE                499\n",
       "perimeter_SE              526\n",
       "area_SE                   523\n",
       "smoothness_SE             495\n",
       "compactness_SE            534\n",
       "concavity_SE              526\n",
       "concave_points_SE         500\n",
       "symmetry_SE               438\n",
       "fractal_dimension_SE      539\n",
       "radius_max                452\n",
       "texture_max               490\n",
       "perimeter_max             510\n",
       "area_max                  537\n",
       "smoothness_max            378\n",
       "compactness_max           523\n",
       "concavity_max             532\n",
       "concave_points_max        485\n",
       "symmetry_max              449\n",
       "fractal_dimension_max     530\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns the number of unique values in each column\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_max</th>\n",
       "      <th>texture_max</th>\n",
       "      <th>perimeter_max</th>\n",
       "      <th>area_max</th>\n",
       "      <th>smoothness_max</th>\n",
       "      <th>compactness_max</th>\n",
       "      <th>concavity_max</th>\n",
       "      <th>concave_points_max</th>\n",
       "      <th>symmetry_max</th>\n",
       "      <th>fractal_dimension_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.051467e+07</td>\n",
       "      <td>14.113021</td>\n",
       "      <td>19.293431</td>\n",
       "      <td>91.877909</td>\n",
       "      <td>653.288576</td>\n",
       "      <td>0.096087</td>\n",
       "      <td>0.104536</td>\n",
       "      <td>0.088620</td>\n",
       "      <td>0.048837</td>\n",
       "      <td>0.181091</td>\n",
       "      <td>...</td>\n",
       "      <td>16.261896</td>\n",
       "      <td>25.660803</td>\n",
       "      <td>107.211142</td>\n",
       "      <td>880.163796</td>\n",
       "      <td>0.132090</td>\n",
       "      <td>0.254557</td>\n",
       "      <td>0.271681</td>\n",
       "      <td>0.114377</td>\n",
       "      <td>0.288856</td>\n",
       "      <td>0.084012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250417e+08</td>\n",
       "      <td>3.506148</td>\n",
       "      <td>4.327287</td>\n",
       "      <td>24.162787</td>\n",
       "      <td>349.476899</td>\n",
       "      <td>0.013924</td>\n",
       "      <td>0.052674</td>\n",
       "      <td>0.079011</td>\n",
       "      <td>0.038578</td>\n",
       "      <td>0.027899</td>\n",
       "      <td>...</td>\n",
       "      <td>4.841175</td>\n",
       "      <td>6.202916</td>\n",
       "      <td>33.621975</td>\n",
       "      <td>570.498628</td>\n",
       "      <td>0.022685</td>\n",
       "      <td>0.158042</td>\n",
       "      <td>0.208298</td>\n",
       "      <td>0.065760</td>\n",
       "      <td>0.062520</td>\n",
       "      <td>0.018151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.691040e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.167500</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086050</td>\n",
       "      <td>0.065260</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020360</td>\n",
       "      <td>0.161800</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.017500</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.112500</td>\n",
       "      <td>0.064020</td>\n",
       "      <td>0.247650</td>\n",
       "      <td>0.071270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.785000</td>\n",
       "      <td>86.340000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095780</td>\n",
       "      <td>0.094530</td>\n",
       "      <td>0.061550</td>\n",
       "      <td>0.033700</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.370000</td>\n",
       "      <td>97.650000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131200</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.100100</td>\n",
       "      <td>0.280650</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.910251e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.825000</td>\n",
       "      <td>103.800000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.104800</td>\n",
       "      <td>0.130500</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>0.074040</td>\n",
       "      <td>0.195750</td>\n",
       "      <td>...</td>\n",
       "      <td>18.760000</td>\n",
       "      <td>29.675000</td>\n",
       "      <td>125.100000</td>\n",
       "      <td>1070.000000</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>0.339900</td>\n",
       "      <td>0.385300</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>0.317525</td>\n",
       "      <td>0.092110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    548.000000      569.000000   569.000000   \n",
       "mean   3.051467e+07    14.113021     19.293431       91.877909   653.288576   \n",
       "std    1.250417e+08     3.506148      4.327287       24.162787   349.476899   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.691040e+05    11.700000     16.167500       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.785000       86.340000   551.100000   \n",
