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jb-apps/Exploring Population Metadata.ipynb

Created December 22, 2020 14:18
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Exploring Population Metadata.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from random import sample\n",
"\n",
"from itertools import chain\n",
"from random import sample \n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"d = pd.read_csv('findings_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Patient ID</th>\n",
" <th>Finding Labels</th>\n",
" <th>Patient Age</th>\n",
" <th>Patient Gender</th>\n",
" <th>Mass_Size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cardiomegaly|Emphysema</td>\n",
" <td>57</td>\n",
" <td>M</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>No Finding</td>\n",
" <td>77</td>\n",
" <td>M</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Atelectasis</td>\n",
" <td>79</td>\n",
" <td>M</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>Cardiomegaly|Edema|Effusion</td>\n",
" <td>55</td>\n",
" <td>F</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>Consolidation|Mass</td>\n",
" <td>68</td>\n",
" <td>M</td>\n",
" <td>2516.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Patient ID Finding Labels Patient Age \\\n",
"0 1 1 Cardiomegaly|Emphysema 57 \n",
"1 2 2 No Finding 77 \n",
"2 3 3 Atelectasis 79 \n",
"3 4 4 Cardiomegaly|Edema|Effusion 55 \n",
"4 5 5 Consolidation|Mass 68 \n",
"\n",
" Patient Gender Mass_Size \n",
"0 M NaN \n",
"1 M NaN \n",
"2 M NaN \n",
"3 F NaN \n",
"4 M 2516.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All Labels (14): ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis', 'Infiltration', 'Mass', 'No Finding', 'Nodule', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax']\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Patient ID</th>\n",
" <th>Finding Labels</th>\n",
" <th>Patient Age</th>\n",
" <th>Patient Gender</th>\n",
" <th>Mass_Size</th>\n",
" <th>Atelectasis</th>\n",
" <th>Cardiomegaly</th>\n",
" <th>Consolidation</th>\n",
" <th>Edema</th>\n",
" <th>Effusion</th>\n",
" <th>Emphysema</th>\n",
" <th>Fibrosis</th>\n",
" <th>Infiltration</th>\n",
" <th>Mass</th>\n",
" <th>No Finding</th>\n",
" <th>Nodule</th>\n",
" <th>Pleural_Thickening</th>\n",
" <th>Pneumonia</th>\n",
" <th>Pneumothorax</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>316</th>\n",
" <td>317</td>\n",
" <td>317</td>\n",
" <td>No Finding</td>\n",
" <td>13</td>\n",
" <td>M</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>No Finding</td>\n",
" <td>77</td>\n",
" <td>M</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>900</th>\n",
