{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Introduction\n\nThis notebook was developed for analyzing the main data delivered on the RSNA competition https://www.kaggle.com/competitions/rsna-breast-cancer-detection, focused on the main CSV table and the images.","metadata":{}},{"cell_type":"markdown","source":"# Data overview\n\nThe description of the data is shown on the following list, where is specified that some of them are exclusive for the training set and also they give an introduction to the data form:\n\n- site_id - ID code for the source hospital.\n- patient_id - ID code for the patient.\n- image_id - ID code for the image.\n- laterality - Whether the image is of the left or right breast.\n- view - The orientation of the image. The default for a screening exam is to capture two views per breast.\n- age - The patient's age in years.\n- implant - Whether or not the patient had breast implants. Site 1 only provides breast implant information at the patient level, not at the breast level.\n- density - A rating for how dense the breast tissue is, with A being the least dense and D being the most dense. Extremely dense tissue can make diagnosis more difficult. **Only provided for train**.\n- machine_id - An ID code for the imaging device.\n- cancer - Whether or not the breast was positive for malignant cancer. The target value. **Only provided for train**.\n- biopsy - Whether or not a follow-up biopsy was performed on the breast. **Only provided for train**.\n- invasive - If the breast is positive for cancer, whether or not the cancer proved to be invasive. **Only provided for train**.\n- BIRADS - 0 if the breast required follow-up, 1 if the breast was rated as negative for cancer, and 2 if the breast was rated as normal. Only provided for train.\n- prediction_id - The ID for the matching submission row. Multiple images will share the same prediction ID. **Test only**.\n- difficult_negative_case - True if the case was unusually difficult. **Only provided for train.**\n\nFrom this description, the main EDA was built as a reference for the future Cancer Detection.","metadata":{}},{"cell_type":"markdown","source":"First, the main libraries are loaded, focused on plotting and statistical analysis.","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing\nimport seaborn as sns #plotting\nimport matplotlib.pyplot as plt #plotting\nsns.set_theme()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-06T01:11:37.07408Z","iopub.execute_input":"2023-02-06T01:11:37.074525Z","iopub.status.idle":"2023-02-06T01:11:37.081122Z","shell.execute_reply.started":"2023-02-06T01:11:37.074491Z","shell.execute_reply":"2023-02-06T01:11:37.079922Z"},"trusted":true},"execution_count":94,"outputs":[]},{"cell_type":"markdown","source":"Later, the train table was saved on a pandas DataFrame. At first glace, it shows that each patient has more than two images and that there are some missing elements on the **BIRADS** and **density** columns.","metadata":{}},{"cell_type":"code","source":"sample_table=pd.read_csv(\"/kaggle/input/rsna-breast-cancer-detection/train.csv\")\nsample_table.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.117742Z","iopub.execute_input":"2023-02-06T01:11:37.118141Z","iopub.status.idle":"2023-02-06T01:11:37.19731Z","shell.execute_reply.started":"2023-02-06T01:11:37.118109Z","shell.execute_reply":"2023-02-06T01:11:37.196057Z"},"_kg_hide-input":true,"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"   site_id  patient_id    image_id laterality view   age  cancer  biopsy  \\\n0        2       10006   462822612          L   CC  61.0       0       0   \n1        2       10006  1459541791          L  MLO  61.0       0       0   \n2        2       10006  1864590858          R  MLO  61.0       0       0   \n3        2       10006  1874946579          R   CC  61.0       0       0   \n4        2       10011   220375232          L   CC  55.0       0       0   \n\n   invasive  BIRADS  implant density  machine_id  difficult_negative_case  \n0         0     NaN        0     NaN          29                    False  \n1         0     NaN        0     NaN          29                    False  \n2         0     NaN        0     NaN          29                    False  \n3         0     NaN        0     NaN          29                    False  \n4         0     0.0        0     NaN          21                     True  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>site_id</th>\n      <th>patient_id</th>\n      <th>image_id</th>\n      <th>laterality</th>\n      <th>view</th>\n      <th>age</th>\n      <th>cancer</th>\n      <th>biopsy</th>\n      <th>invasive</th>\n      <th>BIRADS</th>\n      <th>implant</th>\n      <th>density</th>\n      <th>machine_id</th>\n      <th>difficult_negative_case</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>10006</td>\n      <td>462822612</td>\n      <td>L</td>\n      <td>CC</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>29</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>10006</td>\n      <td>1459541791</td>\n      <td>L</td>\n      <td>MLO</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>29</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>10006</td>\n      <td>1864590858</td>\n      <td>R</td>\n      <td>MLO</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>29</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>10006</td>\n      <td>1874946579</td>\n      <td>R</td>\n      <td>CC</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>29</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>10011</td>\n      <td>220375232</td>\n      <td>L</td>\n      <td>CC</td>\n      <td>55.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>21</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Exploratory Data Analysis\n\nWith this in consideration, it was compared the total of images related to each column category to find if there is any pattern or if there is a possible imbalance of the data that could harm the performance of predicting models.\n\nFirst, it was checked the total sites registered on the table, finding that there are only two of them and the images are almost equally distributed. Then, the plot helps to identify that there is a difference of almost 5000 images between places.","metadata":{}},{"cell_type":"code","source":"count_site=sample_table.groupby(by=\"site_id\").count()[\"patient_id\"]\ncount_site.reset_index()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.199624Z","iopub.execute_input":"2023-02-06T01:11:37.200174Z","iopub.status.idle":"2023-02-06T01:11:37.226529Z","shell.execute_reply.started":"2023-02-06T01:11:37.200124Z","shell.execute_reply":"2023-02-06T01:11:37.225496Z"},"_kg_hide-input":true,"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"   site_id  patient_id\n0        1       29519\n1        2       25187","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>site_id</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>29519</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>25187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per site_id\")\nsns.barplot(data=count_site.reset_index(),x=\"site_id\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.228545Z","iopub.execute_input":"2023-02-06T01:11:37.228974Z","iopub.status.idle":"2023-02-06T01:11:37.466825Z","shell.execute_reply.started":"2023-02-06T01:11:37.228934Z","shell.execute_reply":"2023-02-06T01:11:37.46572Z"},"_kg_hide-input":true,"trusted":true},"execution_count":97,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The total images per laterality show an almost equal trend as it shows that the Screening Mammography is done on both breasts.","metadata":{}},{"cell_type":"code","source":"count_lateral=sample_table.groupby(by=\"laterality\").count()[\"patient_id\"]\ncount_lateral.reset_index()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.468986Z","iopub.execute_input":"2023-02-06T01:11:37.469333Z","iopub.status.idle":"2023-02-06T01:11:37.498717Z","shell.execute_reply.started":"2023-02-06T01:11:37.469304Z","shell.execute_reply":"2023-02-06T01:11:37.497866Z"},"_kg_hide-input":true,"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"  laterality  patient_id\n0          L       27267\n1          R       27439","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>laterality</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>L</td>\n      <td>27267</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>R</td>\n      <td>27439</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per laterality\")\nsns.barplot(data=count_lateral.reset_index(),x=\"laterality\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.500209Z","iopub.execute_input":"2023-02-06T01:11:37.500534Z","iopub.status.idle":"2023-02-06T01:11:37.729928Z","shell.execute_reply.started":"2023-02-06T01:11:37.500505Z","shell.execute_reply":"2023-02-06T01:11:37.7287Z"},"_kg_hide-input":true,"trusted":true},"execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA8gAAAH1CAYAAAAqKddYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAtbElEQVR4nO3daXRV9b3/8U8CBAdABgED+telVoqlajCKVlEbVJBSQatCUa/Vq9ehgtaRagVFeluGqxZr6zy0xaFVahQRsNYBO1hRKKJ1oS6nCooyaAWZkvwfeM2SargRSQ7g6/Uo2b9z9vmec1wrvtn77FNUU1NTEwAAAPiSKy70AAAAALAhEMgAAAAQgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMwJfQsGHDcuWVVxbksWtqavLDH/4we+21V4466qhPrd9333056aSTCjDZxqGQ792/++c//5kuXbpk9erVSZKTTz45v//97ws8FQBfhEAGoOAqKiqy7777ZtmyZbXbfve73+X4448v4FQN4+mnn86f/vSnPPbYY7n77rs/tX744Yfn5ptvLsBkm57jjz8+v/vd7xrt8W688cYcccQRSZKJEyfmu9/9bqM9NgDrh0AGYINQXV2dX/3qV4Ue43Orqqr6XLd/880307lz52yxxRYNNNGm4+Mjs4Xyed9bADZ+AhmADcJ//ud/5uabb87777//qbV/P5U1WfPo4MSJEzNo0KD893//d8rLy9OrV68888wzmThxYg488MDsu+++nzr1dfHixTnxxBNTVlaW4447Lm+++Wbt2ssvv5wTTzwxe++9d3r37p3JkyfXrg0bNiwjRozIKaeckj322CNPPvnkp+Z9++23c9ppp2XvvffOIYcckt/+9rdJPjoq/qMf/SizZs1KWVlZxo8f/6n7/vuRxy5dumTChAk59NBDU1ZWlquuuiqvv/56Bg0alO7du+ess87KypUrkyTvvfdeTj311Oyzzz7Za6+9cuqpp+att96q3dcbb7yRY489NmVlZfne976Xyy67LOedd17t+qxZszJo0KCUl5fn8MMPX+O5TZw4Mb169UpZWVkqKipy3333fWr2JLn66qszdOjQnH322SkrK8sRRxyRF154YY3XZsiQIdlnn31SUVGxxj+KfHzf8847L927d/8/T1de2/O98sorM2PGjIwcOTJlZWUZOXJkks//3j766KMZMGBAunfvngMPPDBXX311nfN8/N/kyy+/nBEjRtS+z+Xl5Zk9e3a+8Y1vrBHd06ZNy+GHH77W5whA4xLIAGwQunXrlr333js33XTTOt1/9uzZ6dKlS5588sn069cv55xzTp599tk89NBDGTt2bEaOHJmlS5fW3v7+++/PGWeckSeffDJf/epXa0Nx2bJlOemkk9KvX7/8+c9/zpVXXpnLLrssL730Uu19J02alNNOOy3PPPNM9txzz0/Ncs4552SbbbbJ9OnTM378+FxxxRX5y1/+kqOPPjqXXXZZ9thjj8ycOTNDhw6t13N74oknMnHixPz2t7/NjTfemEsuuSRjx47NY489lhdffDEPPPBAko+Owh955JF55JFH8sgjj6R58+a1YZgk5513Xnbbbbc8+eSTOfPMM1NZWVm79vbbb+fUU0/N6aefnr/97W+58MILM3To0CxatCjLli3LqFGjcsMNN2TmzJm5884707Vr1zrnffjhh9OnT5/87W9/S79+/XLGGWdk1apVqa6uzumnn54uXbrk8ccfz2233Zbbbrst06dP/9R9Z8yYkW9/+9trfV3W9nx/8IMfpLy8PMOHD8/MmTMzfPjwdXpvN99884wePTozZszIddddlzvuuCN/+MMf1jrXTjvttMb7PGPGjOy2225p3bp1nnjiidrbVVZWZsCAAWvdFwCNSyADsMEYOnRofvOb32TRokWf+77bbrttvvOd76RJkybp27dv5s+fn+9///spKSnJ/vvvn5KSkrz++uu1tz/ooIOy1157paSkJD/4wQ8ya9aszJ8/P48++mg6d+6c73znO2natGl23XXX9O7dO1OmTKm9b69evbLnnnumuLg4zZs3X2OO+fPn55lnnsl5552X5s2bp2vXrjn66KPXiNHP6+STT06LFi3yla98Jbvsskv222+/bLfddmnZsmUOOOCAPP/880mSNm3apHfv3tl8883TokWLnH766XnqqaeSJPPmzcuzzz6boUOHpqSkJOXl5amoqKh9jMrKyhxwwAE58MADU1xcnP322y/dunXLY489liQpLi7Oiy++mOXLl6dDhw75yle+Uue8X/va19KnT580a9YsJ554YlauXJm///3vefbZZ7No0aKceeaZKSkpyXbbbZdjjjlmjaO4e+yxRw4++OAUFxdns802W+vrsrbn+1nW5b3t0aNHunTpkuLi4nz1q1/Nt771rfztb39b61x1GTBgQO2R9yVLluSJJ55Iv3791mlfADSMpoUeAAA+tssuu+Sggw7K9ddfn5122ulz3bddu3a1P38cVltvvXXttubNm69xBHmbbbap/XnLLbfMVlttlQULFuTNN9/M7NmzU15eXrteVVW1xqmwpaWldc6xYMGCbLXVVmnRokXttk6dOmXOnDmf6/l80r8/j3///d13302SfPjhh/nJT36S6dOn57333kuSLF26NFVVVbVzbb755ms8j/nz5yf5KKCnTJmSRx55pHZ99erV6dGjR7bYYotceeWVufnmm3PxxRene/fuufDCC+t8jz752hYXF6djx45ZsGBB7evz76/tJ3//5H3/L2t7vk2aNPnU7dflvf373/+ecePG5cUXX8yqVauycuXK9OnTp94zflL//v1z2GGHZdmyZXnwwQdTXl6eDh06rNO+AGgYAhmADcrQoUNzxBFHrPFVRx9f0Gr58uW14fnOO+98ocf55Gdzly5dmvfeey8dOnRIaWlp9tprr9xyyy3rtN8OHTrkvffeywcffFA76/z589OxY8cvNG993HzzzXnllVfy29/+Nu3bt88//vGPDBgwIDU1NWnfvn3ee++9fPjhh7WR/HEcJx+FYf/+/TNq1KjP3HfPnj3Ts2fPLF++PFdddVUuueSS3H777Z9520++ttXV1Xn77bfToUOHNGnSJNtuu22mTZtW53MoKipaL8/3s6zLe3vuuefmuOOOy4033pjmzZvnxz/+cRYvXvx/3u+znkfHjh1TVlaWadOmpbKy0lWuATZATrEGYIOy/fbbp2/fvvn1r39du61t27bp2LFjKisrU1VVlbvvvjtvvPHGF3qcxx57LDNmzMjKlSvzs5/9LLvvvntKS0tz0EEH5dVXX829996bVatWZdWqVZk9e3Zefvnleu23tLQ0ZWVlueKKK7JixYq88MILufvuuxvlYkxLly5N8+bN06pVqyxZsiQ///nPa9c6d+6cbt265eqrr87KlSszc+bMNY4WH3744XnkkUcyffr0VFVVZcWKFXnyySfz1ltv5d13380f/vCHLFu2LCUlJdliiy1SXFz3/0I899xzmTZtWlavXp3bbrstJSUl2X333bPbbrtlyy23zPXXX5/ly5enqqoqc+fOzezZs9f7800+OvL+yf9O1uW9Xbp0abbaaqs0b948s2fPzqRJk+o1W7t27fL222/XXkDtY/37989NN92UuXPn5tBDD/0czxaAxiCQAdjgfP/731/jO5GT5PLLL89NN92UHj165KWXXkpZWdkXeox+/frlmmuuSY8ePfLcc89l7NixSZIWLVrkpptuyuTJk9OzZ8/sv//+GTdu3KdCZ22uuOKKvPnmm+nZs2fOPPPMDBkyJN/4xje+0Lz1ccIJJ2TFihXZZ599MnDgwPTs2XON9XHjxmXWrFnp0aNHrrrqqvTt2zclJSVJPgr7X/ziF7nuuuuy77775sADD8xNN92U6urqVFdX59Zbb03Pnj2z995756mnnsqll15a5xy9evXK5MmTs9dee6WysjJXX311mjVrliZNmuTaa6/NCy+8kF69emWfffbJj370o3zwwQcN8nz/4z/+I1OnTs1ee+2VUaNGrdN7O2LEiIwfPz5lZWW55pprcthhh9Vrtn322Sc777xz9t9///To0aN2+yGHHJI333wzhxxyyBqnuwOwYSiqqes8JABgk3b22Wdnxx13rPfVtOvj6quvzmuvvZZx48att31uag4++OCMHDmyUf7RBIDPxxFkAPiSmD17dl5//fVUV1fn8ccfz8MPP5yDDz640GN9qUydOjVFRUXZZ599Cj0KAJ/BRboA4Evi3XffzZAhQ7JkyZJss802ufTSS7PrrrsWeqwvjeOPPz4vvfRSxowZs9bPcANQOE6xBgAAgDjFGgAAAJIIZAAAAEjiM8h1Wrx4aaqrnX0OAACwKSkuLkqbNlt+5ppArkN1dY1ABgAA+BJxijUAAABEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAkqRpoQcAAPi82mxVkqYlzQs9BgBJVq9ckcXvrSz0GOuFQAYANjpNS5rn6TEnF3oMAJLsecGNSTaNQHaKNQAAAMQR5E1Cy1abZbPmzQo9BgBJlq9YlX+9v7zQYwAA60AgbwI2a94sgy+YUOgxAEhy+5hj868IZADYGDnFGgAAACKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEiSNG2MB1m8eHEuuOCCvP766ykpKcn222+fkSNHpm3btunSpUt22WWXFBd/1OpjxoxJly5dkiR//OMfM2bMmFRVVeVrX/tafvKTn2TzzTf/QmsAAADwWRrlCHJRUVFOPvnkTJ06Nffff3+22267jBs3rnb9zjvvTGVlZSorK2vjeOnSpbnkkkty7bXX5qGHHsqWW26Zm2666QutAQAAQF0aJZBbt26dHj161P6+xx57ZN68eWu9z+OPP55u3bplhx12SJIMGjQoDz744BdaAwAAgLo0yinWn1RdXZ077rgjFRUVtduOP/74VFVV5YADDsiQIUNSUlKS+fPnp1OnTrW36dSpU+bPn58k67wGAAAAdWn0i3Rdfvnl2WKLLXLcccclSR599NFMnDgxEyZMyEsvvZRrrrmmsUcCAACAxg3k0aNH57XXXstVV11Ve1Gu0tLSJEmLFi1y9NFH55lnnqnd/snTsOfNm1d723VdAwAAgLo0WiBfccUVmTNnTq655pqUlJQkSd57770sX748SbJ69epMnTo1Xbt2TZL07Nkzzz77bF599dUkH13I67DDDvtCawAAAFCXRvkM8osvvpjrrrsuO+ywQwYNGpQk2XbbbXPyySdn+PDhKSoqyurVq1NWVpazzjoryUdHlEeOHJlTTz011dXV6dq1ay6++OIvtAYAAAB1Kaqpqakp9BAbooULP0h19cbx0rRv3zKDL5hQ6DEASHL7mGPzzjv/KvQYm7z27Vvm6TEnF3oMAJLsecGNG9XfvuLiorRr1+Kz1xp5FgAAANggCWQAAACIQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgSSMF8uLFi3PKKaekd+/e+fa3v50zzzwzixYtSpLMmjUrhx9+eHr37p2TTjopCxcurL1fQ6wBAADAZ2mUQC4qKsrJJ5+cqVOn5v777892222XcePGpbq6Oueff36GDx+eqVOnpry8POPGjUuSBlkDAACAujRKILdu3To9evSo/X2PPfbIvHnzMmfOnDRv3jzl5eVJkkGDBmXKlClJ0iBrAAAAUJdG/wxydXV17rjjjlRUVGT+/Pnp1KlT7Vrbtm1TXV2dJUuWNMgaAAAA1KXRA/nyyy/PFltskeOOO66xHxoAAADq1LQxH2z06NF57bXXcu2116a4uDilpaWZN29e7fqiRYtSXFyc1q1bN8gaAAAA1KXRjiBfccUVmTNnTq655pqUlJQkSbp165bly5dnxowZSZI777wzffr0abA1AAAAqEujHEF+8cUXc91112WHHXbIoEGDkiTbbrttrrnmmowZMyYjRozIihUr0rlz54wdOzZJUlxcvN7XAAAAoC5FNTU1NYUeYkO0cOEHqa7eOF6a9u1bZvAFEwo9BgBJbh9zbN5551+FHmOT1759yzw95uRCjwFAkj0vuHGj+ttXXFyUdu1afPZaI88CAAAAGySBDAAAABHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJCknoG8aNGiLF26NElSVVWVe+65J7///e9TXV3doMMBAABAY6lXIJ966ql57bXXkiRXXnllbr755tx666356U9/2qDDAQAAQGOpVyC/+uqr6dq1a5Lkvvvuyw033JDbbrstkydPbtDhAAAAoLE0rc+NiouLs2rVqrzyyitp2bJlOnXqlOrq6trTrgEAAGBjV69APuCAA3LWWWdlyZIl6du3b5LkpZdeSseOHRt0OAAAAGgs9QrkH//4x/n973+fpk2bpn///kmSxYsXZ8iQIQ06HAAAADSWegVySUlJBg4cmOrq6rz77rvp0KFDevTo0dCzAQAAQKOp10W63n///Zx77rnZbbfdcuihhyZJHn744Vx55ZUNOhwAAAA0lnoF8ogRI9KiRYv88Y9/TLNmzZIkZWVlefDBBxt0OAAAAGgs9TrF+i9/+UumT5+eZs2apaioKEnStm3bLFy4sEGHAwAAgMZSryPILVu2zOLFi9fYNm/evLRv375BhgIAAIDGVq9APvroozN06ND89a9/TXV1dWbOnJkLL7wwgwYNauj5AAAAoFHU6xTrU045Jc2bN8/IkSOzevXqXHTRRRk4cGBOOOGEhp4PAAAAGkW9ArmoqCgnnHCCIAYAAGCTVe+LdH2WkpKSbLPNNuncufN6HQoAAAAaW70C+eKLL86CBQuSJK1bt86SJUuSJO3atcu7776bLl265IorrsgOO+zQUHMCAABAg6rXRbqOOuqoHH/88ZkxY0aeeOKJzJgxIyeccEIGDRqUp556Kt26dctll13W0LMCAABAg6lXIP/qV7/Kueeem8022yxJstlmm+Xss8/Obbfdli222CLDhg3LnDlzGnRQAAAAaEj1CuQtttgizz777BrbnnvuuWy++eYf7aS4XrsBAACADVa9PoM8dOjQnHTSSamoqEhpaWneeuutPPLII7nkkkuSfHQRr969ezfooAAAANCQ6hXIAwYMSLdu3TJ16tQsWLAgO+ywQ+66667svPPOSZJvfvOb+eY3v9mggwIAAEBDqlcgJ8nOO+9cG8QAAACwqal3ID/88MN56qmnsnjx4tTU1NRuHzNmTIMMBgAAAI2pXlfX+vnPf54RI0akuro6U6ZMSevWrfPEE0+kVatWDT0fAAAANIp6BfI999yTm2++ORdddFGaNWuWiy66KNdee23++c9/1vuBRo8enYqKinTp0iVz586t3V5RUZE+ffqkf//+6d+/f6ZPn167NmvWrBx++OHp3bt3TjrppCxcuPALrwEAAMBnqVcgv//++9lll12SJM2aNcuqVauy22675amnnqr3A/Xq1SsTJkxI586dP7U2fvz4VFZWprKyMj179kySVFdX5/zzz8/w4cMzderUlJeXZ9y4cV9oDQAAAOpSr0D+f//v/+XFF19MknzlK1/JHXfckXvvvTdbbbVVvR+ovLw8paWl9b79nDlz0rx585SXlydJBg0alClTpnyhNQAAAKhLvS7SdfbZZ2fJkiVJknPPPTfnnXdeli1blhEjRqyXIc4777zU1NRkzz33zDnnnJNWrVpl/vz56dSpU+1t2rZtm+rq6ixZsmSd11q3br1e5gUAAGDTU69APvDAA2t/3n333fPQQw+ttwEmTJiQ0tLSrFy5Mj/+8Y8zcuRIp0QDAADQ6Or9NU