{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Observations and Insights " ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Patient IDTime Passed (Days)AntibodyLevel (U/mL)VaccinationGenderAgeWeight (Pounds)
0QwMNj01678.40ModernaMale3499
1szsYJ01490.84J&JFemale17334
2vN44A01111.66J&JMale67150
3u4lQ_0262.43J&JFemale51342
4uPC6L0545.38J&JMale37138
\n", "
" ], "text/plain": [ " Patient ID Time Passed (Days) AntibodyLevel (U/mL) Vaccination Gender \\\n", "0 QwMNj 0 1678.40 Moderna Male \n", "1 szsYJ 0 1490.84 J&J Female \n", "2 vN44A 0 1111.66 J&J Male \n", "3 u4lQ_ 0 262.43 J&J Female \n", "4 uPC6L 0 545.38 J&J Male \n", "\n", " Age Weight (Pounds) \n", "0 34 99 \n", "1 17 334 \n", "2 67 150 \n", "3 51 342 \n", "4 37 138 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dependencies and Setup\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import scipy.stats as st\n", "\n", "# Study data files\n", "vaccination_metadata = \"data/vaccination_metadata.csv\"\n", "vaccination_study_path = \"data/vaccination_study_results.csv\"\n", "\n", "# Read the mouse data and the study results\n", "vaccination_data = pd.read_csv(vaccination_metadata)\n", "study_results = pd.read_csv(vaccination_study_path)\n", "\n", "# Combine the data into a single dataset\n", "\n", "# Display the data table for preview\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "249" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the number of patients.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['RS7EK'], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting the duplicate patients by ID number that shows up Patient ID and the Time Passed. \n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Patient IDTime Passed (Days)AntibodyLevel (U/mL)VaccinationGenderAgeWeight (Pounds)
107RS7EK0906.41J&JFemale4977
137RS7EK0400.95J&JFemale4977
329RS7EK5158.23J&JFemale4977
360RS7EK5510.04J&JFemale4977
620RS7EK1071.97J&JFemale4977
681RS7EK1065.69J&JFemale4977
815RS7EK15762.45J&JFemale4977
869RS7EK15801.72J&JFemale4977
950RS7EK20418.17J&JFemale4977
1111RS7EK20105.74J&JFemale4977
1195RS7EK25667.95J&JFemale4977
1380RS7EK301089.58J&JFemale4977
1592RS7EK351158.20J&JFemale4977
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" ], "text/plain": [ " Patient ID Time Passed (Days) AntibodyLevel (U/mL) Vaccination Gender \\\n", "107 RS7EK 0 906.41 J&J Female \n", "137 RS7EK 0 400.95 J&J Female \n", "329 RS7EK 5 158.23 J&J Female \n", "360 RS7EK 5 510.04 J&J Female \n", "620 RS7EK 10 71.97 J&J Female \n", "681 RS7EK 10 65.69 J&J Female \n", "815 RS7EK 15 762.45 J&J Female \n", "869 RS7EK 15 801.72 J&J Female \n", "950 RS7EK 20 418.17 J&J Female \n", "1111 RS7EK 20 105.74 J&J Female \n", "1195 RS7EK 25 667.95 J&J Female \n", "1380 RS7EK 30 1089.58 J&J Female \n", "1592 RS7EK 35 1158.20 J&J Female \n", "\n", " Age Weight (Pounds) \n", "107 49 77 \n", "137 49 77 \n", "329 49 77 \n", "360 49 77 \n", "620 49 77 \n", "681 49 77 \n", "815 49 77 \n", "869 49 77 \n", "950 49 77 \n", "1111 49 77 \n", "1195 49 77 \n", "1380 49 77 \n", "1592 49 77 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Optional: Get all the data for the duplicate mouse ID. \n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Patient IDTime Passed (Days)AntibodyLevel (U/mL)VaccinationGenderAgeWeight (Pounds)
0QwMNj01678.40ModernaMale3499
1szsYJ01490.84J&JFemale17334
2vN44A01111.66J&JMale67150
3u4lQ_0262.43J&JFemale51342
4uPC6L0545.38J&JMale37138
\n", "
" ], "text/plain": [ " Patient ID Time Passed (Days) AntibodyLevel (U/mL) Vaccination Gender \\\n", "0 QwMNj 0 1678.40 Moderna Male \n", "1 szsYJ 0 1490.84 J&J Female \n", "2 vN44A 0 1111.66 J&J Male \n", "3 u4lQ_ 0 262.43 J&J Female \n", "4 uPC6L 0 545.38 J&J Male \n", "\n", " Age Weight (Pounds) \n", "0 34 99 \n", "1 17 334 \n", "2 67 150 \n", "3 51 342 \n", "4 37 138 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a clean DataFrame by dropping the duplicate patient by its ID.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "248" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the number of patients in the clean DataFrame.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary Statistics" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Mean Antibody LevelMedian Antibody LevelAntibody Level VarianceAntibody Level Std. Dev.Antibody Level Std. Err.