       "75%    8.910251e+06    15.780000     21.825000      103.800000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "count       521.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096087          0.104536        0.088620             0.048837   \n",
       "std           0.013924          0.052674        0.079011             0.038578   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086050          0.065260        0.029560             0.020360   \n",
       "50%           0.095780          0.094530        0.061550             0.033700   \n",
       "75%           0.104800          0.130500        0.131900             0.074040   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean          ...            radius_max  texture_max  \\\n",
       "count     504.000000          ...            569.000000   548.000000   \n",
       "mean        0.181091          ...             16.261896    25.660803   \n",
       "std         0.027899          ...              4.841175     6.202916   \n",
       "min         0.106000          ...              7.930000    12.020000   \n",
       "25%         0.161800          ...             13.010000    21.017500   \n",
       "50%         0.178950          ...             14.970000    25.370000   \n",
       "75%         0.195750          ...             18.760000    29.675000   \n",
       "max         0.304000          ...             36.040000    49.540000   \n",
       "\n",
       "       perimeter_max     area_max  smoothness_max  compactness_max  \\\n",
       "count     569.000000   569.000000      521.000000       569.000000   \n",
       "mean      107.211142   880.163796        0.132090         0.254557   \n",
       "std        33.621975   570.498628        0.022685         0.158042   \n",
       "min        50.410000   185.200000        0.071170         0.027290   \n",
       "25%        84.110000   515.300000        0.116600         0.146000   \n",
       "50%        97.650000   686.500000        0.131200         0.211900   \n",
       "75%       125.100000  1070.000000        0.145000         0.339900   \n",
       "max       251.200000  4254.000000        0.222600         1.058000   \n",
       "\n",
       "       concavity_max  concave_points_max  symmetry_max  fractal_dimension_max  \n",
       "count     569.000000          569.000000    504.000000             569.000000  \n",
       "mean        0.271681            0.114377      0.288856               0.084012  \n",
       "std         0.208298            0.065760      0.062520               0.018151  \n",
       "min         0.000000            0.000000      0.156500               0.055040  \n",
       "25%         0.112500            0.064020      0.247650               0.071270  \n",
       "50%         0.226700            0.100100      0.280650               0.080040  \n",
       "75%         0.385300            0.162500      0.317525               0.092110  \n",
       "max         1.252000            0.291000      0.663800               0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns useful descriptive statistics for each column of data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_max</th>\n",
       "      <th>texture_max</th>\n",
       "      <th>perimeter_max</th>\n",
       "      <th>area_max</th>\n",
       "      <th>smoothness_max</th>\n",
       "      <th>compactness_max</th>\n",
       "      <th>concavity_max</th>\n",
       "      <th>concave_points_max</th>\n",
       "      <th>symmetry_max</th>\n",
       "      <th>fractal_dimension_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99           NaN          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3              NaN           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "           ...            radius_max  texture_max  perimeter_max  area_max  \\\n",
       "0          ...                 25.38          NaN         184.60    2019.0   \n",
       "1          ...                 24.99        23.41         158.80    1956.0   \n",
       "2          ...                 23.57        25.53         152.50    1709.0   \n",
       "3          ...                 14.91        26.50          98.87     567.7   \n",
       "4          ...                 22.54        16.67         152.20    1575.0   \n",
       "\n",
       "   smoothness_max  compactness_max  concavity_max  concave_points_max  \\\n",
       "0          0.1622           0.6656         0.7119              0.2654   \n",
       "1          0.1238           0.1866         0.2416              0.1860   \n",
       "2          0.1444           0.4245         0.4504              0.2430   \n",
       "3             NaN           0.8663         0.6869              0.2575   \n",
       "4          0.1374           0.2050         0.4000              0.1625   \n",
       "\n",
       "   symmetry_max  fractal_dimension_max  \n",
       "0        0.4601                0.11890  \n",
       "1        0.2750                0.08902  \n",
       "2        0.3613                0.08758  \n",
       "3        0.6638                0.17300  \n",