" <td>901</td>\n",
" <td>901</td>\n",
" <td>Infiltration</td>\n",
" <td>45</td>\n",
" <td>F</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Patient ID Finding Labels Patient Age Patient Gender \\\n",
"316 317 317 No Finding 13 M \n",
"1 2 2 No Finding 77 M \n",
"900 901 901 Infiltration 45 F \n",
"\n",
" Mass_Size Atelectasis Cardiomegaly Consolidation Edema Effusion \\\n",
"316 NaN 0.0 0.0 0.0 0.0 0.0 \n",
"1 NaN 0.0 0.0 0.0 0.0 0.0 \n",
"900 NaN 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Emphysema Fibrosis Infiltration Mass No Finding Nodule \\\n",
"316 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"900 0.0 0.0 1.0 0.0 0.0 0.0 \n",
"\n",
" Pleural_Thickening Pneumonia Pneumothorax \n",
"316 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"900 0.0 0.0 0.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Here I'm just going to split up my \"Finding Labels\" column so that I have one column in my dataframe\n",
"# per disease, with a binary flag. This makes EDA a lot easier! \n",
"\n",
"all_labels = np.unique(list(chain(*d['Finding Labels'].map(lambda x: x.split('|')).tolist())))\n",
"all_labels = [x for x in all_labels if len(x)>0]\n",
"print('All Labels ({}): {}'.format(len(all_labels), all_labels))\n",
"for c_label in all_labels:\n",
" if len(c_label)>1: # leave out empty labels\n",
" d[c_label] = d['Finding Labels'].map(lambda finding: 1.0 if c_label in finding else 0)\n",
"d.sample(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I see here that there are 14 unique types of labels found in my dataset"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Atelectasis 0.093093\n",
"Cardiomegaly 0.038038\n",
"Consolidation 0.043043\n",
"Edema 0.016016\n",
"Effusion 0.095095\n",
"Emphysema 0.018018\n",
"Fibrosis 0.027027\n",
"Infiltration 0.134134\n",
"Mass 0.035035\n",
"No Finding 0.575576\n",
"Nodule 0.041041\n",
"Pleural_Thickening 0.032032\n",
"Pneumonia 0.006006\n",
"Pneumothorax 0.033033\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d[all_labels].sum()/len(d)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0, 0.5, 'Number of Images with Label')]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = d[all_labels].sum().plot(kind='bar')\n",
"ax.set(ylabel = 'Number of Images with Label')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, I see the relative frequencies of each disease in my dataset. It looks like 'No Finding' is the most common occurrence. 'No Finding' can never appear with any other label by definition, so we know that in 57.5% of this dataset, there is no finding in the image. Beyond that, it appears that 'Infiltration' is the most common disease-related label, and it is followed by 'Effusion' and 'Atelectasis.'\n",