8ffvhhXnvttSxbtmyN7d27d/9CA3x82nVJSUkGDx6c008/vXb7vHnzam+3aNGiFBcXp3Xr1uu8BgAAAHWpVyDfe++9GTlyZJo1a5bNNtusdntRUVEeffTRdX7wZcuWpaqqKi1btkxNTU0mT56crl27Jkm6deuW5cuXZ8aMGSkvL8+dd96ZPn36fKE1AAAAqEu9Anns2LG5+uqrs99++63zA40aNSrTpk3Lu+++mxNPPDGtW7fOtddemyFDhqSqqirV1dXZaaedaj/XXFxcnDFjxmTEiBFZsWJFOnfunLFjx36hNQAAAKhLUU1NTc3/daODDjooDz30UJo1a9YYM20QFi78INXV/+dLs0Fo375lBl8wodBjAJDk9jHH5p13/lXoMTZ57du3zNNjTi70GAAk2fOCGzeqv33FxUVp167FZ6/VZwdnnXVWfvrTn2bRokXrdTAAAADYUNTrFOsddtgh48ePz+233167raamJkVFRfnHP/7RYMMBAABAY6lXIF9wwQXp379/+vbtu8ZFugAAAGBTUa9AXrJkSc4666wUFRU19DwAAABQEPX6DPKRRx6ZysrKhp4FAAAACqZeR5Bnz56dCRMm5Je//GW23nrrNdYmTHD1ZAAAADZ+9QrkY445Jsccc0xDzwIAAAAFU69APuKIIxp6DgAAACioOgP53nvvzYABA5Ikd999d507OOqoo9b7UAAAANDY6gzkBx54oDaQ67pAV1FRkUAGAABgk1BnIN9www21P//6179ulGEAAACgUOr1NU8AAACwqRPIAAAAEIEMAAAASQQyAAAAJFnLRbreeOONeu1gu+22W2/DAAAAQKHUGciHHHJIioqKUlNTU+edi4qK8o9//KNBBgMAAIDGVGcgv/DCC405BwAAABSUzyADAABA1nIE+ZNWr16d22+/PU899VQWL168xmnXEyZMaLDhAAAAoLHU6wjyT37yk9x1110pLy/Pc889l0MPPTQLFy7MPvvs09DzAQAAQKOoVyBPmzYtN9xwQ0444YQ0adIkJ5xwQq655po8+eSTDT0fAAAANIp6BfLy5ctTWlqaJNlss83y4YcfZqeddsrzzz/foMMBAABAY6nXZ5B32mmnPPvss9ltt93SrVu3XH311WnRokU6duzY0PMBAABAo6jXEeSLLrooTZo0SZIMGzYszz//fB555JFcfvnlDTocAAAANJZ6HUEuLS1N+/btkyQ77LBDbr311iTJO++802CDAQAAQGOq1xHk3r17f+b2b33rW+t1GAAAACiUegXyJ7/3+GMffPBBioqK1vtAAAAAUAhrPcX6wAMPTFFRUVasWJGDDjpojbUlS5Y4ggwAAMAmY62BPHbs2NTU1OS//uu/MmbMmNrtRUVFadeuXXbccccGHxAAAAAaw1oDee+9906S/PWvf83mm2/eKAMBAABAIdTrM8hNmzbN+PHj06tXr3z9619Pr169Mn78+KxcubKh5wMAAIBGUa+veRo7dmxmz56dyy67LJ06dcq8efPyi1/8Ih988EEuuuiihp4RAAAAGly9AnnKlCmprKxMmzZtkiQ77rhjdt111/Tv318gAwAAsElY5695Wtt2AAAA2NisNZAnTZqUJOnTp09OP/30TJ8+PS+//HIef/zxfP/7389hhx3WKEMCAABAQ1vrKdbDhw9Pv379cv755+eXv/xlRo4cmQULFqRDhw751re+lTPOOKOx5gQAAIAGtdZA/vgU6pKSkpx11lk566yzGmUoAAAAaGxrDeTq6ur89a9/Xetnjffdd9/1PhQAAAA0trUG8sqVK3PxxRfXGchFRUV5+OGHG2QwAAAAaExrDeTNN99cAAMAAPClUK+veQIAAIBN3VoD2fccAwAA8GWx1kCeOXNmY80BAAAABeUUawAAAIhABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJI0UyKNHj05FRUW6dOmSuXPn1m5/5ZVXMnDgwPTu3TsDBw7Mq6++2qBrAAAAUJdGCeRevXplwoQJ6dy58xrbR4wYkcGDB2fq1KkZPHhwhg8f3qBrAAAAUJdGCeTy8vKUlpausW3hwoV5/vnn069fvyRJv3798vzzz2fRokUNsgYAAABr07RQDzx//vx07NgxTZo0SZI0adIkHTp0yPz581NTU7Pe19q2bVuYJwoAAMBGwUW6AAAAIAU8glxaWpq33347VVVVadKkSaqqqrJgwYKUlpampqZmva8BAADA2hTsCHK7du3StWvXTJo0KUkyadKkdO3aNW3btm2QNQAAAFibopqampqGfpBRo0Zl2rRpeffdd9OmTZu0bt06DzzwQF5++eUMGzYs77//flq1apXRo0dnxx13TJIGWfs8Fi78INXVDf7SrBft27fM4AsmFHoMAJLcPubYvPPOvwo9xiavffuWeXrMyYUeA4Ake15w40b1t6+4uCjt2rX4zLVGCeSNkUAGYF0I5MYhkAE2HJtSILtIFwAAAEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJkqaFHiBJKioqUlJSkubNmydJzjvvvPTs2TOzZs3K8OHDs2LFinTu3Dljx45Nu3btkmSd1wAAAOCzbDBHkMePH5/KyspUVlamZ8+eqa6uzvnnn5/hw4dn6tSpKS8vz7hx45JkndcAAACgLhtMIP+7OXPmpHnz5ikvL0+SDBo0KFOmTPlCawAAAFCXDeIU6+Sj06pramqy55575pxzzsn8+fPTqVOn2vW2bdumuro6S5YsWee11q1bN+ZTAgAAYCOyQRxBnjBhQu67777cc889qampyciRIws9EgAAAF8yG0Qgl5aWJklKSkoyePDgPPPMMyktLc28efNqb7No0aIUFxendevW67wGAAAAdSl4IC9btiz/+te/kiQ1NTWZPHlyunbtmm7dumX58uWZMWNGkuTOO+9Mnz59kmSd1wAAAKAuBf8M8sKFCzNkyJBUVVWluro6O+20U0aMGJHi4uKMGTMmI0aMWOPrmpKs8xoAAADUpaimpqam0ENsiBYu/CDV1RvHS9O+fcsMvmBCoccAIMntY47NO+/8q9BjbPLat2+Zp8ecXOgxAEiy5wU3blR/+4qLi9KuXYvPXmvkWQAAAGCDJJABAAAgAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJJtwIL/yyisZOHBgevfunYEDB+bVV18t9EgAAABswDbZQB4xYkQGDx6cqVOnZvDgwRk+fHihRwIAAGAD1rTQAzSEhQsX5vnnn88tt9ySJOnXr18uv/zyLFq0KG3btq3XPoqLixpyxPVu6zZbFnoEAP7XxvY3ZGNV0qpdoUcA4H9tTH/71jbrJhnI8+fPT8eOHdOkSZMkSZMmTdKhQ4fMnz+/3oHcZiMLzvE/HFDoEQD4X+3atSj0CF8KXz9tdKFHAOB/bSp/+zbZU6wBAADg89gkA7m0tDRvv/12qqqqkiRVVVVZsGBBSktLCzwZAAAAG6pNMpDbtWuXrl27ZtKkSUmSSZMmpWvXrvU+vRoAAIAvn6KampqaQg/REF5++eUMGzYs77//flq1apXRo0dnxx13LPRYAAAAbKA22UAGAACAz2OTPMUaAAAAPi+BDAAAABHIAAAAkEQgAwAAQBKBDAAAAEmSpoUeAKCioiLXXnttdtlll0KPAgANrqKiIiUlJSkpKcmqVaty0kkn5eijjy70WEAEMgAANLrx48dnl112ydy5c3PkkUfmgAMOSMeOHQs9FnzpOcUaAAAKZJdddkmrVq3y9ttvF3oUIAIZAAAK5umnn06bNm3y1a9+tdCjAHGKNQAANLqhQ4empqYmr7/+en72s5+lpKSk0CMBEcgAANDoPv4M8oMPPpgf/vCH6d69e7beeutCjwVfek6xBgCAAjnssMOy33775brrriv0KEAcQQY2ECeeeGKaNGlS+/v999+frbbaqoATAUDjOPfcc3PkkUfmlFNOSYcOHQo9DnypFdXU1NQUeggAAAAoNKdYAwAAQAQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgBssCoqKvLnP/+50GNk4sSJ+e53v1v7e1lZWd54440CTgQADUMgA8BGrkuXLnnttdca7fFmzpyZ7bbbLkkybNiwXHnllY322ADQkAQyAHyJrV69utAjAMAGQyADwAZu9uzZGThwYMrLy7P//vtn5MiRWblyZZLk2GOPTZL0798/ZWVlmTx5cpLkkUceSf/+/VNeXp5BgwblhRdeqN1fRUVFrr/++nz729/OHnvskdWrV+f666/PwQcfnLKysvTt2zcPPfRQnfN8fMT6rrvuyv3335+bbropZWVlOe2003LjjTdmyJAha9x+1KhRGTVq1Pp+WQBgvWta6AEAgLUrLi7OD3/4w3Tr1i1vvfVWTjnllNx+++353ve+lwkTJqRLly6prKzM9ttvnyR5/vnnc9FFF+Xaa69Nt27dct999+WMM87IlClTUlJSkiR54IEHcv3116dNmzZp2rRptttuu0yYMCHt27fPlClTcv7552fatGnp0KFDnXMNHDgwM2fOTMeOHfODH/wgSbJgwYL8/Oc/z/vvv59WrVpl9erVeeCBB3LDDTc0/AsFAF+QI8gAsIHr1q1b9thjjzRt2jTbbrttBg4cmKeeeqrO2991110ZOHBgdt999zRp0iRHHHFEmjVrllmzZtXe5vjjj09paWk222yzJMlhhx2Wjh07pri4OH379s3222+f2bNnf+5ZO3TokPLy8kyZMiVJMn369LRp0ybdunX73PsCgMbmCDIAbOBeeeWV/PSnP82cOXPy4YcfpqqqKl/72tfqvP28efNy77335je/+U3ttlWrVmXBggW1v5eWlq5xn3vvvTe33HJL3nzzzSTJsmXLsnjx4nWa94gjjsgdd9yRY445Jvfdd1/69++/TvsBgMYmkAFgA3fppZdm1113zf/8z/+kRYsWufXWWzN16tQ6b19aWprTTjstp59+ep23KSoqqv35zTffzI9+9KPceuutKSsrS5MmTeodtZ/cz8cOPvjgXHrppZk7d24effTRnH/++fXaFwAUmlOsAWADt3Tp0my55ZbZcsst8/LLL+eOO+5YY33rrbde43uJjz766Nx55535+9//npqamixbtiyPPvpoPvjgg8/c/4cffpiioqK0bds2SXLPPffkxRdfrNds7dq1yz//+c81tjVv3jy9e/fOueeem69//evp1KnT53m6AFAwAhkANnAXXnhhJk2alO7du+eSSy5J375911g/88wzM2zYsJSXl2fy5Mn5+te/nssvvzwjR47MXnvtlUMPPTQTJ06sc/8777xzTjrppAwaNCjf+MY3Mnfu3HTv3r1esx111FF56aWXUl5enjPOOKN2+4ABAzJ37lynVwOwUSmqqampKfQQAMCmZd68eTnssMPypz/9KS1atCj0OABQL44gAwDrVXV1dW655Zb07dtXHAOwUXGRLgBgvVm2bFn222+/dOrUKTfeeGOhxwGAz8Up1gAAABCnWAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAkuT/A9C/FNvXtkoPAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":"However, the first important differences are related to the Mammography view, as in the following figure are six different views, where the main part of images are related to the **CC** and **MLO** categories.","metadata":{}},{"cell_type":"code","source":"count_view=sample_table.groupby(by=\"view\").count()[\"patient_id\"]\ncount_view.reset_index()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.731241Z","iopub.execute_input":"2023-02-06T01:11:37.732162Z","iopub.status.idle":"2023-02-06T01:11:37.760242Z","shell.execute_reply.started":"2023-02-06T01:11:37.73213Z","shell.execute_reply":"2023-02-06T01:11:37.759117Z"},"_kg_hide-input":true,"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"  view  patient_id\n0   AT          19\n1   CC       26765\n2   LM          10\n3  LMO           1\n4   ML           8\n5  MLO       27903","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>view</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AT</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CC</td>\n      <td>26765</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LM</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>LMO</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ML</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>MLO</td>\n      <td>27903</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per view\")\nsns.barplot(data=count_view.reset_index(),x=\"view\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:37.761677Z","iopub.execute_input":"2023-02-06T01:11:37.76208Z","iopub.status.idle":"2023-02-06T01:11:38.038845Z","shell.execute_reply.started":"2023-02-06T01:11:37.762049Z","shell.execute_reply":"2023-02-06T01:11:38.037805Z"},"_kg_hide-input":true,"trusted":true},"execution_count":101,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA8gAAAH1CAYAAAAqKddYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAvsElEQVR4nO3deZSWdf3/8dcMMKOIiBDggKSJSigi6DBohhqkgOCuwdf0mFvuaC65JSRSyvJVwyzUNM0oc0FRVFDRXPq5QEq45nJETVCUTQFZnJnfH57myFegcZm5GXo8zvEc7utz3ff9vob7eObJdd33XVRdXV0dAAAA+C9XXOgBAAAAYF0gkAEAACACGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAau3cc8/N5ZdfXpDnrq6uznnnnZcePXrkkEMO+dz6XXfdlaOPProAkzV848aNywUXXFDoMQBYBzQu9AAA8GX17t07H3/8caZOnZqmTZsmSW699dbcdddduemmmwo83dfr73//e/72t7/lkUceqTnWz9pvv/2y3377FWCyhu+EE04o9AgArCOcQQagQauqqsof/vCHQo/xhVVWVn6h/d955520b99+tXHMqj755JNCjwBAAyWQAWjQjjnmmFx//fX58MMPP7f2r3/9K506dVolmI444ojceuutSZIJEyZk8ODB+eUvf5ny8vL06dMnzzzzTCZMmJA99tgju+66a+64445VHnPBggU56qij0r179xx++OF55513atZef/31HHXUUamoqEjfvn1z77331qyde+65GTZsWI477rh069YtTz311Ofmfe+993LCCSekoqIie+21V2655ZYkn54V/9nPfpYZM2ake/fuGTt27OfuO2HChPzP//xPze1OnTpl/Pjx2XvvvdO9e/dcccUVeeuttzJ48ODstNNOOe2007JixYokyaJFi3L88cdnl112