       "4        0.2364                0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this returns the first few lines in our dataframe\n",
    "# by default, it returns the first five\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_max</th>\n",
       "      <th>texture_max</th>\n",
       "      <th>perimeter_max</th>\n",
       "      <th>area_max</th>\n",
       "      <th>smoothness_max</th>\n",
       "      <th>compactness_max</th>\n",
       "      <th>concavity_max</th>\n",
       "      <th>concave_points_max</th>\n",
       "      <th>symmetry_max</th>\n",
       "      <th>fractal_dimension_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
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       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
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       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>843786</td>\n",
       "      <td>M</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.17410</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>844359</td>\n",
       "      <td>M</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.07400</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.1442</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84458202</td>\n",
       "      <td>M</td>\n",
       "      <td>13.71</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.05985</td>\n",
       "      <td>...</td>\n",
       "      <td>17.06</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.1654</td>\n",
       "      <td>0.3682</td>\n",
       "      <td>0.2678</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>844981</td>\n",
       "      <td>M</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.09353</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84501001</td>\n",
       "      <td>M</td>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>...</td>\n",
       "      <td>15.09</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>845636</td>\n",
       "      <td>M</td>\n",
       "      <td>16.02</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "      <td>0.08206</td>\n",
       "      <td>0.06669</td>\n",
       "      <td>0.03299</td>\n",
       "      <td>0.03323</td>\n",
       "      <td>...</td>\n",
       "      <td>19.19</td>\n",
       "      <td>33.88</td>\n",
       "      <td>123.80</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.1181</td>\n",
       "      <td>0.1551</td>\n",
       "      <td>0.1459</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.08452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>84610002</td>\n",
       "      <td>M</td>\n",
       "      <td>15.78</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>0.12920</td>\n",
       "      <td>0.09954</td>\n",
       "      <td>0.06606</td>\n",
       "      <td>...</td>\n",
       "      <td>20.42</td>\n",
       "      <td>27.28</td>\n",
       "      <td>136.50</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>0.1396</td>\n",
       "      <td>0.5609</td>\n",
       "      <td>0.3965</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.10480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>846226</td>\n",
       "      <td>M</td>\n",
       "      <td>19.17</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.09740</td>\n",
       "      <td>0.24580</td>\n",
       "      <td>0.20650</td>\n",
       "      <td>0.11180</td>\n",
       "      <td>...</td>\n",
       "      <td>20.96</td>\n",
       "      <td>29.94</td>\n",
       "      <td>151.70</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.1037</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>0.3639</td>\n",
       "      <td>0.17670</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.10230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>846381</td>\n",
       "      <td>M</td>\n",
       "      <td>15.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>0.10020</td>\n",
       "      <td>0.09938</td>\n",
       "      <td>0.05364</td>\n",
       "      <td>...</td>\n",
       "      <td>16.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.00</td>\n",
       "      <td>876.5</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>0.1924</td>\n",
       "      <td>0.2322</td>\n",
       "      <td>0.11190</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.06287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>84667401</td>\n",
       "      <td>M</td>\n",
       "      <td>13.73</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>0.21280</td>\n",
       "      <td>0.08025</td>\n",
       "      <td>...</td>\n",
       "      <td>15.03</td>\n",
       "      <td>32.01</td>\n",
       "      <td>108.80</td>\n",
       "      <td>697.7</td>\n",
       "      <td>0.1651</td>\n",
       "      <td>0.7725</td>\n",
       "      <td>0.6943</td>\n",
       "      <td>0.22080</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>0.14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>84799002</td>\n",
       "      <td>M</td>\n",
       "      <td>14.54</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.15950</td>\n",
       "      <td>0.16390</td>\n",