Vaccination
J&J472.729903372.620164236.778866405.26137117.806162
Moderna477.716571364.085176750.666784420.41725317.279043
None533.828871379.815234784.611298484.54577835.528620
Pfizer495.083510370.710193936.200663440.38188018.223132
\n", "
" ], "text/plain": [ " Mean Antibody Level Median Antibody Level \\\n", "Vaccination \n", "J&J 472.729903 372.620 \n", "Moderna 477.716571 364.085 \n", "None 533.828871 379.815 \n", "Pfizer 495.083510 370.710 \n", "\n", " Antibody Level Variance Antibody Level Std. Dev. \\\n", "Vaccination \n", "J&J 164236.778866 405.261371 \n", "Moderna 176750.666784 420.417253 \n", "None 234784.611298 484.545778 \n", "Pfizer 193936.200663 440.381880 \n", "\n", " Antibody Level Std. Err. \n", "Vaccination \n", "J&J 17.806162 \n", "Moderna 17.279043 \n", "None 35.528620 \n", "Pfizer 18.223132 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the antibody count for each vaccine\n", "\n", "# Use groupby and summary statistical methods to calculate the following properties of each vaccine: \n", "# mean, median, variance, standard deviation, and SEM of the antibody count. \n", "# Assemble the resulting series into a single summary dataframe.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AntibodyLevel (U/mL)
meanmedianvarstdsem
Vaccination
J&J472.729903372.620164236.778866405.26137117.806162
Moderna477.716571364.085176750.666784420.41725317.279043
None533.828871379.815234784.611298484.54577835.528620
Pfizer495.083510370.710193936.200663440.38188018.223132
\n", "
" ], "text/plain": [ " AntibodyLevel (U/mL) \\\n", " mean median var std \n", "Vaccination \n", "J&J 472.729903 372.620 164236.778866 405.261371 \n", "Moderna 477.716571 364.085 176750.666784 420.417253 \n", "None 533.828871 379.815 234784.611298 484.545778 \n", "Pfizer 495.083510 370.710 193936.200663 440.381880 \n", "\n", " \n", " sem \n", "Vaccination \n", "J&J 17.806162 \n", "Moderna 17.279043 \n", "None 35.528620 \n", "Pfizer 18.223132 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the antibody count for each vaccination\n", "\n", "# Using the aggregation method, produce the same summary statistics in a single line\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar and Pie Charts" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a bar plot showing the total number of timepoints for all patient tested for each vaccine using Pandas.