SY8ePXL88cfn3XffrXmst99+Oz/84Q/TvXv3/OhHP8pFF12Us846q2Z9xowZGTx4cMrLy7PffvutcmwTJkxInz590r179/Tu3Tt33XXX52ZPkiuvvDJDhgzJ6aefnu7du+fAAw/Myy+/vMrP5tRTT80uu+yS3r17r/KPIv++71lnnZWddtrpc39n//jHP7Lbbrut8o8SDzzwQPbdd9+a+9fmeJ588sma+yTJUUcdlYMPPrjm9mGHHZYHH3xwtccHQMMgkAFo0Lp06ZKKiopcd911X+r+M2fOTKdOnfLUU09l4MCBOeOMM/Lcc8/lgQceyOjRozN8+PAsWbKkZv+77747J510Up566ql8+9vfrgmrpUuX5uijj87AgQPz//7f/8vll1+eiy66KK+99lrNfSdNmpQTTjghzzzzTHbeeefPzXLGGWdks802y2OPPZaxY8fmsssuyxNPPJFDDz00F110Ubp165Znn302Q4YMqdWxPf7445kwYUJuueWW/O53v8uFF16Y0aNH55FHHsmrr76ae+65J8mnZ+EPOuigPPzww3n44YdTWlqa4cOH1zzOWWedla5du+app57KKaeckokTJ9asvffeezn++ONz4okn5umnn84555yTIUOGZP78+Vm6dGlGjBiRa6+9Ns8++2xuvvnmdO7ceY3zTp06Nf369cvTTz+dgQMH5qSTTsrKlStTVVWVE088MZ06dcqjjz6aG2+8MTfeeGMee+yxz913+vTpq0Rskuy4447ZcMMN8+STT67y9/h/9/tPx9OtW7fMmjUr8+fPz8qVK/PPf/4zc+fOzeLFi7Ns2bI8//zzq/17BaDhEMgANHhDhgzJH//4x8yfP/8L33fzzTfPwQcfnEaNGmWfffbJnDlzcvLJJ6ekpCTf/e53U1JSkrfeeqtm/z333DM9evRISUlJfvKTn2TGjBmZM2dO/vrXv6Z9+/Y5+OCD07hx42y33Xbp27dvJk+eXHPfPn36ZOedd05xcXFKS0tXmWPOnDl55plnctZZZ6W0tDSdO3fOoYceukqMflHHHntsmjVrlm222Sbbbrttdtttt3To0CEbb7xxdt9997z44otJkk033TR9+/bNhhtumGbNmuXEE0/MtGnTkiSzZ8/Oc889lyFDhqSkpCTl5eXp3bt3zXNMnDgxu+++e/bYY48UFxdnt912S5cuXfLII48kSYqLi/Pqq69m2bJladOmTbbZZps1zrv99tunX79+adKkSY466qisWLEi//jHP/Lcc89l/vz5OeWUU1JSUpIOHTrkBz/4wSpn6Lt165bvf//7KS4uzgYbbPC5xx4wYEAmTZqUJFm8eHEeffTRDBgw4HP7re14Nthgg+ywww6ZPn16XnjhhXz729/OTjvtlGeeeSYzZszIFltskU033fRL/E0BsK7wIV0ANHjbbrtt9txzz1xzzTXp2LHjF7pvq1atav7877D6xje+UbOttLR0lTPIm222Wc2fN9poo2yyySaZO3du3nnnncycOTPl5eU165WVlat8cFZZWdka55g7d2422WSTNGvWrGZbu3bt8vzzz3+h4/ms/3sc//f2Bx98kCT5+OOPc8kll+Sxxx7LokWLkiRLlixJZWVlzVwbbrjhKscxZ86cJJ8G9OTJk/Pwww/XrH/yySfp2bNnmjZtmssvvzzXX399Lrjgguy0004555xz1vh39NmfbXFxcdq2bZu5c+fW/Hz+78/2s7c/e9/V2XfffTN48OBcdNFFeeCBB7Lddtulffv2n9tvbceTJD169MjTTz+dtm3bpkePHmnevHmmTZuWkpKSVFRUrHUGANZ9AhmA9cKQIUNy4IEHrvJVR//+QKtly5bVhOf777//lZ7ns+/NXbJkSRYtWpQ2bdqkrKwsPXr0yO9///sv9bht2rTJokWLsnjx4ppZ58yZk7Zt236leWvj+uuvzxtvvJFbbrklrVu3zksvvZQDDjgg1dXVad26dRYtWpSPP/64JpL/HcfJp7G8//77Z8SIEat97F69eqVXr15ZtmxZrrjiilx44YX505/+tNp9P/uzraqqynvvvZc2bdqkUaNG2XzzzXP//fev8RiKiorWeoxbb7112rVrl0cffTSTJk3KwIEDV7vffzqeioqKXHrppWnXrl2OO+64bLLJJrnwwgvTpEmT/PCHP1zrDACs+1xiDcB6YYsttsg+++yzytc7tWzZMm3bts3EiRNTWVmZ2267LW+//fZXep5HHnkk06dPz4oVK/KrX/0qO+64Y8rKyrLnnntm1qxZufPOO7Ny5cqsXLkyM2fOzOuvv16rxy0rK0v37t1z2WWXZfny5Xn55Zdz22231ctXNy1ZsiSlpaVp3rx5Fi5cmF//+tc1a+3bt0+XLl1y5ZVXZsWKFXn22WdXObu633775eGHH85jjz2WysrKLF++PE899VTefffdfPDBB3nwwQezdOnSlJSUpGnTpikuXvOvHi+88ELuv//+fPLJJ7nxxhtTUlKSHXfcMV27ds1GG22Ua665JsuWLUtlZWVeeeWVzJw58wsd58CBA3PjjTdm2rRp6dev32r3WdvxJEn37t3zxhtvZObMmenatWu22WabmqsHevTo8YXmAWDdI5ABWG+cfPLJWbp06SrbLr744lx33XXp2bNnXnvttXTv3v0rPcfAgQNz1VVXpWfPnnnhhRcyevToJEmzZs1y3XXX5d57702vXr3y3e9+N2PGjKn5pOjauOyyy/LOO++kV69eOeWUU3LqqafmO9/5zleatzaOPPLILF++PLvssksGDRqUXr16rbI+ZsyYzJgxIz179swVV1yRffbZJyUlJUk+Dfvf/OY3ufrqq7Prrrtmjz32yHXXXZeqqqpUVVXlhhtuSK9evVJRUZFp06bl5z//+Rrn6NOnT+6999706NEjEydOzJVXXpkmTZqkUaNGGTduXF5++eX06dMnu+yyS372s59l8eLFX+g4Bw4cmGnTpmWXXXZJy5YtV7vP2o4n+fSqhO233z5bb711zc+ge/fuadeu3SqX6wPQMBVVV1dXF3oIAKDhOP3007PVVlvV+tO0a+PKK6/Mm2++mTFjxnxtjwkAX5QzyADAWs2cOTNvvfVWqqqq8uijj2bq1Kn5/ve/X+ixAOBr50O6AIC1+uCDD3Lqqadm4cKF2WyzzfLzn/882223XaHHAoCvnUusAQAAIC6xBgAAgCQCGQAAAJJ4D/IaLViwJFVVrj4HAABYnxQXF2XTTTda7ZpAXoOqqmqBDAAA8F/EJdYAAAAQgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASZLGhR4AAACgrmzSvCQlpaWFHoM6sGL58iz6cMXX+pgCGQAAWG+VlJbmsvOOL/QY1IEzLrk6ydcbyC6xBgAAgAhkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIEnSuNADAGu26SYlaVxSWugxqAOfrFieBYtWFHoMAAA+QyDDOqxxSWn+PurYQo9BHdj5p79LIpABANYlLrEGAACACGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIkjevjSRYsWJCf/vSneeutt1JSUpItttgiw4cPT8uWLdOpU6dsu+22KS7+tNVHjRqVTp06JUkeeuihjBo1KpWVldl+++1zySWXZMMNN/xKawAAALA69XIGuaioKMcee2ymTJmSu+++Ox06dMiYMWNq1m+++eZMnDgxEydOrInjJUuW5MILL8y4cePywAMPZKONNsp11133ldYAAABgTeolkFu0aJGePXvW3O7WrVtmz5691vs8+uij6dKlS7bccsskyeDBg3Pfffd9pTUAAABYk3q5xPqzqqqq8uc//zm9e/eu2XbEEUeksrIyu+++e0499dSUlJRkzpw5adeuXc0+7dq1y5w5c5LkS68BAADAmtT7h3RdfPHFadq0aQ4//PAkyV//+tdMmDAh48ePz2uvvZarrrqqvkcCAACA+g3kkSNH5s0338wVV1xR86FcZWVlSZJmzZrl0EMPzTPPPFOz/bOXYc+ePbtm3y+7BgAAAGtSb4F82WWX5fnnn89VV12VkpKSJMmiRYuybNmyJMknn3ySKVOmpHPnzkmSXr165bnnnsusWbOSfPpBXv379/9KawAAALAm9fIe5FdffTVXX311ttxyywwePDhJsvnmm+fYY4/N0KFDU1RUlE8++STdu3fPaaedluTTM8rDhw/P8ccfn6qqqnTu3DkXXHDBV1oDAACANSmqrq6uLvQQ66J58xanqsqPhsJq3Xrj/H3UsYUegzqw809/l/ff/6jQYwDAeq91641z2XnHF3oM6sAZl1z9pX6fKi4uSqtWzVa/9lWHAgAAgPWBQAYAAIAIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAk9RTICxYsyHHHHZe+fftm3333zSmnnJL58+cnSWbMmJH99tsvffv2zdFHH5158+bV3K8u1gAAAGB16iWQi4qKcuyxx2bKlCm5++6706FDh4wZMyZVVVU5++yzM3To0EyZMiXl5eUZM2ZMktTJGgAAAKxJvQRyixYt0rNnz5rb3bp1y+zZs/P888+ntLQ05eXlSZLBgwdn8uTJSVInawAAALAm9f4e5Kqqqvz5z39O7969M2fOnLRr165mrWXLlqmqqsrChQvrZA0AAADWpN4D+eKLL07Tpk1z+OGH1/dTAwAAwBo1rs8nGzlyZN58882MGzcuxcXFKSsry+zZs2vW58+fn+Li4rRo0aJO1gAAAGBN6u0M8mWXXZbnn38+V111VUpKSpIkXbp0ybJlyzJ9+vQkyc0335x+/frV2RoAAACsSb2cQX711Vdz9dVXZ8stt8zgwYOTJJtvvnmuuuqqjBo1KsOGDcvy5cvTvn37jB49OklSXFz8ta8BAADAmhRVV1dXF3qIddG8eYtTVeVHQ2G1br1x/j7q2EKPQR3Y+ae/y/vvf1ToMQBgvde69ca57LzjCz0GdeCMS67+Ur9PFRcXpVWrZqtf+6pDAQAAwPpAIAMAAEAEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAElqGcjz58/PkiVLkiSVlZW5/fbbc8cdd6SqqqpOhwMAAID6UqtAPv744/Pmm28mSS6//PJcf/31ueGGG3LppZfW6XAAAABQX2oVyLNmzUrnzp2TJHfddVeuvfba3Hjjjbn33nvrdDgAAACoL41rs1NxcXFWrlyZN954IxtvvHHatWuXqqqqmsuuAQAAoKGrVSDvvvvuOe2007Jw4cLss88+SZLXXnstbdu2rdPhAAAAoL7UKpB/8Ytf5I477kjjxo2z//77J0kWLFiQU089tU6HAwAAgPpSq0AuKSnJoEGDUlVVlQ8++CBt2rRJz54963o2AAAAqDe1+pCuDz/8MGeeeWa6du2avffeO0kyderUXH755XU6HAAAANSXWgXysGHD0qxZszz00ENp0qRJkqR79+6577776nQ4AAAAqC+1usT6iSeeyGOPPZYmTZqkqKgoSdKyZcvMmzevTocDAACA+lKrM8gbb7xxFixYsMq22bNnp3Xr1nUyFAAAANS3WgXyoYcemiFDhuTJJ59MVVVVnn322ZxzzjkZPHhwXc8HAAAA9aJWl1gfd9xxKS0tzfDhw/PJJ5/k/PPPz6BBg3LkkUfW9XwAAABQL2oVyEVFRTnyyCMFMQAAAOutWn9I1+qUlJRks802S/v27b/WoQAAAKC+1SqQL7jggsydOzdJ0qJFiyxcuDBJ0qpVq3zwwQfp1KlTLrvssmy55ZZ1NScAAADUqVp9SNchhxySI444ItOnT8/jjz+e6dOn58gjj8zgwYMzbdq0dOnSJRdddFFdzwoAAAB1plaB/Ic//CFnnnlmNthggyTJBhtskNNPPz033nhjmjZtmnPPPTfPP/98nQ4KAAAAdalWgdy0adM899xzq2x74YUXsuGGG376IMW1ehgAAABYZ9XqPchDhgzJ0Ucfnd69e6esrCzvvvtuHn744Vx44YVJPv0Qr759+9bpoAAAAFCXahXIBxxwQLp06ZIpU6Zk7ty52XLLLfOXv/wlW2+9dZLke9/7Xr73ve/V6aAAAABQl2oVyEmy9dZb1wQxAAAArG9qHchTp07NtGnTsmDBglRXV9dsHzVqVJ0MBgAAAPWpVp+u9etf/zrDhg1LVVVVJk+enBYtWuTxxx9P8+bN63o+AAAAqBe1CuTbb789119/fc4///w0adIk559/fsaNG5d//etfdT0fAAAA1ItaBfKHH36YbbfdNknSpEmTrFy5Ml27ds20adPqdDgAAACoL7V6D/I3v/nNvPrqq9lmm22yzTbb5M9//nOaN2+eTTbZpK7nAwAAgHpRq0A+/fTTs3DhwiTJmWeembPOOitLly7NsGHDav1EI0eOzJQpU/LOO+/k7rvvrjkj3bt375SUlKS0tDRJctZZZ6VXr15JkhkzZmTo0KFZvnx52rdvn9GjR6dVq1ZfaQ0AAABWp1aXWO+xxx7p0aNHkmTHHXfMAw88kL/97W/Ze++9a/1Effr0yfjx49O+ffvPrY0dOzYTJ07MxIkTa+K4qqoqZ599doYOHZopU6akvLw8Y8aM+UprAAAAsCa1CuQk+fjjj/Pyyy/nmWeeWeW/2iovL09ZWVmt93/++edTWlqa8vLyJMngwYMzefLkr7QGAAAAa1KrS6zvvPPODB8+PE2aNMkGG2xQs72oqCh//etfv/IQZ511Vqqrq7PzzjvnjDPOSPPmzTNnzpy0a9euZp+WLVumqqoqCxcu/NJrLVq0+MqzAgAAsH6qVSCPHj06V155ZXbbbbevfYDx48enrKwsK1asyC9+8YsMHz7cJdEAAADUu1pdYt2kSZNUVFTUyQD/vuy6pKQkhx12WM1l22VlZZk9e3bNfvPnz09xcXFatGjxpdcAAABgTWoVyKeddlouvfTSzJ8//2t98qVLl+ajjz5KklRXV+fee+9N586dkyRdunTJsmXLMn369CTJzTffnH79+n2lNQAAAFiTWl1iveWWW2bs2LH505/+VLOturo6RUVFeemll2r1RCNGjMj999+fDz74IEcddVRatGiRcePG5dRTT01lZWWqqqrSsWPHmq+OKi4uzqhRozJs2LBVvq7pq6wBAADAmhRVV1dX/6ed9tprrwwYMCD77LPPKh/SlSTf/OY362y4Qpo3b3Gqqv7jjwbqVOvWG+fvo44t9BjUgZ1/+ru8//5HhR4DANZ7rVtvnMvOO77QY1AHzrjk6i/1+1RxcVFatWq22rVanUFeuHBhTjvttBQVFX3hJwcAAICGoFbvQT7ooIMyceLEup4FAAAACqZWZ5BnzpyZ8ePH57e//W2+8Y1vrLI2fvz4OhkMAAAA6lOtAvkHP/hBfvCDH9T1LAAAAFAwtQrkAw88sK7nAAAAgIJaYyDfeeedOeCAA5Ikt9122xof4JBDDvnahwIAAID6tsZAvueee2oCeU0f0FVUVCSQAQAAWC+sMZCvvfbamj/fdNNN9TIMAAAAFEqtvuYJAAAA1ncCGQAAACKQAQAAIIlABgAAgCRr+ZCut99+u1YP0KFDh69tGAAAACiUNQbyXnvtlaKiolRXV6/xzkVFRXnppZfqZDAAAACoT2sM5Jdffrk+5wAAAICC8h5kAAAAyFrOIH/WJ598kj/96U+ZNm1aFixYsMpl1+PHj6+z4QAAAKC+1OoM8iWXXJK//OUvKS8vzwsvvJC999478+bNyy677FLX8wEAAEC9qFUg33///bn22mtz5JFHplGjRjnyyCNz1VVX5amnnqrr+QAAAKBe1CqQly1blrKysiTJBhtskI8//jgdO3bMiy++WKfDAQAAQH2p1XuQO3bsmOeeey5du3ZNly5dcuWVV6ZZs2Zp27ZtXc8HAAAA9aJWZ5DPP//8NGrUKEly7rnn5sUXX8zDDz+ciy++uE6HAwAAgPpSqzPIZWVlad26dZJkyy23zA033JAkef/99+tsMAAAAKhPtTqD3Ldv39VuHzBgwNc6DAAAABRKrQL5s997/G+LFy9OUVHR1z4QAAAAFMJaL7HeY489UlRUlOXLl2fPPfdcZW3hwoXOIAMAALDeWGsgjx49OtXV1fnxj3+cUaNG1WwvKipKq1atstVWW9X5gAAAAFAf1hrIFRUVSZInn3wyG264Yb0MBAAAAIVQq/cgN27cOGPHjk2fPn2yww47pE+fPhk7dmxWrFhR1/MBAABAvajV1zyNHj06M2fOzEUXXZR27dpl9uzZ+c1vfpPFixfn/PPPr+sZAQAAoM7VKpAnT56ciRMnZtNNN02SbLXVVtluu+2y//77C2QAAADWC1/6a57Wth0AAAAamrUG8qRJk5Ik/fr1y4knnpjHHnssr7/+eh599NGcfPLJ6d+/f70MCQAAAHVtrZdYDx06NAMHDszZZ5+d3/72txk+fHjmzp2bNm3aZMCAATnppJPqa04AAACoU2sN5H9fQl1SUpLTTjstp512Wr0MBQAAAPVtrYFcVVWVJ598cq3vNd51112/9qEAAACgvq01kFesWJELLrhgjYFcVFSUqVOn1slgAAAAUJ/WGsgbbrihAAYAAOC/Qq2+5gkAAADWd2sNZN9zDAAAwH+LtQbys88+W19zAAAAQEG5xBoAAAAikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEhST4E8cuTI9O7dO506dcorr7xSs/2NN97IoEGD0rdv3wwaNCizZs2q0zUAAABYk3oJ5D59+mT8+PFp3779KtuHDRuWww47LFOmTMlhhx2WoUOH1ukaAAAArEm9BHJ5eXnKyspW2TZv3ry8+OKLGThwYJJk4MCBefHFFzN//vw6WQMAAIC1aVyoJ54zZ07atm2bRo0aJUkaNWqUNm3aZM6cOamurv7a11q2bFmYAwUAAKBB8CFdAAAAkAKeQS4rK8t7772XysrKNGrUKJWVlZk7d27KyspSXV39ta8BAADA2hTsDHKrVq3SuXPnTJo0KUkyadKkdO7cOS1btqyTNQAAAFibourq6uq6fpIRI0bk/vvvzwcffJBNN900LVq0yD333JPXX3895557bj788MM0b948I0eOzFZbbZUkdbL2RcybtzhVVXX+o4G1at164/x91LGFHoM6sPNPf5f33/+o0GMAwHqvdeuNc9l5xxd6DOrAGZdc/aV+nyouLkqrVs1Wu1YvgdwQCWTWBQJ5/SWQAaB+COT1V10Esg/pAgAAgAhkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIkjQs9QJL07t07JSUlKS0tTZKcddZZ6dWrV2bMmJGhQ4dm+fLlad++fUaPHp1WrVolyZdeAwAAgNVZZ84gjx07NhMnTszEiRPTq1evVFVV5eyzz87QoUMzZcqUlJeXZ8yYMUnypdcAAABgTdaZQP6/nn/++ZSWlqa8vDxJMnjw4EyePPkrrQEAAMCarBOXWCefXlZdXV2dnXfeOWeccUbmzJmTdu3a1ay3bNkyVVVVWbhw4Zdea9GiRX0eEgAAAA3IOnEGefz48bnrrrty++23p7q6OsOHDy/0SAAAAPyXWScCuaysLElSUlKSww47LM8880zKysoye/bsmn3mz5+f4uLitGjR4kuvAQAAwJoUPJCXLl2ajz76KElSXV2de++9N507d06XLl2ybNmyTJ8+PUly8803p1+/fknypdcAAABgTQr+HuR58+bl1FNPTWVlZaqqqtKxY8cMGzYsxcXFGTVqVIYNG7bK1zUl+dJrAAAAsCYFD+QOHTrkzjvvXO3aTjvtlLvvvvtrXQMAAIDVKfgl1gAAALAuEMgAAAAQgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQZD0O5DfeeCODBg1K3759M2jQoMyaNavQIwEAALAOW28DediwYTnssMMyZcqUHHbYYRk6dGihRwIAAGAd1rjQA9SFefPm5cUXX8zvf//7JMnAgQNz8cUXZ/78+WnZsmWtHqO4uKguR4RaK2neqtAjUEf8fwYA6kfzFn6fWl99md+n1naf9TKQ58yZk7Zt26ZRo0ZJkkaNGqVNmzaZM2dOrQN50003qssRodZ2OGFkoUegjrRq1azQIwDAf4Vjz/lloUegjnzdv0+tt5dYAwAAwBexXgZyWVlZ3nvvvVRWViZJKisrM3fu3JSVlRV4MgAAANZV62Ugt2rVKp07d86kSZOSJJMmTUrnzp1rfXk1AAAA/32Kqqurqws9RF14/fXXc+655+bDDz9M8+bNM3LkyGy11VaFHgsAAIB11HobyAAAAPBFrJeXWAMAAMAXJZABAAAgAhkAAACSCGQAAABIIpABAAAgiUDmC1i0aFG6du2aESNG5LHHHsv++++f/fffP7vttlt23XXXmtsPPPBAoUdlHbdy5cr86le/St++fbPvvvvmgAMOyKWXXpqVK1fmjTfeyMknn5w+ffrkoIMOyuDBg/Pggw8WemTWYb17984rr7yyyrYjjjgiXbp0ycKFC2u2PfXUU+nUqVNGjhxZzxOyLvsqr59XXnklxxxzTPbaa6/06dMnQ4YMyZw5c+prdBqQ3r1757vf/W4qKytrtk2YMCGdOnXKH//4x0yYMCFDhgwp4ISsy77K6+eWW27JgAED0r9///Tt2zfjxo1LVVVVfY3eIAlkam3SpEnZcccdc88996Rnz56ZOHFiJk6cmMGDB+eAAw6oub3XXnsVelTWceedd15ee+213H777bn77rtz22235Vvf+lY++OCDHH744dlrr70yderUTJgwIVdeeWUWL15c6JFpgLbddtvcc889NbcnTJiQ7bffvoAT0ZD8p9fPokWLctRRR+WQQw7JAw88kKlTp2annXbK0UcfnZUrVxZiZNZxbdq0yeOPP15z+4477vD/JGrty7x+7rzzztx444259tprc9999+WWW27J448/nnHjxtX1uA2aQKbWbr/99px00knp1KlTpk6dWuhxaKBmzZqVBx98MCNGjEizZs2SJI0bN86gQYNy8803p2fPnjnggANq9m/duvUqt6G2/v0Pd0myZMmS/P3vf0+vXr0KPBUNxX96/dx0002pqKhI//79a7b96Ec/SrNmzVYJa/i3Aw88MBMmTEiSvP3221m6dGm23XbbAk9FQ/FlXj9XXnllzjnnnLRr1y5Jsskmm+Siiy7K1VdfnRUrVtT5zA2VQKZWXn755SxcuDC77LJLDjrooNx+++2FHokG6sUXX8wWW2yRTTbZZLVrXbt2LcBUrI86dOiQ0tLSvP7665k8eXK+//3vp3HjxoUeiwbiP71+Xnnlley4446fu9+OO+6Yf/7zn/U5Kg1ERUVFXnnllSxatCh33HGHf/zlC/mir5/FixfnX//6V7p167bK9o4dO6Zx48aZNWtWnc3a0AlkauW2227L/vvvn6Kiouy9996ZOXNm3nvvvUKPBbBWBxxwQO64447ceeedOfDAAws9Dg3M2l4/1dXVBZqKhqqoqCj9+/fPPffck3vuuScDBw4s9Eg0IF/n66eoqOhrnGz9I5D5j1asWJFJkybl9ttvT+/evbPPPvtk5cqVNZd5wBex3Xbb5c0338yiRYtWu/bcc88VYCrWV/369cs999yTpUuXplOnToUehwZmba+fTp065R//+Mfn7jNz5kyvNdbowAMPzNixY7Pttttm0003LfQ4NDBf5PXTrFmzbL755pkxY8Yq219//fWsXLkyW2yxRR1O2rAJZP6jqVOn5lvf+lYeffTRPPTQQ3nooYdy/fXX54477ij0aDRAW265ZXr37p2hQ4fWfPhWZWVlbr311gwaNChPPPFE7r777pr9582blzvvvLNA09LQbbTRRjn77LNzzjnnFHoUGqC1vX4OP/zwPPXUU7nvvvtqtt1www358MMPM2DAgPockwakQ4cO+clPfpKTTjqp0KPQAH3R188pp5ySUaNG1Xy6/qJFi/Lzn/88xx13XEpLS+ty1AbNm7H4j26//fbsu+++q2zr3r17qqqq8vTTTxdoKhqySy+9NFdddVUOPvjgNGnSJFVVVdljjz1ywAEH5KabbsqYMWNyxRVXpGnTpmnatGmOO+64Qo/MOu6oo45Ko0aNam63aNGi5s/77LNPASaiIfkyr58WLVrk+uuvz6hRo/K///u/qa6uTufOnXPdddelSZMmdT0yDdigQYNWu/2RRx7J7rvvXnP7oIMOyumnn15PU9FQfNHXz7Jly3LMMcekuro6lZWV2X///XPiiSfW17gNUlG1N9EAAACAS6wBAAAgEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAPBf49hjj/Ud9gCwFr7mCQAAAOIMMgAAACQRyACwXrnmmmsyZMiQVbaNGDEiI0aMyBFHHJFbb721Zvttt92W/v37p0ePHjnmmGPyzjvvJEnGjh2biy++OEmycuXKdOvWLSNHjkySLFu2LDvssEMWLlxYPwcEAPVIIAPAemTAgAF55JFHsnjx4iRJZWVlJk+enIEDB66y34MPPpirr746v/71r/PEE09k5513zplnnpkk6dGjR55++ukkyXPPPZdvfOMbmT59epLk2Wefzbe+9a20aNGi/g4KAOqJQAaA9Uj79u2z3Xbb5cEHH0ySPPnkk9lggw3SrVu3Vfa7+eab8+Mf/zgdO3ZM48aNc8IJJ+Sll17KO++8k+7du2fWrFlZsGBBpk+fnkMOOSTvvfdelixZkmnTpqWioqIARwYAdU8gA8B6ZuDAgZk0aVKSZNKkSZ87e5wks2fPzi9/+cuUl5envLw8FRUVqa6uznvvvZcNNtggXbp0ybRp0zJt2rT06NEj3bt3zzPPPFNzGwDWR40LPQAA8PXq379/Ro4cmXfffTcPPPBA/vKXv3xun7KyspxwwgnZb7/9VvsYFRUVefLJJ/PSSy9lhx12SEVFRR5//PHMnDlTIAOw3nIGGQDWMy1btkxFRUXOO++8bL755unYsePn9hk8eHCuueaavPrqq0mSjz76KPfdd1/Neo8ePXLnnXemY8eOKSkpSUVFRW699dZsvvnmadmyZb0dCwDUJ2eQAWA9NHDgwJxzzjk5++yzV7u+1157ZcmSJTnjjDPyzjvvZOONN853vvOd9O/fP0nSvXv3LF++vOZs8dZbb53S0tKUl5fX2zEAQH0rqq6uri70EAAAAFBoLrEGAACACGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCTJ/wcGhPXrrf/rTgAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":"count_age=sample_table.groupby(by=\"age\").count()[\"patient_id\"]\ncount_age.reset_index()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.041862Z","iopub.execute_input":"2023-02-06T01:11:38.042173Z","iopub.status.idle":"2023-02-06T01:11:38.072783Z","shell.execute_reply.started":"2023-02-06T01:11:38.042144Z","shell.execute_reply":"2023-02-06T01:11:38.071758Z"},"_kg_hide-input":true,"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"     age  patient_id\n0   26.0          11\n1   28.0          18\n2   29.0           7\n3   30.0           5\n4   31.0           4\n..   ...         ...\n58  85.0          68\n59  86.0          78\n60  87.0          47\n61  88.0          37\n62  89.0          70\n\n[63 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>26.0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>28.0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>29.0</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>30.0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>31.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>85.0</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>86.0</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>87.0</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>88.0</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>89.0</td>\n      <td>70</td>\n    </tr>\n  </tbody>\n</table>\n<p>63 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"With age, it was found a normal distribution centered on 60 years and with outliers from young (20 to 40 years old) to older women (75 to 90 years old).","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per age\")\nsns.histplot(data=sample_table.reset_index(),x=\"age\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.073967Z","iopub.execute_input":"2023-02-06T01:11:38.074774Z","iopub.status.idle":"2023-02-06T01:11:38.559982Z","shell.execute_reply.started":"2023-02-06T01:11:38.074745Z","shell.execute_reply":"2023-02-06T01:11:38.558532Z"},"_kg_hide-input":true,"trusted":true},"execution_count":103,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Then, with the cancer occurrence, it was found that most of the images correspond to Mammographies of non-cancer patients. This could be a challenge for the overall prediction as the percentage of cancer images is 2.11%.","metadata":{}},{"cell_type":"code","source":"count_cancer=sample_table.groupby(by=\"cancer\").count()[\"patient_id\"]\ncount_cancer.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.561879Z","iopub.execute_input":"2023-02-06T01:11:38.562873Z","iopub.status.idle":"2023-02-06T01:11:38.589187Z","shell.execute_reply.started":"2023-02-06T01:11:38.562838Z","shell.execute_reply":"2023-02-06T01:11:38.588116Z"},"_kg_hide-input":true,"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"   cancer  patient_id\n0       0       53548\n1       1        1158","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cancer</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>53548</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1158</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per cancer\")\nsns.barplot(data=count_cancer.reset_index(),x=\"cancer\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.590772Z","iopub.execute_input":"2023-02-06T01:11:38.591351Z","iopub.status.idle":"2023-02-06T01:11:38.8232Z","shell.execute_reply.started":"2023-02-06T01:11:38.591309Z","shell.execute_reply":"2023-02-06T01:11:38.822186Z"},"_kg_hide-input":true,"trusted":true},"execution_count":105,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The biopsy variable follows a similar trend, as it represents that not all the patients that got this procedure got diagnosed with cancer:","metadata":{}},{"cell_type":"code","source":"count_biopsy=sample_table.groupby(by=\"biopsy\").count()[\"patient_id\"]\ncount_biopsy.