       "      <td>0.07364</td>\n",
       "      <td>...</td>\n",
       "      <td>17.46</td>\n",
       "      <td>37.13</td>\n",
       "      <td>124.10</td>\n",
       "      <td>943.2</td>\n",
       "      <td>0.1678</td>\n",
       "      <td>0.6577</td>\n",
       "      <td>0.7026</td>\n",
       "      <td>0.17120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>848406</td>\n",
       "      <td>M</td>\n",
       "      <td>14.68</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.09867</td>\n",
       "      <td>0.07200</td>\n",
       "      <td>0.07395</td>\n",
       "      <td>0.05259</td>\n",
       "      <td>...</td>\n",
       "      <td>19.07</td>\n",
       "      <td>30.88</td>\n",
       "      <td>123.40</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.1464</td>\n",
       "      <td>0.1871</td>\n",
       "      <td>0.2914</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>0.3029</td>\n",
       "      <td>0.08216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>84862001</td>\n",
       "      <td>M</td>\n",
       "      <td>16.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.20220</td>\n",
       "      <td>0.17220</td>\n",
       "      <td>0.10280</td>\n",
       "      <td>...</td>\n",
       "      <td>20.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.80</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>0.1789</td>\n",
       "      <td>0.4233</td>\n",
       "      <td>0.4784</td>\n",
       "      <td>0.20730</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>0.11420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>849014</td>\n",
       "      <td>M</td>\n",
       "      <td>19.81</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>0.09831</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.14790</td>\n",
       "      <td>0.09498</td>\n",
       "      <td>...</td>\n",
       "      <td>27.32</td>\n",
       "      <td>30.88</td>\n",
       "      <td>186.80</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>0.1512</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>0.23880</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>0.07615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8510426</td>\n",
       "      <td>B</td>\n",
       "      <td>13.54</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.06664</td>\n",
       "      <td>0.04781</td>\n",
       "      <td>...</td>\n",
       "      <td>15.11</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>0.1773</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0     842302         M        17.99           NaN          122.80     1001.0   \n",
       "1     842517         M        20.57         17.77          132.90     1326.0   \n",
       "2   84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3   84348301         M        11.42         20.38           77.58      386.1   \n",
       "4   84358402         M        20.29         14.34          135.10     1297.0   \n",
       "5     843786         M        12.45         15.70           82.57      477.1   \n",
       "6     844359         M        18.25         19.98          119.60     1040.0   \n",
       "7   84458202         M        13.71         20.83           90.20      577.9   \n",
       "8     844981         M        13.00         21.82           87.50      519.8   \n",
       "9   84501001         M        12.46         24.04           83.97      475.9   \n",
       "10    845636         M        16.02         23.24          102.70      797.8   \n",
       "11  84610002         M        15.78         17.89          103.60      781.0   \n",
       "12    846226         M        19.17         24.80          132.40     1123.0   \n",
       "13    846381         M        15.85           NaN          103.70      782.7   \n",
       "14  84667401         M        13.73         22.61           93.60      578.3   \n",
       "15  84799002         M        14.54         27.54           96.73      658.8   \n",
       "16    848406         M        14.68         20.13           94.74      684.5   \n",
       "17  84862001         M        16.13           NaN          108.10      798.8   \n",
       "18    849014         M        19.81         22.15          130.00     1260.0   \n",
       "19   8510426         B        13.54         14.36           87.46      566.3   \n",
       "\n",
       "    smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0           0.11840           0.27760         0.30010              0.14710   \n",
       "1           0.08474           0.07864         0.08690              0.07017   \n",
       "2           0.10960           0.15990         0.19740              0.12790   \n",
       "3               NaN           0.28390         0.24140              0.10520   \n",
       "4           0.10030           0.13280         0.19800              0.10430   \n",
       "5           0.12780           0.17000         0.15780              0.08089   \n",
       "6           0.09463           0.10900         0.11270              0.07400   \n",
       "7           0.11890           0.16450         0.09366              0.05985   \n",