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a bar plot showing the total number of timepoints for all mice tested for each vaccination using pyplot.\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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z87AZtQcPUnb6fTtW5wU/WpkfrFqZH2xalxfI2+YP9KvxyZFGpPRwn09HGnc0Uv9hPX3Gdt47lfMrKT7e6dX+H+/Pxoc2Uvl6JUO+OITdL++m/+n98eVnZBX0lC48KyIGuMcYc2Py9k1AX2NM5HADiEgCWIGz19IMPAbca4xpEZEpwNd6ujafiDwKPGuM+cvh5uvk+8eAKcaYnZ3dJ9WR1xRgonHj5bU9LEBz04mydu1M/6IdZ/pW+MbIlqFBmkeLZG7t9NJEy6DSRP2g0+rrD/patcjeD4J5W1YGg5Ur84P1a4J5/i2BQHGVzzcoAUci0uP2SNQn2PDABoZ8ZQj+Ph0v7Vb9XjWV8ysZ/T1n7QB/oZ/QDSFn+5oEO6I7GHntSDbP3kyiNkHpJ0spHFfY0yipOvgH07EG4PMicmdX/2EPUZ0x5gQAESkDfg+UALcaYxYCC3v58TIi5XMbcY7z2prGLKnI6cunh2TrxnN8Szae41vcdKwvNrCY2nEiHAMcYztbR/oaUzypofHoSQ2NcMDOUyM0xPLytryXH9y5MhisXR3MY0NeoLDS7y9tgmGIHDTPZpoNGx/YSP+p/SmZ0vGq5PUb69k8ezOhG0ME+h788t7+9HbKzisj/macPqE+lEwtYcN9GxgdTtsiKQcuaNCZZuARnDfH2p3JIiKjgNk47/DvAL5hjNmQHP1U4QwuhgA3dzcSMsZsF5ErgbdFJIJz9fmbjDGfEZFPAPe13hWYZozZKyI3A5fi/P/7uzEmfEC+c3COCQ0AbwNXGWMaRKQCOD/53F4wxtwkIoOAh4CRyc2vN8a8LiIDcU49HAQsAKS7H1iq5VUKrBSRBTi/IVp/EJk+wn4nOXK6RQnVe6b5ln8w079o78m+VUWDqRztEzMCGNHtxi4QhPyjmppGH9XUNPoCOpxn29x2nm19wJ+/4OGtE4ODgwWlnyzt8A2Exl2NbPjFBkZcOYL8IfkHfb3howaa9jRRNKGIug11+ILOwK+lKa2/E1MtL4BfAstF5KcHfP4B4HFjzGMicjlwP/DZ5NeOBM7Amb+cC3S7G2eMWSfOqLfsgC/dBFyTLJO+QL2IfCr5WKcaY2pFZEDbDUSkAHgUOMcYs1pEHgeuSv75OWCCMcaISP/kJvcBPzfGvCYiI4HncX753gq8Zoy5XUTKgSu7ex6pllckxful22Y8WF55NDeeJKvXzvQv2nmmb4V/tHw0NI/mkEhuroHe0TzbaxuaOXNRLceV+Wi+eU+i1kjTrHNL1q2Mm7zdAb+v5DNlte8/sW18c3VzwZbHtzjfyA/jIuP2fd9tT2xj8IWDAWce7MP7P2TXC7so+9yB/4d7VWWqdzTGVCX/018HtJ1gnIpzlgvAHKBtuf3VGNOCM7gY3INcHY1sXgfuEZH/BZ40xmwSkRnAb5PnN2OM2X3ANkcD640xq5O3HwOuwSnceuA3IhIFnk1+fQYwUWTfw/cT502faa3P0RgTFZFuf26pnpj9anLoOt4Y8w8RKcTOWvKbLTxmrxsjWz6c4Vu86Wz/kuaPSay0L3XjRJhoO1c2O2NkAHNrv9abfuejOfkzS8CGTTATqmcVt59na2jYN8828uoR++bZAv0CjL1lbCai7+jh/e8FFgO/7eI+beee2x6y1O2uFkDygPMEsJ02Uw7GmIpk0XwaeDNZXELXa/d1+JjGmGYROQU4B7gY+HfgbJwD46caY9q9+5Mssx7Nqaf6buM3cYZxA3DedRyGs996Tk8erBdsyvDjHbb+7K2c7lv2wQz/ouopvtV9y9gzxidmFDDKdjYvSmWe7f384M53U5xn6wU9mnw3xuwWkT8B/4YzzwXO2nkX44y6vgq8dqhh2sw5PZDcnWv7tbHGmBU4VwqbirMr+gLwAxH5fetu4wGjr/eBkIiMM8asxZkbezW521lojPmbiLwJrE3e/wWcIrsr+ZgnJFdmnp98bj9M7qoe0d1zSXW38RqcZaDfAjDGrEm+a5FpWT3yCtLUMMW3au1M36Jdp/ve8Yfko+FBSYzCmVBVlrWdZzu/m3m294PBRCwvULDj8I9nO5TX7M9w/oO3ug6YLSL/SXLCvoffr4+ILGX/oRJzgHs6uN/1InIWzqhsJc7kfIOInAAsFJFG4G/Ad1s3MMbUi8g3gD+LSOuE/UM4A52nk3NigvNGROtz+aWILMfpn/nAt4HbgD+IyGKci+xs6O5JpXSEvYi8ZYw5VUSWGGNOTIZcbIw5vtuNe1EoHL0E5wefFcbLptg5vsWbz/YvaZ4oHw4qon6cCNaPRle9b6fft3N1XnDre87xbM0f5AUCKR7PNmPF11e8lLGgOSTVkderIvJdnAafCVwNPJO+WJ2yNvIaQHzXdN+ydTP8i2um+Fb3LWXPWJ8QwjnrQHlcaaKltDRRX9rV8WzvBYOV7+YHG9YE83xbAoG+VT5fWUJkvYW4OSHVkZcPZx98VvJTzxtjfpPOYB0JhaNHAWm/bmQ+jfUn+1atmelbWHm6793AKNk2PE8SI7vfUql2moBCIvFm20G8qLuVVC8Ahhtjfgn8OjlxPwiYLCJ70nVqQBfSMGFvzNGyMTbDt3jzWf6lLRNkQ+vu33G9/1gqx6zT4kqfLkdeIvI6cLExZmPy9lKctzv74hz7kel3GwmFo+s5jF21gcR3nuVfum6mb1HtSb41xaXEx4nQ8eHaSh2ep4nEP2s7hFd1N+cVbC2upNeSb5PuFpGizjZKs8WkWF4FNNSd4nt/zSzfwsrTfCvzRsj2kXmSGI5zxoBS6fa+7QBe1l15tTvWwhjT9u1bWyupLmb/0cZtGHOMbFg307do63T/0pajZWNZIQ3jRMjoO6JKtfG27QBe1l15vSUi3zTG/LrtJ0XkWzgnT9qwGGAwu7dP9y+NzfAtrj3Rt7bfQKrGiTAW5yBapbKBjQsz54zu5rzKgL/inIKwOPnpyUA+8FljzLZ0BzzQg7dcUvpNf3RJQFoOaX0qpTJkPZH4mO7vpg5VqodKnI1zxWyAd40xL6c1VXciJauB8VYzKNW13xGJX2o7hJelemL2y4DdwmrvDbS8VHbTXcY0y8g6uGnwuu0ASnVDX6Np5tbyetF2AKW6UIWz+rBKI3eWVyQeA5bbjqFUJ+YRief0kuWZ4M7ycjxtO4BSnXjSdoBc4Obymms7gFIdaEJfmxnh5vJaRJYvTqhy0jwi8ZTXrVeHzr3lFYkb9Decyj5P2A6QK9xbXg6d91LZpAXnjBSVAW4vr1fo4QUOlEqj14nEM37KXK5yd3lF4o04F7xUKhv8n+0AucTd5eV4mB5e702pNNgLPG47RC5xf3lF4msBvTqLsm0Okfje7u+meov7y8vxkO0AKuc9YDtArvFKeT0NbLUdQuWsl4jE37MdItd4o7ycK7T8j+0YKmfpqMsCb5SX4yGcFV+VyqQPsXMB5pznnfKKxDcDj9iOoXLO/UTiCdshcpF3ysvxY6DOdgiVMzYDv7IdIld5q7wi8Y/QF5PKnNuJxOtth8hV3iovx0+AatshlOetAWbbDpHLvFdekfgO4Be2YyjP+37yXW5liffKy3E3zjriSqXDEuBPtkPkOm+WVyS+G7jNdgzlWd9LrienLPJmeTnuw/kNqVRvepZI/O+2Qygvl5dz7M2VOAvEKdUbqoCrbIdQDu+WF0AkvhA9dUP1njCR+CbbIZTD2+XluAXQF5w6XPPR1UuyivfLy1lj6TrbMTIt0WI48eFqPvP72nafv/uNBuS2KnbWdrw3Hbp3L8c9WM0JD1Uz5ZH9h8v914v1HP9gNV97av8JDHOWNXLfmzlxOmk98E2dpM8u3i8vgEj8KeAvtmNk0n1vNXJMaft/3o3xFl5c18zIEuly21e+XsjSb/dl4ZV9AYjXG97YlGD5VX1JGMOKbQnqmgyPLmvi6pODaXsOWeR2IvHVtkOo9nKjvBxXAOtsh8iETVUtRNc0c8VJ7YvlO8/X89MZBXRdXQfzCTQmDMYY6pogzw93vdHIdacEyfP39Lu5zgLgLtsh1MFyp7wi8TjwRXJg2Zzrn3NKytemV+auamJYsY9JQ/xdbisCs+bUMvmRah5Z1AhAcb5w4TF5nPhwDaP7+yjJF97ekuCCCXnpfBrZYCdwkR5Jn50CtgNkVCS+iEjJDcAvbUdJl2dXN1FWJEwe6mdezPk/V9tk+NE/G3jhkqJut3/98iKGFvvYXtPCzDm1TCj1MW1UgJtPz+fm0/MBuGJuHbdPz+c3ixt54YNmjh/s55Zp+Wl9Xha0AF8hEt9oO4jqWO6MvFpF4r/Cw6d2vL4hwdxVzYTu3cvFf6nj5fXNXPpUHesrDZMeqiZ07142VRlOeriGj6oPnrQfWuy8JMqKfHxuQoAFm9svVbVkq3P7qIE+Hl/WxJ++UMg72xOs2eW5Ja1uJRJ/0XYI1bncGnntdwVwIjDedpDedueMAu6cUQDAvFgzd7/RyBNfLGx3n9C9e1l4ZRGlhe1/d9U0GlqMs5tY02h44YMEP/hE+xHV919p4JHzCmhqgUTyvTefQG1T+p6TBc8CP7IdQnUt90Ze0Hr4xEXo0jls2dvCp//XOZxiW43hjN/WMOmhak75TQ3l4wN8ctz+329/fb+Jk4f6GVrso3+BMHW4n+MerEaEbufSXGQdcKkeFpH9xJgc/jeKlMwEooDnZ55VSqqAaUTiy2wHUd3LzZFXK2dO4zL0itvKORD1PC0u98jt8gKIxH8P3Gg7hrKqGfgikfh820FU6rS8ACLxn+MsYKhyjwEuJxLXy5e5jJbXfjcDv7MdQmXc9UTic2yHUD2n5dXKeXfpG8AfbEdRGXM7kfj9tkOoQ6Pl1ZZzGsglwIO2o6i0u41I/FbbIdShy+1DJboSKfkh8D3bMVSvMzi7ijricjktr65ESm5EJ/K9pAm4gkj8cdtB1OHT8upOpORy4BHAM4eQ56i9wIV6vqJ3aHmlIlJyPs47kcW2o6hDsgX4tB6A6i06YZ+KSHwu8HGcS7wrd/kncLIWl/doeaUqEl8JnAL8zXYUlRID/AQ4m0h8i+0wqvfpbmNPRUoECAN3oPNg2Wo38DUi8ajtICp9tLwOVaRkGs4BrUNtR1HtvAl8iUh8g+0gKr10t/FQOSfxHgfoqSXZoQW4B2dJGy2uHKAjr94QKfkk8DAw0naUHLUc+BaR+Ju2g6jM0ZFXb4jEnwM+BjyAMwJQmVEL/BcwWYsr9+jIq7dFSk4DfgMcYzuKxz0PXEUkvt52EGWHllc6RErygauB7wKlltN4zRbgRiLxP9oOouzS8kqnSEkxziqtN6BH5x+ubUAF8BCReL3tMMo+La9MiJQMwhmFXQV47uqsabYTuAt4gEi81nYYlT20vDIpUjIS+D5wKVpi3dmDs6LH/clL1SnVjpaXDc5I7Arg2+jhFQdahXPYyWwi8bjtMCp7aXnZFCnxA+cD1wJnWU5jUxPwFM581iu2wyh30PLKFpGSiTjvUF4EDLacJlNiOGulzSYS32Y5i3IZLa9sEynxAacDFwKfB0bYDdTr3geeTn68RSSuB/WqQ6Llle0iJSfjFNmFwDjLaQ5FC/AvWgsrEl9tOY/yCC0vN4mUjABOwxmZnQ5MIvuW5akHlgBvJT9eJhLfbjeS8iItLzeLlBQBp+IU2WSckdkYoE+GEjQA64G32V9Wy4jEmzL0+CqHaXl5jbNY4lCcIhsHjE1+DMA5yv/Aj45Gbo04F6xo/fgIZ3L9wI+tyYv1KpVxWl65