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.8244Z","iopub.execute_input":"2023-02-06T01:11:38.82469Z","iopub.status.idle":"2023-02-06T01:11:38.853145Z","shell.execute_reply.started":"2023-02-06T01:11:38.824664Z","shell.execute_reply":"2023-02-06T01:11:38.851593Z"},"_kg_hide-input":true,"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"   biopsy  patient_id\n0       0       51737\n1       1        2969","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>biopsy</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>51737</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2969</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per biopsy\")\nsns.barplot(data=count_biopsy.reset_index(),x=\"biopsy\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:38.854669Z","iopub.execute_input":"2023-02-06T01:11:38.855301Z","iopub.status.idle":"2023-02-06T01:11:39.084734Z","shell.execute_reply.started":"2023-02-06T01:11:38.855269Z","shell.execute_reply":"2023-02-06T01:11:39.08344Z"},"_kg_hide-input":true,"trusted":true},"execution_count":107,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"With the invasive variable, it is presented that not all the cancer images represent an invasive disease:","metadata":{}},{"cell_type":"code","source":"count_invasive=sample_table.groupby(by=\"invasive\").count()[\"patient_id\"]\ncount_invasive.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.086301Z","iopub.execute_input":"2023-02-06T01:11:39.087473Z","iopub.status.idle":"2023-02-06T01:11:39.116843Z","shell.execute_reply.started":"2023-02-06T01:11:39.087427Z","shell.execute_reply":"2023-02-06T01:11:39.115687Z"},"_kg_hide-input":true,"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"   invasive  patient_id\n0         0       53888\n1         1         818","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>invasive</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>53888</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>818</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per invasive\")\nsns.barplot(data=count_invasive.reset_index(),x=\"invasive\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.11858Z","iopub.execute_input":"2023-02-06T01:11:39.118951Z","iopub.status.idle":"2023-02-06T01:11:39.350731Z","shell.execute_reply.started":"2023-02-06T01:11:39.118918Z","shell.execute_reply":"2023-02-06T01:11:39.349809Z"},"_kg_hide-input":true,"trusted":true},"execution_count":109,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The BIRADS variable represents that it was a reduced number of patients that required a follow-up analysis and that got a positive result of cancer:","metadata":{}},{"cell_type":"code","source":"count_BIRADS=sample_table.groupby(by=\"BIRADS\").count()[\"patient_id\"]\ncount_BIRADS.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.352232Z","iopub.execute_input":"2023-02-06T01:11:39.352622Z","iopub.status.idle":"2023-02-06T01:11:39.380103Z","shell.execute_reply.started":"2023-02-06T01:11:39.35259Z","shell.execute_reply":"2023-02-06T01:11:39.37894Z"},"_kg_hide-input":true,"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"   BIRADS  patient_id\n0     0.0        8249\n1     1.0       15772\n2     2.0        2265","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BIRADS</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>8249</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>15772</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>2265</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per BIRAIDS\")\nsns.barplot(data=count_BIRADS.reset_index(),x=\"BIRADS\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.381493Z","iopub.execute_input":"2023-02-06T01:11:39.381889Z","iopub.status.idle":"2023-02-06T01:11:39.643746Z","shell.execute_reply.started":"2023-02-06T01:11:39.381858Z","shell.execute_reply":"2023-02-06T01:11:39.642618Z"},"_kg_hide-input":true,"trusted":true},"execution_count":111,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The patients with implants were low with a similar proportion of the previous variables, but with no initial relationship with cancer:","metadata":{}},{"cell_type":"code","source":"count_implants=sample_table.groupby(by=\"implant\").count()[\"patient_id\"]\ncount_implants.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.645574Z","iopub.execute_input":"2023-02-06T01:11:39.645935Z","iopub.status.idle":"2023-02-06T01:11:39.674147Z","shell.execute_reply.started":"2023-02-06T01:11:39.645903Z","shell.execute_reply":"2023-02-06T01:11:39.673109Z"},"_kg_hide-input":true,"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"   implant  patient_id\n0        0       53229\n1        1        1477","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>implant</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>53229</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1477</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per implants\")\nsns.barplot(data=count_implants.reset_index(),x=\"implant\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:39.677188Z","iopub.execute_input":"2023-02-06T01:11:39.677546Z","iopub.status.idle":"2023-02-06T01:11:40.116418Z","shell.execute_reply.started":"2023-02-06T01:11:39.677516Z","shell.execute_reply":"2023-02-06T01:11:40.115532Z"},"_kg_hide-input":true,"trusted":true},"execution_count":113,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 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BRk8eHCqqqoyYsSIrFy5sv41hVgDAACAt9OkZ5CnTJmSadOmZdq0aenbt29qa2tz7rnnZuzYsZk1a1YqKyszefLkJCnIGgAAADSkqJdYL1q0KC1btkxlZWWSZNiwYZk5c2bB1gAAAKAhzZpyZ6NHj05dXV0OPvjgfP3rX091dXW6dOlSv96hQ4fU1tZmzZo1BVlr165dk7xPAAAAdj5NdgZ56tSpmT59em6//fbU1dVl/PjxTbVrAAAAeEdNFsgVFRVJkhYtWmT48OF59NFHU1FRkRdeeKH+OatWrUppaWnatWtXkDUAAABoSJME8oYNG/Lyyy8nSerq6nL33XenR48e6dmzZzZt2pR58+YlSW699dYMGDAgSQqyBgAAAA1pks8gr1y5MmeddVZqampSW1ubffbZJ+PGjUtpaWkmTpyYcePGZfPmzenatWsmTZqUJAVZAwAAgIaU1NXV1RV7iB3RypXrUlu7cxyajh13y/BvTC32GAAkuXniF/Liiy8XewwAoAGlpSUpL2/z9mtNPAsAAADskAQyAAAARCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQpQiD/6Ec/Svfu3fPUU08lSRYsWJDBgwenqqoqI0aMyMqVK+ufW4g1AAAAeDtNGsiPP/54FixYkK5duyZJamtrc+6552bs2LGZNWtWKisrM3ny5IKtAQAAQEOaLJC3bNmS8ePH59vf/nb9Y4sWLUrLli1TWVmZJBk2bFhmzpxZsDUAAABoSJMF8g9/+MMMHjw4e+65Z/1j1dXV6dKlS/3vHTp0SG1tbdasWVOQNQAAAGhIkwTy/Pnzs2jRogwfPrwpdgcAAADvWrOm2MkjjzySJUuWpH///kmSf/zjHzn55JNz4okn5oUXXqh/3qpVq1JaWpp27dqloqJiu68BAABAQ5rkDPJXvvKVPPjgg7nvvvty3333ZY899sj111+fL3/5y9m0aVPmzZuXJLn11lszYMCAJEnPnj23+xoAAAA0pEnOIDektLQ0EydOzLhx47J58+Z07do1kyZNKtgaAAAANKSkrq6urthD7IhWrlyX2tqd49B07Lhbhn9jarHHACDJzRO/kBdffLnYYwAADSgtLUl5eZu3X2viWQAAAGCHJJABAAAgAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABI0shAXrVqVdavX58kqampye23355f//rXqa2tLehwAAAA0FQaFcinnnpqnnvuuSTJ5ZdfnhtuuCE33nhjvve97xV0OAAAAGgqjQrkZ599Nj169EiSTJ8+Pdddd11uuumm3H333QUdDgAAAJpKs8Y8qbS0NK+88kqWLl2a3XbbLV26dEltbW39ZdcAAACws2tUIB955JEZNWpU1qxZk4EDByZJnnnmmXTu3LmgwwEAAEBTaVQgf+c738mvf/3rNGvWLEOGDEmSrF69OmeddVZBhwMAAICm0qhAbtGiRYYOHZra2tqsWLEinTp1Sp8+fQo9GwAAADSZRt2ka+3atTnnnHNywAEH5JhjjkmS3Hvvvbn88ssLOhwAAAA0lUYF8rhx49KmTZvcd999ad68eZKkV69e+e1vf1vQ4QAAAKCpNOoS6z/96U+ZM2dOmjdvnpKSkiRJhw4dsnLlyoIOBwAAAE2lUWeQd9ttt6xevXqrx1544YV07NixIEMBAABAU2tUIJ9wwgkZOXJkHn744dTW1mb+/Pk577zzMmzYsELPBwAAAE2iUZdYn3LKKWnZsmXGjx+fV199Neeff36GDh2ak046qdDzAQAAQJNoVCCXlJTkpJNOEsQAAAC8bzX6Jl1vp0WLFtljjz3StWvX7ToUAAAANLVGBfIFF1yQ5cuXJ0natWuXNWvWJEnKy8uzYsWKdO/ePZdddln23nvvQs0JAAAABdWom3Qdf/zxOfHEEzNv3rw8+OCDmTdvXk466aQMGzYsjzzySHr27JmLLrqo0LMCAABAwTQqkH/2s5/lnHPOSatWrZIkrVq1ytlnn52bbroprVu3zpgxY7Jo0aJtbuP000/P4MGDc9xxx2X48OF54oknkiRLly7N0KFDU1VVlaFDh+bZZ5+tf00h1gAAAODtNCqQW7dunccee2yrxx5//PHssssur22k9J03M2HChEyfPj133nlnRowYkfPPPz9JMm7cuAwfPjyzZs3K8OHDM3bs2PrXFGINAAAA3k6jAnnkyJEZMWJEzjnnnEyePDmjR4/OySefnFGjRiV57SZeVVVV29zGbrvtVv/zunXrUlJSkpUrV2bx4sUZNGhQkmTQoEFZvHhxVq1aVZA1AAAAaEijbtJ13HHHpWfPnpk1a1aWL1+evffeO7/4xS+y7777Jkk+/vGP5+Mf//g7bueCCy7IQw89lLq6uvzkJz9JdXV1OnfunLKysiRJWVlZOnXqlOrq6tTV1W33tQ4dOrz7IwQAAMAHQqMCOUn23Xff+iB+r77zne8kSe68885MnDix/gw0AAAAFFujA/nee+/NI488ktWrV6eurq7+8YkTJ77rnR533HEZO3Zs9thjjyxbtiw1NTUpKytLTU1Nli9fnoqKitTV1W33NQAAAGhIoz6D/KMf/Sjjxo1LbW1tZs6cmXbt2uXBBx9M27ZtG7WT9evXp7q6uv73++67L7vvvnvKy8vTo0ePzJgxI0kyY8aM9OjRIx06dCjIGgAAADSkpO6Np4Mb8PGPfzzXXntt9t9//1RWVmbevHlZuHBhrrrqqlxzzTXvuJMVK1bk9NNPz8aNG1NaWprdd9895513Xj760Y9myZIlGTNmTNauXZu2bdtmwoQJ6datW5IUZK2xVq5cl9radzw0O4SOHXfL8G9MLfYYACS5eeIX8uKLLxd7DACgAaWlJSkvb/O2a40K5IMPPjh/+ctfkiSHHXZYHnjggTRv3nyrx99vBDIA74VABoAd27YCuVGfQf7whz+cp59+Ovvtt1/222+/3HLLLWnbtm1233337TooAAAAFEujAvnss8/OmjVrkiTnnHNORo8enQ0bNmTcuHGFnA0AAACaTKMC+aijjqr/+WMf+1h+97vfFWwgAAAAKIZGf83Txo0b89xzz2XDhg1bPX7QQQdt96EAAACgqTUqkO+8886MHz8+zZs3T6tWreofLykpyR/+8IdCzQYAAABNplGBPGnSpFxxxRU5/PDDCz0PAAAAFEVpY57UvHnz9O7du9CzAAAAQNE0KpBHjRqV733ve1m1alWh5wEAAICiaNQl1nvvvXemTJmSm2++uf6xurq6lJSU5IknnijYcAAAANBUGhXI3/jGNzJkyJAMHDhwq5t0AQAAwPtFowJ5zZo1GTVqVEpKSgo9DwAAABRFoz6D/NnPfjbTpk0r9CwAAABQNI06g7xw4cJMnTo1V199dT70oQ9ttTZ16tSCDAYAAABNqVGB/PnPfz6f//znCz0LAAAAFE2jAvkzn/lMoecAAACAomowkO+8884cd9xxSZLbbrutwQ0cf/zx230oAAAAaGoNBvJdd91VH8gN3aCrpKREIAMAAPC+0GAgX3fddfU///znP2+SYQAAAKBYGvU1TwAAAPB+J5ABAAAgAhkAAACSCGQAAABIso2bdP3tb39r1Ab22muv7TYMAAAAFEuDgfzJT34yJSUlqaura/DFJSUleeKJJwoyGAAAADSlBgP5ySefbMo5AAAAoKh8BhkAAACyjTPIb/Tqq6/m5ptvziOPPJLVq1dvddn11KlTCzYcAAAANJVGnUG+9NJL84tf/CKVlZV5/PHHc8wxx2TlypU59NBDCz0fAAAANIlGBfI999yT6667LieddFLKyspy0kkn5corr8zcuXMLPR8AAAA0iUYF8qZNm1JRUZEkadWqVTZu3Jh99tknixcvLuhwAAAA0FQa9RnkffbZJ4899lgOOOCA9OzZM1dccUXatGmTzp07F3o+AAAAaBKNOoN8/vnnp6ysLEkyZsyYLF68OPfff38uvvjigg4HAAAATaVRZ5ArKirSsWPHJMnee++dG2+8MUny4osvFmwwAAAAaEqNOoNcVVX1to9/+tOf3q7DAAAAQLE0KpDf+L3Hr1u3bl1KSkq2+0AAAABQDNu8xPqoo45KSUlJNm/enKOPPnqrtTVr1jiDDAAAwPvGNgN50qRJqaury1e+8pVMnDix/vGSkpKUl5enW7duBR8QAAAAmsI2A7l3795Jkocffji77LJLkwwEAAAAxdCozyA3a9YsU6ZMSf/+/fNv//Zv6d+/f6ZMmZItW7YUej4AAABoEo36mqdJkyZl4cKFueiii9KlS5e88MILueqqq7Ju3bqcf/75hZ4RAAAACq5RgTxz5sxMmzYt7du3T5J069YtH/nIRzJkyBCBDAAAwPvCe/6ap209DgAAADubbQbyjBkzkiQDBgzIaaedljlz5mTJkiV54IEHcsYZZ+RTn/pUkwwJAAAAhbbNS6zHjh2bQYMG5dxzz83VV1+d8ePHZ/ny5enUqVM+/elP5/TTT2+qOQEAAKCgthnIr19C3aJFi4waNSqjRo1qkqEAAACgqW0zkGtra/Pwww9v87PGhx122HYfCgAAAJraNgN5y5YtueCCCxoM5JKSktx7770FGQwAAACa0jYDeZdddhHAAAAAfCA06mueAAAA4P1um4Hse44BAAD4oNhmIM+fP7+p5gAAAICicok1AAAARCADAABAEoEMAAAASQQyAAAAJBHIAAAAkEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQRCADAABAEoEMAAAASQQyAAAAJBHIAAAAkKSJAnn16tU55ZRTUlVVlWOPPTZnnnlmVq1alSRZsGBBBg8enKqqqowYMSIrV66sf10h1gAAAODtNEkgl5SU5Mtf/nJmzZqV3/zmN9lrr70yefLk1NbW5txzz83YsWMza9asVFZWZvLkyUlSkDUAAABoSJMEcrt27dKnT5/63w888MC88MILWbRoUVq2bJnKysokybBhwzJz5swkKcgaAAAANKTJP4NcW1ubW265Jf369Ut1dXW6dOlSv9ahQ4fU1tZmzZo1BVkDAACAhjR5IF988cVp3bp1vvjFLzb1rgEAAKBBzZpyZxMmTMhzzz2Xa665JqWlpamoqMgLL7xQv75q1aqUlpamXbt2BVkDAACAhjTZGeTLLrssixYtypVXXpkWLVokSXr27JlNmzZl3rx5SZJbb701AwYMKNgaAAAANKSkrq6urtA7efrppzNo0KDsvffeadWqVZJkzz33zJVXXplHH30048aNy+bNm9O1a9dMmjQpH/rQh5KkIGuNtXLlutTWFvzQbBcdO+6W4d+YWuwxAEhy88Qv5MUXXy72GABAA0pLS1Je3uZt15okkHdGAhmA90IgA8CObVuB3OQ36QIAAIAdkUAGAACACGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCRNFMgTJkxIv3790r179zz11FP1jy9dujRDhw5NVVVVhg4dmmeffbagawAAANCQJgnk/v37Z+rUqenatetWj48bNy7Dhw/PrFmzMnz48IwdO7agawAAANCQJgnkysrKVFRUbPXYypUrs3jx4gwaNChJMmjQoCxevDirVq0qyBoAAABsS7Ni7bi6ujqdO3dOWVlZkqSsrCydOnVKdXV16urqtvtahw4divNGAQAA2Cm4SRcAAACkiGeQKyoqsmzZstTU1KSsrCw1NTVZvnx5KioqUldXt93XAAAAYFuKdga5vLw8PXr0yIwZM5IkM2bMSI8ePdKhQ4eCrAEAAMC2lNTV1dUVeieXXHJJ7rnnnqxYsSLt27dPu3btctddd2XJkiUZM2ZM1q5dm7Zt22bChAnp1q1bkhRk7d1YuXJdamsLfmi2i44dd8vwb0wt9hgAJLl54hfy4osvF3sMAKABpaUlKS9v87ZrTRLIOyOBDMB7IZABYMe2rUB2ky4AAACIQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABAAAgiUAGAACAJAIZAAAAkghkAAAASCKQAQAAIIlABgAAgCQCGQAAAJIIZAAAAEgikAEAACBJ0qzYAwAAvFvtd2+RZi1aFnsMAJK8umVzVr+0pdhjbBcCGQDY6TRr0TJ/mfjlYo8BQJKDv/GTJO+PQHaJNQAAAEQgAwAAQBKBDAAAAEkEMgAAACQRyAAAAJBEIAMAAECS93EgL126NEOHDk1VVVWGDh2aZ599ttgjAQAAsAN73wbyuHHjMnz48MyaNSvDhw/P2LFjiz0SAAAAO7BmxR6gEFauXJnFixfnpz/9aZJk0KBBufjii7Nq1ap06NChUdsoLS0p5Ijb3Yfa71rsEQD4Pzvbv0N2Vi3alhd7BAD+z870775tzfq+DOTq6up07tw5ZWVlSZKysrJ06tQp1dXVjQ7k9jtZcE755nHFHgGA/1Ne3qbYI3wg/NtXJxR7BAD+z/vl333v20usAQAA4N14XwZyRUVFli1blpqamiRJTU1Nli9fnoqKiiJPBgAAwI7qfRnI5eXl6dGjR2bMmJEkmTFjRnr06NHoy6sBAAD44Cmpq6urK/YQhbBkyZKMGTMma9euTdu2bTNhwoR069at2GMBAACwg3rfBjIAAAC8G+/LS6wBAADg3RLIAAAAEIEMAAAASQQyAAAAJBHIAAAAkEQgAzuApUuXZujQoamqqsrQoUPz7LPPFnskACiYCRMmpF+/funevXueeuqpYo8DvIFABopu3LhxGT58eGbNmpXhw4dn7NixxR4JAAqmf//+mTp1arp27VrsUYA3EchAUa1cuTKLFy/OoEGDkiSDBg3K4sWLs2rVqiJPBgCFUVlZmYqKimKPAbwNgQwUVXV1dTp37pyysrIkSVlZWTp16pTq6uoiTwYAwAeNQAYAAIAIZKDIKioqsmzZstTU1CRJampqsnz5cpeeAQDQ5AQyUFTl5eXp0aNHZsyYkSSZMWNGevTokQ4dOhR5MgAAPmhK6urq6oo9BPDBtmTJkowZMyZr165N27ZtM2HChHTr1q3YYwFAQVxyySW55557smLFirRv3z7t2rXLXXfdVeyxgAhkAAAASOISawAAAEgikAEAACCJQAYAAIAkAhkAAACSCGQAAABIIpABYKfx6U9/OnPnzt2u2/z73/+e7t2759VXX92u2wWAnVGzYg8AADTOjv49qWPGjEnnzp3zta99rdijAMB74gwyAAAARCADwE6jX79++eMf/5grrrgiI0eOzOjRo9OrV68ce+yxWbp0aa699tocdthhOeqoo/Lggw/Wv+7EE0/M97///Rx//PE56KCDctppp2XNmjVvu4/bb789n/rUp9KrV6/0798/t956a/3a3Llzc+SRR+aGG27IYYcdliOOOCK33357kuQXv/hFfvOb3+T6669Pr1698tWvfrWgxwIACkEgA8BO6P7778+QIUPyyCOPpEePHjn55JNTW1ubBx54IGeccUbGjh271fPvvPPOfPe7382DDz6YZs2a5ZJLLnnb7ZaXl+faa6/No48+mksvvTSXXnppHn/88fr1FStW5OWXX84DDzyQ73znOxk/fnxeeumlDB06NMcee2xOPvnkzJ8/P9dcc01B3z8AFIJABoCdUGVlZfr27ZtmzZplwIABWb16db7yla+kefPmGThwYJ5//vmsXbu2/vlDhgzJ/vvvn9atW2fUqFGZOXNmampq3rLdo48+Oh/+8IdTUlKS3r175/DDD8+8efPq15s1a5YzzjgjzZs3z1FHHZXWrVtn6dKlTfKeAaDQ3KQLAHZC5eXl9T+3atUq7du3T1lZWf3vSbJhw4a0bds2SVJRUVH//C5duuSVV17J6tWr37Ld2bNn58orr8yzzz6b2trabNq0Kfvvv3/9ert27dKs2f//z4dddtklGzZs2L5vDgCKxBlkAPgAqK6u3urn5s2bp3379ls9Z8uWLRk5cmRGjBiRhx56KPPmzcuRRx6Zurq6Ru2jpKRku84MAE1NIAPAB8D06dPzzDPPZOPGjfnhD3+Yqqqq+jPOr9uyZUu2bNmSDh06pFmzZpk9e3YeeuihRu+jvLw8f//737f36ADQZAQyAHwADBkyJGPGjMnhhx+eLVu25IILLnjLc9q0aZMLL7wwZ599dg455JDMmDEj/fr1a/Q+jj/++DzzzDOprKzM6aefvj3HB4AmUVLX2OumAICd0oknnpjBgwfnhBNOKPYoALBDcwYZAAAAIpABAAAgiUusAQAAIIkzyAAAAJBEIAMAAEASgQwAAABJBDIAAAAkEcgAAACQJPl/caQ0iMvLLkMAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The density variable shows that most of the patients have a tissue density in the categories B and C while the number with D is the lowest.","metadata":{}},{"cell_type":"code","source":"count_density=sample_table.groupby(by=\"density\").count()[\"patient_id\"]\ncount_density.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.120178Z","iopub.execute_input":"2023-02-06T01:11:40.121051Z","iopub.status.idle":"2023-02-06T01:11:40.15046Z","shell.execute_reply.started":"2023-02-06T01:11:40.121011Z","shell.execute_reply":"2023-02-06T01:11:40.149574Z"},"_kg_hide-input":true,"trusted":true},"execution_count":114,"outputs":[{"execution_count":114,"output_type":"execute_result","data":{"text/plain":"  density  patient_id\n0       A        3105\n1       B       12651\n2       C       12175\n3       D        1539","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>density</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>3105</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>B</td>\n      <td>12651</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C</td>\n      <td>12175</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>D</td>\n      <td>1539</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per density\")\nsns.barplot(data=count_density.reset_index(),x=\"density\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.152091Z","iopub.execute_input":"2023-02-06T01:11:40.152832Z","iopub.status.idle":"2023-02-06T01:11:40.338241Z","shell.execute_reply.started":"2023-02-06T01:11:40.152785Z","shell.execute_reply":"2023-02-06T01:11:40.33702Z"},"_kg_hide-input":true,"trusted":true},"execution_count":115,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"After, with the machine_id it was found a total of 10 machines, with the number 49 being the most representative.","metadata":{}},{"cell_type":"code","source":"count_machine=sample_table.groupby(by=\"machine_id\").count()[\"patient_id\"]\ncount_machine.