       "8           0.12730           0.19320         0.18590              0.09353   \n",
       "9           0.11860           0.23960         0.22730              0.08543   \n",
       "10          0.08206           0.06669         0.03299              0.03323   \n",
       "11          0.09710           0.12920         0.09954              0.06606   \n",
       "12          0.09740           0.24580         0.20650              0.11180   \n",
       "13          0.08401           0.10020         0.09938              0.05364   \n",
       "14          0.11310           0.22930         0.21280              0.08025   \n",
       "15          0.11390           0.15950         0.16390              0.07364   \n",
       "16          0.09867           0.07200         0.07395              0.05259   \n",
       "17          0.11700           0.20220         0.17220              0.10280   \n",
       "18          0.09831           0.10270         0.14790              0.09498   \n",
       "19          0.09779           0.08129         0.06664              0.04781   \n",
       "\n",
       "            ...            radius_max  texture_max  perimeter_max  area_max  \\\n",
       "0           ...                 25.38          NaN         184.60    2019.0   \n",
       "1           ...                 24.99        23.41         158.80    1956.0   \n",
       "2           ...                 23.57        25.53         152.50    1709.0   \n",
       "3           ...                 14.91        26.50          98.87     567.7   \n",
       "4           ...                 22.54        16.67         152.20    1575.0   \n",
       "5           ...                 15.47        23.75         103.40     741.6   \n",
       "6           ...                 22.88        27.66         153.20    1606.0   \n",
       "7           ...                 17.06        28.14         110.60     897.0   \n",
       "8           ...                 15.49        30.73         106.20     739.3   \n",
       "9           ...                 15.09        40.68          97.65     711.4   \n",
       "10          ...                 19.19        33.88         123.80    1150.0   \n",
       "11          ...                 20.42        27.28         136.50    1299.0   \n",
       "12          ...                 20.96        29.94         151.70    1332.0   \n",
       "13          ...                 16.84          NaN         112.00     876.5   \n",
       "14          ...                 15.03        32.01         108.80     697.7   \n",
       "15          ...                 17.46        37.13         124.10     943.2   \n",
       "16          ...                 19.07        30.88         123.40    1138.0   \n",
       "17          ...                 20.96          NaN         136.80    1315.0   \n",
       "18          ...                 27.32        30.88         186.80    2398.0   \n",
       "19          ...                 15.11        19.26          99.70     711.2   \n",
       "\n",
       "    smoothness_max  compactness_max  concavity_max  concave_points_max  \\\n",
       "0           0.1622           0.6656         0.7119             0.26540   \n",
       "1           0.1238           0.1866         0.2416             0.18600   \n",
       "2           0.1444           0.4245         0.4504             0.24300   \n",
       "3              NaN           0.8663         0.6869             0.25750   \n",
       "4           0.1374           0.2050         0.4000             0.16250   \n",
       "5           0.1791           0.5249         0.5355             0.17410   \n",
       "6           0.1442           0.2576         0.3784             0.19320   \n",
       "7           0.1654           0.3682         0.2678             0.15560   \n",
       "8           0.1703           0.5401         0.5390             0.20600   \n",
       "9           0.1853           1.0580         1.1050             0.22100   \n",
       "10          0.1181           0.1551         0.1459             0.09975   \n",
       "11          0.1396           0.5609         0.3965             0.18100   \n",
       "12          0.1037           0.3903         0.3639             0.17670   \n",
       "13          0.1131           0.1924         0.2322             0.11190   \n",
       "14          0.1651           0.7725         0.6943             0.22080   \n",
       "15          0.1678           0.6577         0.7026             0.17120   \n",
       "16          0.1464           0.1871         0.2914             0.16090   \n",
       "17          0.1789           0.4233         0.4784             0.20730   \n",
       "18          0.1512           0.3150         0.5372             0.23880   \n",
       "19          0.1440           0.1773         0.2390             0.12880   \n",
       "\n",
       "    symmetry_max  fractal_dimension_max  \n",
       "0         0.4601                0.11890  \n",
       "1         0.2750                0.08902  \n",
       "2         0.3613                0.08758  \n",
       "3         0.6638                0.17300  \n",
       "4         0.2364                0.07678  \n",