LlJSCBTgHK7QBDTqJLpyAy0vpZQr6XpeSilX0vJSSrmSlpdSypW0vFSHRCQhIkvbfITS+FgxEdFFG1WPBGwHUFmrzhhzgu0QSnVGR14qZSIyWUReFZFFIvK8iByZ/Pw8Efm5iMwXkfdE5GQReVJE1ojID9ts/9fktu+KyJWdPMYlIrIgOdp7WESy7QwClSW0vFRn+rTZZXxKRPKAXwAXGWMmA7OBH7W5f6MxZhrwEM55jNcAxwKXicjA5H0uT247BbiuzecBEJFjgC8BpydHfQngq+l7isrNdLdRdabdbqOIHItTRi+KCDhH5m9tc/+5yT9XAO8aY7Ymt1uHszLGLpzC+lzyfiOA8cnPtzoH5zSnt5OP0QfQ8yJVh7S8VKoEp5SmdvL1huSfLW3+3no7ICLTgRnAVGNMrYjMwzmy/8DHeMwY89+9FVp5l+42qlStAgaJyFQAEckTkY/1YPsSoDJZXBOAj3dwn5eAi0SkLPkYA0Rk1OEGV96k5aVSYoxpxFnl9ScisgxYirM8T6qewxmBLQfuAA66wrUxZiVwC/BC8n4vAkceZnTlUXpuo1LKlXTkpZRyJS0vpZQraXkppVxJy0sp5UpaXkopV9LyUkq5kpaXUsqVtLyUUq6k5aWUciUtL6WUK2l5KaVcSctLKeVKWl5KKVfS8lJKuZKWl1LKlf4fByAv16R7UiwAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a pie plot showing the distribution of patients by gender using Pandas\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a pie plot showing the distribution of patients by gender using pyplot\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quartiles, Outliers and Boxplots" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Patient IDTime Passed (Days)AntibodyLevel (U/mL)VaccinationGenderAgeWeight (Pounds)
00QUHB451805.55ModernaFemale32215
10S7WD301035.24ModernaMale22302
20pZFa101439.88J&JMale6575
30qDoW596.85J&JMale17325
40zt-N30534.54PfizerFemale35230
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" ], "text/plain": [ " Patient ID Time Passed (Days) AntibodyLevel (U/mL) Vaccination Gender \\\n", "0 0QUHB 45 1805.55 Moderna Female \n", "1 0S7WD 30 1035.24 Moderna Male \n", "2 0pZFa 10 1439.88 J&J Male \n", "3 0qDoW 5 96.85 J&J Male \n", "4 0zt-N 30 534.54 Pfizer Female \n", "\n", " Age Weight (Pounds) \n", "0 32 215 \n", "1 22 302 \n", "2 65 75 \n", "3 17 325 \n", "4 35 230 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the final antibody level of each patient across each of the vaccinations: \n", "# Pfizer, Moderna, J&J, none\n", "\n", "# Start by getting the last (greatest) time passed for each patient\n", "\n", "# Merge this group df with the original dataframe to get the antibody level at the