"\n",
"Since 'Infiltration' is the most common, I'm going to now look at how frequently it appears with all of the other diseases: "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f137b9c3410>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
"plt.figure(figsize=(16,6))\n",
"d[d.Infiltration==1]['Finding Labels'].value_counts()[0:30].plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like Infiltration actually occurs alone for the most part, and that its most-common comorbidities are Atelectasis and Effusion. \n",
"\n",
"Let's see if the same is true for another label, we'll try Effusion:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f137b968750>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
"plt.figure(figsize=(16,6))\n",
"d[d.Effusion==1]['Finding Labels'].value_counts()[0:30].plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same thing! Now let's move on to looking at age & gender: "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 29., 51., 60., 96., 154., 194., 229., 109., 58., 19.]),\n",
" array([ 6. , 14.1, 22.2, 30.3, 38.4, 46.5, 54.6, 62.7, 70.8, 78.9, 87. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.hist(d['Patient Age'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 8., 11., 14., 15., 18., 27., 30., 4., 4., 3.]),\n",
" array([11. , 18.6, 26.2, 33.8, 41.4, 49. , 56.6, 64.2, 71.8, 79.4, 87. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.hist(d[d.Infiltration==1]['Patient Age'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 2., 1., 6., 7., 9., 13., 31., 13., 7., 6.]),\n",
" array([11., 18., 25., 32., 39., 46., 53., 60., 67., 74., 81.]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.hist(d[d.Effusion==1]['Patient Age'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like the distribution of age across the whole population is slightly different than it is specifically for Infiltration and Effusion. Infiltration appears to be more skewed towards younger individuals, and Effusion spans the age range but has a large peak around 55. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1379632d50>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,6))\n",
"d['Patient Gender'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1379543810>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,6))\n",
"d[d.Infiltration ==1]['Patient Gender'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1379536550>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,6))\n",