       "5         0.3985                0.12440  \n",
       "6            NaN                0.08368  \n",
       "7         0.3196                0.11510  \n",
       "8         0.4378                0.10720  \n",
       "9         0.4366                0.20750  \n",
       "10        0.2948                0.08452  \n",
       "11        0.3792                0.10480  \n",
       "12        0.3176                0.10230  \n",
       "13        0.2809                0.06287  \n",
       "14        0.3596                0.14310  \n",
       "15           NaN                0.13410  \n",
       "16        0.3029                0.08216  \n",
       "17        0.3706                0.11420  \n",
       "18        0.2768                0.07615  \n",
       "19        0.2977                0.07259  \n",
       "\n",
       "[20 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# although, you can specify however many rows you'd like returned\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_max</th>\n",
       "      <th>texture_max</th>\n",
       "      <th>perimeter_max</th>\n",
       "      <th>area_max</th>\n",
       "      <th>smoothness_max</th>\n",
       "      <th>compactness_max</th>\n",
       "      <th>concavity_max</th>\n",
       "      <th>concave_points_max</th>\n",
       "      <th>symmetry_max</th>\n",
       "      <th>fractal_dimension_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.3514</td>\n",
       "      <td>0.152</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "567  927241         M        20.60         29.33          140.10     1265.0   \n",
       "568   92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "567          0.11780           0.27700          0.3514                0.152   \n",
       "568          0.05263           0.04362          0.0000                0.000   \n",
       "\n",
       "             ...            radius_max  texture_max  perimeter_max  area_max  \\\n",
       "567          ...                25.740        39.42         184.60    1821.0   \n",
       "568          ...                 9.456        30.37          59.16     268.6   \n",
       "\n",
       "     smoothness_max  compactness_max  concavity_max  concave_points_max  \\\n",
       "567         0.16500          0.86810         0.9387               0.265   \n",
       "568         0.08996          0.06444         0.0000               0.000   \n",
       "\n",
       "     symmetry_max  fractal_dimension_max  \n",
       "567        0.4087                0.12400  \n",
       "568        0.2871                0.07039  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same thing applies to `.tail()` which returns the last few rows\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing and Selecting Data in Pandas\n",
    "Let's separate this dataframe into three new dataframes - one for each metric (mean, standard error, and maximum). To get the data for each dataframe, we need to select the `id` and `diagnosis` columns, as well as the ten columns for that metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 id\n",
      "1 diagnosis\n",
      "2 radius_mean\n",
      "3 texture_mean\n",
      "4 perimeter_mean\n",
      "5 area_mean\n",
      "6 smoothness_mean\n",
      "7 compactness_mean\n",
      "8 concavity_mean\n",
      "9 concave_points_mean\n",
      "10 symmetry_mean\n",
      "11 fractal_dimension_mean\n",
      "12 radius_SE\n",
      "13 texture_SE\n",
      "14 perimeter_SE\n",
      "15 area_SE\n",
      "16 smoothness_SE\n",
      "17 compactness_SE\n",
      "18 concavity_SE\n",
      "19 concave_points_SE\n",
      "20 symmetry_SE\n",
      "21 fractal_dimension_SE\n",
      "22 radius_max\n",
      "23 texture_max\n",
      "24 perimeter_max\n",
      "25 area_max\n",
      "26 smoothness_max\n",
      "27 compactness_max\n",
      "28 concavity_max\n",
      "29 concave_points_max\n",
      "30 symmetry_max\n",
      "31 fractal_dimension_max\n"
     ]
    }
   ],
   "source": [
    "# View the index number and label for each column\n",
    "for i, v in enumerate(df.columns):\n",
    "    print(i, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select data using `loc` and `iloc`, which you can read more about [here](https://pandas.pydata.org/pandas-docs/stable/indexing.html). `loc` uses labels of rows or columns to select data, while `iloc` uses the index numbers. We'll use these to index the dataframe below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99           NaN          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3              NaN           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  fractal_dimension_mean  \n",
       "0         0.2419                 0.07871  \n",
       "1         0.1812                 0.05667  \n",
       "2         0.2069                 0.05999  \n",
       "3         0.2597                 0.09744  \n",
       "4         0.1809                 0.05883  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select all the columns from 'id' to the last mean column\n",
    "df_means = df.loc[:,'id':'fractal_dimension_mean']\n",