last time passed\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pfizer's potential outliers: 6 1315.57\n", "69 1541.15\n", "107 1728.14\n", "Name: AntibodyLevel (U/mL), dtype: float64\n", "Moderna's potential outliers: 0 1805.55\n", "80 1924.57\n", "195 1748.26\n", "Name: AntibodyLevel (U/mL), dtype: float64\n", "J&J's potential outliers: 16 1499.37\n", "149 1678.15\n", "222 1490.84\n", "239 1517.55\n", "Name: AntibodyLevel (U/mL), dtype: float64\n", "None's potential outliers: Series([], Name: AntibodyLevel (U/mL), dtype: float64)\n" ] } ], "source": [ "# Put vaccinations into a list for for loop (and later for plot labels)\n", "\n", "# Create empty list to fill with antibody level data (for plotting)\n", "\n", "\n", "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n", "\n", " \n", " # Locate the rows which contain patients on each vaccine and get the antibody level data\n", " \n", " \n", " # add subset \n", " \n", " \n", " # Determine outliers using upper and lower bounds\n", " " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a box plot of the final antibody level of each patient across four vaccinations of interest\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line and Scatter Plots" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a line plot of tumor volume vs. time point for a patient treated with Pfizer\n", "\n", "\n", "# pick a patient treated with Pfizer\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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iDfCJ/mbTLB9VmTE3r+k0PE3HxNW9a2w3WaYof3lE/EdEbPGNRMS38smWWX9V5UDPK6hVJViWWd27xnbTaW6o2+gwYWBEHJBLjqy2iqwGqUrPn7yCWlWCZZmVoUNAkcdQpwbuI9K/J6d/G6WItwB/yC1HVktFNw6W4UDPIq+gVpVgWWZFdwgo+hjqNDfUrwAkzYmIOU1vLZB0A/DpvDNXBW40zKboeXOKPtCzyiuoVSVYll2RXWOLPoaydJ19hqRDI+J6AEmHAM/IN1vVUHSkr5IyVINUoQ98XkGtKsHS2iv6GMoSLE4Ezpb0zPT1epIBekOv6EhfJa4GyS6voFbWYDnMpfPxbHvRx1CW3lDLI+JA4ACS7rMvaHSfHXZFR/oq8Wyl1sowd+kd77YXfQx1DRaSnpneeOgq4EpJZzSVMobasHelGw/PVmqtDHOX3vFue9HHUJZqqLOB24Fj0tdvA84B3phXpqrCjYbjU9ZqkPEa5mqTfhvm0nkv217kMZQlWMyMiDc1vf6UpFtyyk+luNGweIM+cbtTQ3/1sx6+sS+sXr+BSRKbIphe4mOy6DaI8