"d[d.Effusion ==1]['Patient Gender'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gender distribution seems to be pretty equal in the whole population as well as with Infiltration, with a slight preference towards females in the Effusion distribution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Finally, let's look at if and how age & gender relate to mass size in individuals who have a mass as a finding:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f1377fd28d0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD4CAYAAAAKA1qZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAYw0lEQVR4nO3de4xU93nG8e9TQp2NUxccr13YxYVGhMaOG4hHhJYqcuy4ELcqG0upsOSaP6ISWUSxq4gGeo2lVKZyLo1bxRK51HYudmnjYOpriJ0obWSbDAEbMEYmhdq7UNg2pXEahDB5+8f8NkzWszuzs2eYc+Y8H2k0Z945Z+YdvJ53zu92FBGYmVk5/UK3EzAzs+5xETAzKzEXATOzEnMRMDMrMRcBM7MSe123E2jmoosuivnz53c7DTOzQtm5c+d/RUR/s/1yXwTmz59PtVrtdhpmZoUi6T9a2c/NQWZmJeYiYGZWYi4CZmYl5iJgZlZiLgJmZiWW+9FBZmZlsnXXCHc8foAjJ04yd1Yf61csYmjJQMfez0XAzCwntu4aYeMDezh5+gwAIydOsvGBPQAdKwRuDjIzy4k7Hj/wswIw5uTpM9zx+IGOvaeLgJlZThw5cXJK8Sy4CJiZ5cTcWX1TimfBRcDMLCfWr1hE38wZPxfrmzmD9SsWdew93TFsZpYTY52/Hh1kZlZSQ0sGOvqlP56bg8zMSqxpEZD0ekk7JD0raZ+k21L8Y5JGJO1Ot+vqjtko6aCkA5JW1MWvlLQnPXenJHXmY5mZWStaaQ46BVwdET+WNBP4N0mPpuc+HRGfqN9Z0mXAauByYC7wTUlviYgzwF3AWuBp4BFgJfAoZmbWFU3PBKLmx+nhzHSLSQ5ZBdwfEaci4hBwEFgqaQ5wQUQ8FREB3AsMTS99MzObjpb6BCTNkLQbOA5sj4hn0lMfkvScpC9Kmp1iA8DLdYcPp9hA2h4fb/R+ayVVJVVHR0en8HHMzGwqWioCEXEmIhYDg9R+1b+NWtPOm4HFwFHgk2n3Ru38MUm80fttjohKRFT6+5teItPMzNo0pdFBEXEC+DawMiKOpeLwU+BzwNK02zAwr+6wQeBIig82iJuZWZe0MjqoX9KstN0HvAd4IbXxj3kfsDdtbwNWSzpP0gJgIbAjIo4Cr0halkYF3QQ8mOFnMTOzKWpldNAc4B5JM6gVjS0R8ZCkL0laTK1J5zDwQYCI2CdpC/A88CqwLo0MArgZuBvoozYqyCODzMy6SLWBOvlVqVSiWq12Ow0zs0KRtDMiKs3284xhM7MScxEwMysxFwEzsxJzETAzKzEXATOzEnMRMDMrMRcBM7MScxEwMysxFwEzsxJzETAzKzEXATOzEnMRMDMrMRcBM7MScxEwMysxFwEzsxJr5aIyZma5snXXCHc8foAjJ04yd1Yf61csYmjJQLfTKiQXATMrlK27Rtj4wB5Onq5dsHDkxEk2PrAHwIWgDW4OMrNCuePxAz8rAGNOnj7DHY8f6FJGxeYiYGaFcuTEySnFbXIuAmZWKHNn9U0pbpNzETCzQlm/YhF9M2f8XKxv5gzWr1jUpYyKrWkRkPR6STskPStpn6TbUvxCSdslvZjuZ9cds1HSQUkHJK2oi18paU967k5J6szHMrNeNbRkgNuvv4KBWX0IGJjVx+3XX+FO4TYpIibfofZFfX5E/FjSTODfgFuA64EfRsQmSRuA2RHxUUmXAfcBS4G5wDeBt0TEGUk70rFPA48Ad0bEo5O9f6VSiWq1Or1PaWZWMpJ2RkSl2X5Nh4hGrUr8OD2cmW4BrAKuSvF7gG8DH03x+yPiFHBI0kFgqaTDwAUR8VRK8F5gCJi0CJhZ9jzO3sa01CcgaYak3cBxYHtEPANcEhFHAdL9xWn3AeDlusOHU2wgbY+PN3q/tZKqkqqjo6NT+Txm1sTYOPuREycJzo6z37prpNupWRe0VAQi4kxELAYGqf2qf9skuzdq549J4o3eb3NEVCKi0t/f30qKZtYij7O3elMaHRQRJ6g1+6wEjkmaA5Duj6fdhoF5dYcNAkdSfLBB3MzOIY+zt3qtjA7qlzQrbfcB7wFeALYBa9Jua4AH0/Y2YLWk8yQtABYCO1KT0SuSlqXO5pvqjjGzc8Tj7K1eK2cCc4BvSXoO+B61PoGHgE3AtZJeBK5Nj4mIfcAW4HngMWBdRIyde94MfB44CPwAdwqbnXMeZ2/1mg4R7TYPETXLnkcH9b7MhoiaWe8ZWjLgL30DvGyEmVmp+UzArAe4ecfa5SJgVnCdusiKC0s5uDnIrOA6MfnLs4rLw0XArOA6MfnLs4rLw0XArOA6MfnLs4rLw0XArOA6MfnLs4rLw0XArOA6cZEVzyouD48OMusBWU/+Gnstjw7qfS4CZtaQZxWXg5uDzMxKzEXAzKzEXATMzErMRcDMrMRcBMzMSsxFwMysxFwEzMxKzPMEzKxtXm66+FwEzKwtnbqOgZ1bvtC8mbVl+aYnGZlkVdEZEje8cx4fH7riHGZlY1q90HzTPgFJ8yR9S9J+Sfsk3ZLiH5M0Iml3ul1Xd8xGSQclHZC0oi5+paQ96bk7JandD2hm3dVsWekzEXz56Zf48617zlFG1o5WOoZfBT4SEW8FlgHrJF2Wnvt0RCxOt0cA0nOrgcuBlcBnJY0tR3gXsBZYmG4rs/soZnYutbqs9H3PvNzhTGw6mhaBiDgaEd9P268A+4HJGvxWAfdHxKmIOAQcBJZKmgNcEBFPRa0N6l5gaNqfwMy6otFy042cyXmTc9lNaYiopPnAEuCZFPqQpOckfVHS7BQbAOpL/3CKDaTt8fFG77NWUlVSdXR0dCopmvWcrbtGWL7pSRZseJjlm57MzXV+x1/HYCIz3Oqbay0XAUlvBL4G3BoRP6LWtPNmYDFwFPjk2K4NDo9J4q8NRmyOiEpEVPr7+1tN0azn5P2C70NLBvjuhqs5tOl3uXHZpQ33ueGd885xVjYVLRUBSTOpFYCvRMQDABFxLCLORMRPgc8BS9Puw0D9f/VB4EiKDzaIm9kEinTB948PXcGNyy792S//GRI3LrvUo4Nyruk8gTSC5wvA/oj4VF18TkQcTQ/fB+xN29uAr0r6FDCXWgfwjog4I+kVScuoNSfdBPxddh/FrLO6MTGqaBd8//jQFf7SL5hWJostB/4Q2CNpd4r9KXCDpMXUmnQOAx8EiIh9krYAz1MbWbQuIsZ+ytwM3A30AY+mm1nudWti1NxZfQ3H4vuC75YVTxYza8FEE6MGZvXx3Q1Xd+x9xxcfqF3wfboXkrfe1+pkMS8bYdaCbjXL+ILv1mkuAmYt6GazjC/4bp3kpaTNWtBoYlTfzBmsX7GoSxmZZcNnAmYtcLOM9SoXAbMWuVnGepGbg8zMSsxFwMysxFwEzMxKzEXAzKzEXATMzErMRcDMrMRcBMzMSszzBMysbd1YXtuy5SJglqEyfSl2a3lty5abg8wykvdLQWatSFc9s4m5CJhlpGxfikW76pk15iJglpGyfSlOtIy2r3pWLC4CZhkp25eil9fuDS4CZhkp25fi0JIBbr/+CgZm9SFql9r0ZS+Lx6ODzDJSxmsOeHnt4mtaBCTNA+4FfgX4KbA5Ij4j6ULgH4H5wGHgDyLif9IxG4EPAGeAD0fE4yl+JXA30Ac8AtwSeb/SvdkU+EvRiqaV5qBXgY9ExFuBZcA6SZcBG4AnImIh8ER6THpuNXA5sBL4rKSxc+S7gLXAwnRbmeFnMcu9rbtGWL7pSRZseJjlm57s2eGjVhxNi0BEHI2I76ftV4D9wACwCrgn7XYPMJS2VwH3R8SpiDgEHASWSpoDXBART6Vf//fWHWPW88o2j8CKYUodw5LmA0uAZ4BLIuIo1AoFcHHabQB4ue6w4RQbSNvj443eZ62kqqTq6OjoVFI0y62yzSOwYmi5CEh6I/A14NaI+NFkuzaIxSTx1wYjNkdEJSIq/f39raZolmtlm0dgxdBSEZA0k1oB+EpEPJDCx1ITD+n+eIoPA/PqDh8EjqT4YIO4WSmUbR6BFUPTIiBJwBeA/RHxqbqntgFr0vYa4MG6+GpJ50laQK0DeEdqMnpF0rL0mjfVHWPW88o2j8CKoZV5AsuBPwT2SNqdYn8KbAK2SPoA8BLwfoCI2CdpC/A8tZFF6yJirCH0Zs4OEX003cxKoYzzCCz/lPdh+pVKJarVarfTMLMGyrR0dtFI2hkRlWb7ecawmbXF1xPoDV47yMza4iGvvcFFwMza4iGvvcFFwMza4iGvvcFFwMza4iGvvcEdw2YFk5cROR7y2htcBMwKJG8jcrx0dvG5OcisQDwix7LmImBWIB6RY1lzETArkF/umzmluFkzLgJmBaJGC7JPEjdrxkXArEBO/OT0lOJmzbgImBWIJ2hZ1lwEzArEE7Qsa54nYFYgnqBlWXMRMCsYT9CyLLkImFlL8rJchWXLRcDMmsrbchWWHXcMm1lTXq6id7kImFlTXq6id7kImFlTnp/Qu5oWAUlflHRc0t662MckjUjanW7X1T23UdJBSQckraiLXylpT3ruTskT3c2KwvMTelcrHcN3A38P3Dsu/umI+ER9QNJlwGrgcmAu8E1Jb4mIM8BdwFrgaeARYCXw6LSyN2uTR7pMjecn9K6mRSAiviNpfouvtwq4PyJOAYckHQSWSjoMXBARTwFIuhcYwkXAusAjXdrj+Qm9aTp9Ah+S9FxqLpqdYgPAy3X7DKfYQNoeH29I0lpJVUnV0dHRaaRo9loe6WJ2VrtF4C7gzcBi4CjwyRRv1M4fk8QbiojNEVGJiEp/f3+bKZo15pEuZme1NVksIo