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.339862Z","iopub.execute_input":"2023-02-06T01:11:40.34019Z","iopub.status.idle":"2023-02-06T01:11:40.366864Z","shell.execute_reply.started":"2023-02-06T01:11:40.34016Z","shell.execute_reply":"2023-02-06T01:11:40.365842Z"},"_kg_hide-input":true,"trusted":true},"execution_count":116,"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"   machine_id  patient_id\n0          21        8221\n1          29        8267\n2          48        8699\n3          49       23529\n4          93        1915","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>machine_id</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>21</td>\n      <td>8221</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29</td>\n      <td>8267</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>48</td>\n      <td>8699</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>49</td>\n      <td>23529</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>93</td>\n      <td>1915</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per machine_id\")\nsns.barplot(data=count_machine.reset_index(),x=\"machine_id\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.36799Z","iopub.execute_input":"2023-02-06T01:11:40.368279Z","iopub.status.idle":"2023-02-06T01:11:40.612382Z","shell.execute_reply.started":"2023-02-06T01:11:40.368252Z","shell.execute_reply":"2023-02-06T01:11:40.611076Z"},"_kg_hide-input":true,"trusted":true},"execution_count":117,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Finally, the difficult negative cases show that only 7000 of 54000 images correspond to a difficult final analysis:","metadata":{}},{"cell_type":"code","source":"# difficult_negative_case\ncount_difficult=sample_table.groupby(by=\"difficult_negative_case\").count()[\"patient_id\"]\ncount_difficult.reset_index().head()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.614137Z","iopub.execute_input":"2023-02-06T01:11:40.614612Z","iopub.status.idle":"2023-02-06T01:11:40.64516Z","shell.execute_reply.started":"2023-02-06T01:11:40.614567Z","shell.execute_reply":"2023-02-06T01:11:40.644243Z"},"_kg_hide-input":true,"trusted":true},"execution_count":118,"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":"   difficult_negative_case  patient_id\n0                    False       47001\n1                     True        7705","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>difficult_negative_case</th>\n      <th>patient_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>47001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>True</td>\n      <td>7705</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nplt.grid()\nplt.title(\"Number of images per difficult_negative_case\")\nsns.barplot(data=count_difficult.reset_index(),x=\"difficult_negative_case\",y=\"patient_id\")\nplt.ylabel(\"Total images\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.64663Z","iopub.execute_input":"2023-02-06T01:11:40.647299Z","iopub.status.idle":"2023-02-06T01:11:40.873198Z","shell.execute_reply.started":"2023-02-06T01:11:40.647258Z","shell.execute_reply":"2023-02-06T01:11:40.871236Z"},"_kg_hide-input":true,"trusted":true},"execution_count":119,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Additionally, it was checked the sample test table, corresponded with the properties described in the documentation.","metadata":{}},{"cell_type":"code","source":"sample_test=pd.read_csv(\"/kaggle/input/rsna-breast-cancer-detection/test.csv\")\nsample_test","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.874539Z","iopub.execute_input":"2023-02-06T01:11:40.874922Z","iopub.status.idle":"2023-02-06T01:11:40.890929Z","shell.execute_reply.started":"2023-02-06T01:11:40.87489Z","shell.execute_reply":"2023-02-06T01:11:40.889615Z"},"_kg_hide-input":true,"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"   site_id  patient_id    image_id laterality view  age  implant  machine_id  \\\n0        2       10008   736471439          L  MLO   81        0          21   \n1        2       10008  1591370361          L   CC   81        0          21   \n2        2       10008    68070693          R  MLO   81        0          21   \n3        2       10008   361203119          R   CC   81        0          21   \n\n  prediction_id  \n0       10008_L  \n1       10008_L  \n2       10008_R  \n3       10008_R  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>site_id</th>\n      <th>patient_id</th>\n      <th>image_id</th>\n      <th>laterality</th>\n      <th>view</th>\n      <th>age</th>\n      <th>implant</th>\n      <th>machine_id</th>\n      <th>prediction_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>10008</td>\n      <td>736471439</td>\n      <td>L</td>\n      <td>MLO</td>\n      <td>81</td>\n      <td>0</td>\n      <td>21</td>\n      <td>10008_L</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>10008</td>\n      <td>1591370361</td>\n      <td>L</td>\n      <td>CC</td>\n      <td>81</td>\n      <td>0</td>\n      <td>21</td>\n      <td>10008_L</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>10008</td>\n      <td>68070693</td>\n      <td>R</td>\n      <td>MLO</td>\n      <td>81</td>\n      <td>0</td>\n      <td>21</td>\n      <td>10008_R</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>10008</td>\n      <td>361203119</td>\n      <td>R</td>\n      <td>CC</td>\n      <td>81</td>\n      <td>0</td>\n      <td>21</td>\n      <td>10008_R</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Correlation and additional analysis\n\nTo analyze if there is a correlation with the table data, the following heatmap was built. Here it shows that the strongest correlation corresponds to cancer, biopsy, and invasive variables.","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(16,8))\nsns.heatmap(sample_table.corr(), cmap=\"YlGnBu\", annot=True)","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:40.892577Z","iopub.execute_input":"2023-02-06T01:11:40.892941Z","iopub.status.idle":"2023-02-06T01:11:41.905888Z","shell.execute_reply.started":"2023-02-06T01:11:40.892908Z","shell.execute_reply":"2023-02-06T01:11:41.904693Z"},"_kg_hide-input":true,"trusted":true},"execution_count":121,"outputs":[{"execution_count":121,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"To complement this, the following bar plots show that as in previous plots, the biopsy does have a relation to cancer detection, as it is one of the most specific procedures to check if there is abnormal tissue.","metadata":{}},{"cell_type":"code","source":"sns.countplot(data=sample_table, x=\"cancer\", hue=\"biopsy\");","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:41.907621Z","iopub.execute_input":"2023-02-06T01:11:41.907955Z","iopub.status.idle":"2023-02-06T01:11:42.143722Z","shell.execute_reply.started":"2023-02-06T01:11:41.907925Z","shell.execute_reply":"2023-02-06T01:11:42.142419Z"},"_kg_hide-input":true,"trusted":true},"execution_count":122,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# Plotting of images and association\n\nFinally, a sample image was plotted with the pydicom library, where the final shape corresponds to 2776x2082 pixels.","metadata":{}},{"cell_type":"code","source":"# https://www.geeksforgeeks.org/view-dicom-images-using-pydicom-and-matplotlib/\nimport pydicom\nimport pydicom.data\nds = pydicom.dcmread(\"/kaggle/input/rsna-breast-cancer-detection/test_images/10008/1591370361.dcm\")\nplt.imshow(ds.pixel_array, cmap=plt.cm.bone)  # set the color map to bone\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:42.145427Z","iopub.execute_input":"2023-02-06T01:11:42.145834Z","iopub.status.idle":"2023-02-06T01:11:43.529224Z","shell.execute_reply.started":"2023-02-06T01:11:42.145801Z","shell.execute_reply":"2023-02-06T01:11:43.528398Z"},"_kg_hide-input":true,"trusted":true},"execution_count":123,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"ds.pixel_array.shape","metadata":{"execution":{"iopub.status.busy":"2023-02-06T01:11:43.530622Z","iopub.execute_input":"2023-02-06T01:11:43.531766Z","iopub.status.idle":"2023-02-06T01:11:43.5385Z","shell.execute_reply.started":"2023-02-06T01:11:43.531727Z","shell.execute_reply":"2023-02-06T01:11:43.537403Z"},"trusted":true},"execution_count":124,"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":"(2776, 2082)"},"metadata":{}}]},{"cell_type":"markdown","source":"# Final Insight\n\nThis initial approach helped to recognize trends related to the cancer diagnosis process and at the same time shows that there is a challenge in predicting the probability of cancer.\n\nThe following processes will be related to the prediction model construction, with the use of a GPU and with caution of the RAM memory and time limits. To complement this, the following bar plots show that as in previous plots, the biopsy does have a relation to cancer detection, as it is one of the most specific procedures to check if there is abnormal tissue.","metadata":{}}]}