Plotly_vs_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary libraries\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dataframe from csv \n",
    "breast_cancer_dataframe = pd.read_csv('data.csv')\n",
    "\n",
    "#get features information\n",
    "#breast_cancer_dataframe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</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>10.38</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>17.33</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",
       "      <td>NaN</td>\n",
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       "    <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>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",
       "      <td>NaN</td>\n",
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       "    <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>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",
       "      <td>NaN</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>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</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",
       "      <td>NaN</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          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          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...               17.33           184.60      2019.0            0.1622   \n",
       "1     ...               23.41           158.80      1956.0            0.1238   \n",
       "2     ...               25.53           152.50      1709.0            0.1444   \n",
       "3     ...               26.50            98.87       567.7            0.2098   \n",
       "4     ...               16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract a few sample data to have a look\n",
    "breast_cancer_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data cleaning step - remove the columns or rows with missing values and the ID as it doesn't have any relevance in anaysis\n",
    "breast_cancer_df = breast_cancer_dataframe.drop(['id','Unnamed: 32'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></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",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</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",
       "      <td>...</td>\n",
       "      <td>17.33</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>...</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>...</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</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",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>...</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",
       "      <td>1</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean     ...       texture_worst  perimeter_worst  \\\n",
       "0                 0.07871     ...               17.33           184.60   \n",
       "1                 0.05667     ...               23.41           158.80   \n",
       "2                 0.05999     ...               25.53           152.50   \n",
       "3                 0.09744     ...               26.50            98.87   \n",
       "4                 0.05883     ...               16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  diagnosis_M  \n",
       "0                0.2654          0.4601                  0.11890            1  \n",
       "1                0.1860          0.2750                  0.08902            1  \n",
       "2                0.2430          0.3613                  0.08758            1  \n",
       "3                0.2575          0.6638                  0.17300            1  \n",
       "4                0.1625          0.2364                  0.07678            1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the column called diagnosis and having a columns of 0 and 1 instead --> 1 for M(Malignant) and 0 for B(Benign)\n",
    "breast_cancer_df= pd.get_dummies(breast_cancer_df,'diagnosis',drop_first=True) \n",
    "\n",
    "#check if new column is added and contains 0 and 1\n",
    "breast_cancer_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file://C:\\\\Users\\\\Natasha Sharma\\\\Documents\\\\Python Scripts\\\\temp-plot.html'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First Plotly chart - Bar chart to see the count of Malignant and Benign in our data\n",
    "\n",
    "#create data to feed into the plot - x-axis will hold the name of diagnosis\n",
    "#and y axis will have the counts according the number of matches found in diagnosis column in our dataframe\n",
    "\n",
    "color = ['red','green']\n",
    "data = [go.Bar(x=['Malignant','Benign'],\n",
    "y=[breast_cancer_dataframe.loc[breast_cancer_dataframe['diagnosis']=='M'].shape[0],\n",
    "   breast_cancer_dataframe.loc[breast_cancer_dataframe['diagnosis']=='B'].shape[0]],\n",
    "   marker=dict(color=color) \n",
    "\n",
    ")]\n",
    "\n",
    "#create the layout of the chart by defining titles for chart, x-axis and y-axis\n",
    "layout = go.Layout(title='Breast Cancer - Diagnosis',\n",
    "xaxis=dict(title='Diagnosis'),\n",
    "yaxis=dict(title='Number of people')\n",
    ")\n",
    "\n",
    "#Imbed data and layout into charts figure using Figure function\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "#Use plot function of plotly to visualize the data\n",
    "py.offline.plot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#breast_cancer_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndata = [go.Bar(x=breast_cancer_dataframe['radius_mean'],\\ny= breast_cancer_dataframe['texture_mean'])]\\n\\nlayout = go.Layout(title='Radius Mean v/s Texture Mean',\\nxaxis=dict(title='Radius Mean '),\\nyaxis=dict(title='Texture Mean')\\n)\\n\\nfig = go.Figure(data=data, layout=layout)\\npy.offline.plot(fig)\\n\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "data = [go.Bar(x=breast_cancer_dataframe['radius_mean'],\n",
    "y= breast_cancer_dataframe['texture_mean'])]\n",
    "\n",
    "layout = go.Layout(title='Radius Mean v/s Texture Mean',\n",
    "xaxis=dict(title='Radius Mean '),\n",
    "yaxis=dict(title='Texture Mean')\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.offline.plot(fig)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file://C:\\\\Users\\\\Natasha Sharma\\\\Documents\\\\Python Scripts\\\\temp-plot.html'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Heatmap - to visualize the correlation between features/factors given in the dataset\n",
    "\n",
    "#calculate the pairwise correlation of columns - Pearson correlation coefficient. \n",
    "z = breast_cancer_df.corr()\n",
    "\n",
    "#use Heatmap function available in plotly and create trace(collection of data) for plot\n",
    "trace = go.Heatmap(\n",
    "            x=z.index,       #set x as the feature id/name\n",
    "            y=z.index,       #set y as the feature id/name\n",
    "            z=z.values,      #set z as the correlation matrix values, \n",
    "                             #these values will be used to show the coloring on heatmap,\n",
    "                             #which will eventually define which coefficient has more impact or are closly related\n",
    "            colorscale='Viridis', #colorscale to define different colors for different range of values in correlation matrix\n",
    "    )\n",
    "\n",
    "#set the title of the plot\n",
    "title = \"plotting the correlation matrix of the breast cancer dataset\"\n",
    "\n",
    "##create the layout of the chart by defining title for chart, height and width of it\n",
    "layout = go.Layout(\n",
    "    title=title,          # set plot title\n",
    "    autosize=False,       # turn off autosize \n",
    "    height=800,           # plot's height in pixels \n",
    "    width=800             # plot's height in pixels \n",
    ")\n",
    "\n",
    "#covert the trace into list object before passing thru Figure function\n",
    "data = go.Data([trace])\n",
    "\n",
    "#Imbed data and layout into plot using Figure function\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "#Use plot function of plotly to visualize the data\n",
    "py.offline.plot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Bar Chart and Heatmap using Seaborn library. Seaborn code is a bit less than what we need to code for Plotly but the plot can be more interactive using plotly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29caaaed3c8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29caac79898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.countplot(x='diagnosis',data = breast_cancer_dataframe,palette='BrBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29ca8cff400>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29ca8cffef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,10), dpi=100)\n",
    "sns.heatmap(breast_cancer_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}