    "df_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean\n",
       "0    842302         M        17.99           NaN          122.80     1001.0\n",
       "1    842517         M        20.57         17.77          132.90     1326.0\n",
       "2  84300903         M        19.69         21.25          130.00     1203.0\n",
       "3  84348301         M        11.42         20.38           77.58      386.1\n",
       "4  84358402         M        20.29         14.34          135.10     1297.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# practice code from above cell\n",
    "# select all the columns from 'id' to the last mean column\n",
    "df_means = df.loc[:,'id':'area_mean']\n",
    "df_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99           NaN          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3              NaN           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  \n",
       "0         0.2419  \n",
       "1         0.1812  \n",
       "2         0.2069  \n",
       "3         0.2597  \n",
       "4         0.1809  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repeat the step above using index numbers, first pair for rows, second for cols\n",
    "df_means = df.iloc[:,:11]\n",
    "df_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</td>\n",
       "      <td>1326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1203.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean\n",
       "1         M        20.57         17.77           132.9     1326.0\n",
       "2         M        19.69         21.25           130.0     1203.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# self code to practice the step above using index numbers\n",
    "df_means = df.iloc[1:3,1:6]\n",
    "df_means.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's save the dataframe of means for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_means.to_csv('cancer_data_means.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99           NaN          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3              NaN           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  \n",
       "0         0.2419  \n",
       "1         0.1812  \n",
       "2         0.2069  \n",
       "3         0.2597  \n",
       "4         0.1809  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read new csv\n",
    "df_new = pd.read_csv('cancer_data_means.csv')\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Multiple Ranges in Pandas\n",
    "Selecting the columns for the mean dataframe was pretty straightforward - the columns we needed to select were all together (`id`, `diagnosis`, and the mean columns). Now we run into a little issue when we try to do the same for the standard errors or maximum values. `id` and `diagnosis` are separated from the rest of the columns we need! We can't specify all of these in one range.\n",
    "\n",
    "First, try creating the standard error dataframe on your own to see why doing this with just `loc` and `iloc` is an issue. Then, use this [stackoverflow link](https://stackoverflow.com/questions/41256648/select-multiple-ranges-of-columns-in-pandas-dataframe) to learn how to select multiple ranges in Pandas and try it below. By the way, to figure this out myself, I just found this link by googling \"how to select multiple ranges df.iloc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_SE</th>\n",
       "      <th>texture_SE</th>\n",
       "      <th>perimeter_SE</th>\n",
       "      <th>area_SE</th>\n",
       "      <th>smoothness_SE</th>\n",
       "      <th>compactness_SE</th>\n",
       "      <th>concavity_SE</th>\n",
       "      <th>concave_points_SE</th>\n",
       "      <th>symmetry_SE</th>\n",
       "      <th>fractal_dimension_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_SE  texture_SE  perimeter_SE  area_SE  smoothness_SE  \\\n",
       "0     1.0950         NaN         8.589   153.40       0.006399   \n",
       "1     0.5435      0.7339         3.398    74.08       0.005225   \n",
       "2     0.7456      0.7869         4.585    94.03       0.006150   \n",
       "3     0.4956      1.1560         3.445    27.23            NaN   \n",
       "4     0.7572      0.7813         5.438    94.44       0.011490   \n",
       "\n",
       "   compactness_SE  concavity_SE  concave_points_SE  symmetry_SE  \\\n",
       "0         0.04904       0.05373            0.01587      0.03003   \n",
       "1         0.01308       0.01860            0.01340      0.01389   \n",
       "2         0.04006       0.03832            0.02058      0.02250   \n",
       "3         0.07458       0.05661            0.01867      0.05963   \n",
       "4         0.02461       0.05688            0.01885      0.01756   \n",
       "\n",
       "   fractal_dimension_SE  \n",
       "0              0.006193  \n",
       "1              0.003532  \n",
       "2              0.004571  \n",
       "3              0.009208  \n",
       "4              0.005115  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# create the standard errors dataframe\n",
    "df_SE = df.loc[:, 'radius_SE':'fractal_dimension_SE']\n",