6NbUv6HPBQejgMzKvbdRA4kuKDDeJmTWXdfj93Vh8jDb7wPdLFyqitMwFJc+oevg8YGzm0DVgt6TxJC4CFwI6IOAq8ImlZGhV0E/DgNPK2khhrvx85cZLgbPv91l0jbb+mR7qYndX0TEDSfcBVwEWShoG/Aq6StJhak85h4IMAEbFP0hbgeeBVYF0aGQRwM7WRRn3UOoTdKWxNTdZ+3+7ZgEe6mJ2liAmb5nOhUqlEtVrtdhrWJQs2PNyw80jAoU2/e67TMSsMSTsjotJsP88YtlzzTFWzznIRsFxz+71ZZ3kpacs1t9+3xjOgrV0uApZ7nqk6Oc+AtulwETCj2L+kOzGCysrDRcBKr+i/pD0D2qbDRcBKb6Jf0rf9y75CnB14BrRNh0cHWelN9Iv5f35yOtOZyp3iEVQ2HS4CVnqt/mLO60qjQ0sGuP36KxiY1YeAgVl93H79Fbk8a7H8cXOQld76FYt+rk9gMnltZ/cIKmuXi4CVXqO5CP936lVOnDz9mn3dzm69xkXAjNf+kh4/Ygjczm69yUXArIFWZioXeW6B2RgXAbMJTNbOXvS5BWZjPDrIrA2+TrH1ChcBszZ4lq71CjcHmbWh6LN03Z9hY3wmYNaGbs7S3bprhOWbnmTBhodZvunJKc9i7sR1m624XATM2tCtWbpZfIG7P8PquTnIrE3dmKWbxbLR7s+wek3PBCR9UdJxSXvrYhdK2i7pxXQ/u+65jZIOSjogaUVd/EpJe9Jzd0pS9h/HrLdl8QXu6zZbvVaag+4GVo6LbQCeiIiFwBPpMZIuA1YDl6djPitprOH0LmAtsDDdxr+mmTWRxRe4Vx21ek2LQER8B/jhuPAq4J60fQ8wVBe/PyJORcQh4CCwVNIc4IKIeCoiAri37hizQphuh2wWsvgC96qjVq/dPoFLIuIoQEQclXRxig8AT9ftN5xip9P2+LhZIeRlhnAry1m0+jr+0jfIvmO4UTt/TBJv/CLSWmpNR1x66aXZZGY2DXm6jq+/wC1L7Q4RPZaaeEj3x1N8GJhXt98gcCTFBxvEG4qIzRFRiYhKf39/mymaZccjaqxXtVsEtgFr0vYa4MG6+GpJ50laQK0DeEdqOnpF0rI0KuimumPMcs8jaqxXtTJE9D7gKWCRpGFJHwA2AddKehG4Nj0mIvYBW4DngceAdRExdg59M/B5ap3FPwAezfizmHVMp0bU5KGz2cpNtcE6+VWpVKJarXY7DWtBr69Hk/Xnm+jCNR6pY1mQtDMiKs3284xhy0QnR8/kpbhk3SGbp85mKy+vHWSZ6NR6NL282Jk7my0PXAQsE536Quvlxc7c2Wx54CJgmejUF1ov/1r28g2WBy4ClolOfaFlVVzyOArHyzdYHrhj2DKR1XIG461fsajhCJqpFJe8LPnQiGf/Wre5CFhmOvGFlkVx8Sgcs4m5CFjuTbe49HK/gtl0uQhYz8vTReHzMufBbIw7hq3n5WUUTi/PebDichGwnpeXUTi9POfBisvNQVYKeRiF474JyyOfCZidI54hbHnkImB2jrTbN5HHiW7WO9wcZHaOtDPnIc8T3aw3uAiYnUNT7ZvwRDfrNBcBswxlPQ/AncnWae4TMMtIJ+YBuDPZOs1FwCwjnZgHkJeJbta73BxklpFONN10anVWszEuAmYZ6dQaRXmY6Ga9a1rNQZIOS9ojabekaopdKGm7pBfT/ey6/TdKOijpgKQV003eLE/cdGNFlEWfwLsjYnFEVNLjDcATEbEQeCI9RtJlwGrgcmAl8FlJMxq9oFkR5WWNIrOp6ERz0CrgqrR9D/Bt4KMpfn9EnAIOSToILAWe6kAOZl3hphsrmumeCQTwDUk7Ja1NsUsi4ihAur84xQeAl+uOHU6x15C0VlJVUnV0dHSaKZqZ2USmeyawPCKOSLoY2C7phUn2VYNYNNoxIjYDmwEqlUrDfczMbPqmdSYQEUfS/XHg69Sad45JmgOQ7o+n3YeBeXWHDwJHpvP+ZmY2PW0XAUnnS/qlsW3gd4C9wDZgTdptDfBg2t4GrJZ0nqQFwEJgR7vvb2Zm0zed5qBLgK9LGnudr0bEY5K+B2yR9AHgJeD9ABGxT9IW4HngVWBdRJxp/NJmZnYutF0EIuLfgbc3iP83cM0Ex/w18NftvqeZmWXLM4YnkPVqkGZmeeQi0IAv5GFmZdGTRWC6v+J9IQ8zK4ueKwJZ/Ir3hTzMrCx67noCWazp7gt5mFlZ9FwRyOJXvFeDNLOy6LkikMWveK8GaWZl0XN9AutXLPq5PgFo71e8V4M0szLouSLgy/GZmbWu54oA+Fe8mVmreq5PwMzMWuciYGZWYi4CZmYl5iJgZlZiLgJmZiWmiHxfwlfSKPAfXUzhIuC/uvj+U+FcO8O5Zq8oeUJxc/3ViOhvdkDui0C3SapGRKXbebTCuXaGc81eUfKE3s/VzUFmZiXmImBmVmIuAs1t7nYCU+BcO8O5Zq8oeUKP5+o+ATOzEvOZgJlZibkImJmVmItAImmepG9J2i9pn6RbUvxCSdslvZjuZ+cg19dL2iHp2ZTrbXnNdYykGZJ2SXooPc5lrpIOS9ojabekaorlNddZkv5Z0gvp7/Y385irpEXp33Ps9iNJt+YxVwBJf5z+v9or6b70/1vucpV0S8pxn6RbU2zKeboInPUq8JGIeCuwDFgn6TJgA/BERCwEnkiPu+0UcHVEvB1YDKyUtIx85jrmFmB/3eM85/ruiFhcN946r7l+BngsIn4deDu1f9/c5RoRB9K/52LgSuAnwNfJYa6SBoAPA5WIeBswA1hNznKV9Dbgj4Cl1P7b/56khbSTZ0T41uAGPAhcCxwA5qTYHOBAt3Mbl+cbgO8D78xrrsBg+oO8GngoxfKa62HgonGx3OUKXAAcIg3uyHOu4/L7HeC7ec0VGABeBi6kdr2Vh1LOucoVeD/w+brHfwH8STt5+kygAUnzgSXAM8AlEXEUIN1f3L3MzkrNK7uB48D2iMhtrsDfUvsD/WldLK+5BvANSTslrU2xPOb6a8Ao8A+pme3zks4nn7nWWw3cl7Zzl2tEjACfAF4CjgL/GxHfIH+57gXeJelNkt4AXAfMo408XQTGkfRG4GvArRHxo27nM5GIOBO10+tBYGk6PcwdSb8HHI+Ind3OpUXLI+IdwHupNQm+q9sJTeB1wDuAuyJiCfB/5KA5ZTKSfhH4feCfup3LRFIb+ipgATAXOF/Sjd3N6rUiYj/wN8B24DHgWWpN2lPmIlBH0kxqBeArEfFACh+TNCc9P4faL+/ciIgTwLeBleQz1+XA70s6DNwPXC3py+QzVyLiSLo/Tq3dein5zHUYGE5ngAD/TK0o5DHXMe8Fvh8Rx9LjPOb6HuBQRIxGxGngAeC3yGGuEfGFiHhHRLwL+CHwIm3k6SKQSBLwBWB/RHyq7qltwJq0vYZaX0FXSeqXNCtt91H7w32BHOYaERsjYjAi5lNrCngyIm4kh7lKOl/SL41tU2sL3ksOc42I/wRelrQoha4BnieHuda5gbNNQZDPXF8Clkl6Q/pOuIZah3vucpV0cbq/FLie2r/t1PPsdkdMXm7Ab1NrD34O2J1u1wFvotap+WK6vzAHuf4GsCvluhf4yxTPXa7j8r6Ksx3DucuVWjv7s+m2D/izvOaa8loMVNPfwVZgdo5zfQPw38Av18Xymutt1H5U7QW+BJyXx1yBf6VW+J8Frmn339TLRpiZlZibg8zMSsxFwMysxFwEzMxKzEXAzKzEXATMzErMRcDMrMRcBMzMSuz/AehFpRI3TlIiAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(d['Patient Age'],d['Mass_Size'])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.7275663300043572, 7.354553889321959e-07)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mass_sizes = d['Mass_Size'].values\n",
"mass_inds = np.where(~np.isnan(mass_sizes))\n",
"ages = d.iloc[mass_inds]['Patient Age']\n",
"mass_sizes=mass_sizes[mass_inds]\n",
"scipy.stats.pearsonr(mass_sizes,ages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above tells us that age and mass size are significantly correlated, with a Pearson's coerrelation coefficient of 0.727"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1735.7"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(d[d['Patient Gender']== 'M']['Mass_Size'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1550.8"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(d[d['Patient Gender']== 'F']['Mass_Size'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=-0.6188395721019645, pvalue=0.5402707532656862)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.ttest_ind(d[d['Patient Gender']== 'F']['Mass_Size'],d[d['Patient Gender']== 'M']['Mass_Size'],nan_policy='omit')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above tells us that there is no statistically significant difference between mass size with gender. "
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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