    "\n",
    "# view the first few rows to confirm this was successful\n",
    "df_SE.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_SE</th>\n",
       "      <th>texture_SE</th>\n",
       "      <th>perimeter_SE</th>\n",
       "      <th>area_SE</th>\n",
       "      <th>smoothness_SE</th>\n",
       "      <th>compactness_SE</th>\n",
       "      <th>concavity_SE</th>\n",
       "      <th>concave_points_SE</th>\n",
       "      <th>symmetry_SE</th>\n",
       "      <th>fractal_dimension_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_SE  texture_SE  perimeter_SE  area_SE  \\\n",
       "0    842302         M     1.0950         NaN         8.589   153.40   \n",
       "1    842517         M     0.5435      0.7339         3.398    74.08   \n",
       "2  84300903         M     0.7456      0.7869         4.585    94.03   \n",
       "3  84348301         M     0.4956      1.1560         3.445    27.23   \n",
       "4  84358402         M     0.7572      0.7813         5.438    94.44   \n",
       "\n",
       "   smoothness_SE  compactness_SE  concavity_SE  concave_points_SE  \\\n",
       "0       0.006399         0.04904       0.05373            0.01587   \n",
       "1       0.005225         0.01308       0.01860            0.01340   \n",
       "2       0.006150         0.04006       0.03832            0.02058   \n",
       "3            NaN         0.07458       0.05661            0.01867   \n",
       "4       0.011490         0.02461       0.05688            0.01885   \n",
       "\n",
       "   symmetry_SE  fractal_dimension_SE  \n",
       "0      0.03003              0.006193  \n",
       "1      0.01389              0.003532  \n",
       "2      0.02250              0.004571  \n",
       "3      0.05963              0.009208  \n",
       "4      0.01756              0.005115  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use np.r_ function to select SE values\n",
    "df_range_max = df.iloc[:, np.r_[0:2, 22:]]\n",
    "\n",
    "df_range.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_SE</th>\n",
       "      <th>texture_SE</th>\n",
       "      <th>perimeter_SE</th>\n",
       "      <th>area_SE</th>\n",
       "      <th>smoothness_SE</th>\n",
       "      <th>compactness_SE</th>\n",
       "      <th>concavity_SE</th>\n",
       "      <th>concave_points_SE</th>\n",
       "      <th>symmetry_SE</th>\n",
       "      <th>fractal_dimension_SE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis        id diagnosis  radius_mean  texture_mean  \\\n",
       "0    842302         M    842302         M        17.99           NaN   \n",
       "1    842517         M    842517         M        20.57         17.77   \n",
       "2  84300903         M  84300903         M        19.69         21.25   \n",
       "3  84348301         M  84348301         M        11.42         20.38   \n",
       "4  84358402         M  84358402         M        20.29         14.34   \n",
       "\n",
       "   perimeter_mean  area_mean  smoothness_mean  compactness_mean  \\\n",
       "0          122.80     1001.0          0.11840           0.27760   \n",
       "1          132.90     1326.0          0.08474           0.07864   \n",
       "2          130.00     1203.0          0.10960           0.15990   \n",
       "3           77.58      386.1              NaN           0.28390   \n",
       "4          135.10     1297.0          0.10030           0.13280   \n",
       "\n",
       "           ...           radius_SE  texture_SE  perimeter_SE  area_SE  \\\n",
       "0          ...              1.0950         NaN         8.589   153.40   \n",
       "1          ...              0.5435      0.7339         3.398    74.08   \n",
       "2          ...              0.7456      0.7869         4.585    94.03   \n",
       "3          ...              0.4956      1.1560         3.445    27.23   \n",
       "4          ...              0.7572      0.7813         5.438    94.44   \n",
       "\n",
       "   smoothness_SE  compactness_SE  concavity_SE  concave_points_SE  \\\n",
       "0       0.006399         0.04904       0.05373            0.01587   \n",
       "1       0.005225         0.01308       0.01860            0.01340   \n",
       "2       0.006150         0.04006       0.03832            0.02058   \n",
       "3            NaN         0.07458       0.05661            0.01867   \n",
       "4       0.011490         0.02461       0.05688            0.01885   \n",
       "\n",
       "   symmetry_SE  fractal_dimension_SE  \n",
       "0      0.03003              0.006193  \n",
       "1      0.01389              0.003532  \n",
       "2      0.02250              0.004571  \n",
       "3      0.05963              0.009208  \n",
       "4      0.01756              0.005115  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the maximum values dataframe\n",
    "df_range_max = df.iloc[:, np.r_[0:2, 22:]]\n",
    "\n",
    "# view the first few rows to confirm this was successful\n",
    "df_range_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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