coSLDaM6Q01B6h/2M+oLlfLg9SvE3wRJ253auivfpXOx+4LjdsMlDmYV61mIsvcUO8GviLpl5J+RTJX1N/lmy2rq342aBZR3z3M1SZ56Fc9fKt9oaGsbSBFt0GMV5b7WdwCHChpx/T17/LOlNVXP6/Mizhxd6s6GIb2jH5vYz9K591+87IG8yrVTGS5reqzgFOAQ4GQdD3w6Yj4Td6Zs8EY5Amunyf4Iup8O1UdDEN7Rlm3sd2+0Px+GVT5YiJLNdR5wDrgTcBR6fPv5ZkpG5xB93PvZ3fjIvqdd6o6GIZuoGXdxlb7QkNZ2gGqPqYkSwP3zhHxz02vPyNpXk75sQEbdINtPxv1iuqN1q7qYBjaM8q6jc37Qll7Q1W9c0SWYHG1pOOApenro4DL8suSDdKgD/5+n+DLVOdbta6Qvcijq2uZ2j7yVNZAm1Wn+1k8SnI/CwEf4ukpyicBvydpx7CKK+IEV/aDuldV6wrZi7y6upal7SNPVb+YaNtmERE7RMSO6d+tImJy+tgqInYcZCYtP0XPN1MnVesK2Ys8u7oOou3johWrmbPoKvZZcBlzFl010PaCqh9rnUoWz4uIuyW9sNX7nkywHjwKvb/qWmpqlmdX16xVMr1UYRVdmqn6sdapzeJDwEnAGS3eC+CwXHJkAzcMJzgrl4lUyfR60i9DA3OVj7VO1VAnpU9fGxGvbH4ArxtM9sysjiZSJdNrFVbVG5iLlqU31E+AsVVRrdLMrAYGMXBsIlUyvZ70q97AXLRObRbPBqYDUyQdRNIrCmBHYLsB5M2sMFUeaTsRg6zX77VKpteT/jD0VstTp5LFXODtwJ7AmU3pjwIfzzFPZoXqdMKE6jZQZlGGev1uej3pF93AXPULkLbBIiKWAEskvSkiLhhgnoZW1Xemumh3wvzUD+7gjxufqvXYgCrU60/kpF9UA3PRPbH6IUubxaWSjgdmNC8fEZ+eyIolTQJGgdURcYSknUnmnJoB/BI4JiIeSZddCJwIbALeFxHLJrLuMqrKzjQMAa3difGRP2zcIq1sV90TVZV6/Sr1KrpoxWo+vPTWP91jo6Fq+06WiQQvBo4EngQea3pM1PuBu5peLwCujIhZwJXpayTtCxwH7AccDnw1DTS1UtYJ2ppVfSK0rMZ7Yux01d3PQWCDGFBW9YFjZdM4ZsYGioYyldi6yVKy2DMiDu/nSiXtCfw1cBrJeA5IAtIr0udLgGuAj6Xp50XE48B9ku4FDgZu7GeeilaF4n/R9dmDKtW0qxPfZuutWL9hy9JFu+DSz9LioEqeRdfr102nmzJB+UpsnWTqOivp+RFxW/dFM/sC8FFgh6a03SNiLUBErJW0W5o+Hfhp03Kr0rRaqULxv8iANuheOrDlCRMYV8NqP4PrIAN1lap4yq7TsVG1EluWYHEo8HZJ9wGPk3ShjYg4oJcVSjoCeDAilkt6RZaPtEhrWaaTdBLJqHP23nvvXrJXmCp06ysyoA26VNPphJn1qrufwbUKJU/bUrtjZpJUuXnDsgSL1/Z5nXOAv5H0OmBbYEdJ3wYekDQtLVVMAx5Ml18F7NX0+T2BNa3+cUQsBhYDjIyMtK4kLKkqFP+LDGhlOVmO56q7n8G1CiVP21K7Y6ZqgQKy3YP7V43nkp4BzAOOJ2lzGLeIWAgsTP/fK4CPRMRbJZ0OnAAsSv9enH7kEuA7ks4E9gBmATf1su6yK3vxv8iAVsWTZT+DaxVKnva05va1qdtNZputt+K3GzaW8iIwqyz34P4zkrmgjifpjXQB8G855GURsFTSicD9wNEAEXGHpKXAnSQ9sk6OiPYtRparogLa/LmzmX/+rWzc9HSBcfIklfpk2c/gmlegHoau0IM2tn3tkT9sZMrkSXz+2BdU+rvtNN3HXwFvJhnJfTXJzY8Ojoh39GvlEXENSa8nIuI3wKvaLHcaSc8pG2ZjKxYrUNHYz+Da70BdlbE9VVN0r8G8dBpnsQyYCRwaEW+NiB8ATw0mW2abO33ZSjY+tXl02PhUlGocStVUYWxPFZWlfa3fOgWLF5F0Wf2xpCvS6qHaDYazaqjrAVgkf6f5aNeOVub2tSw63c9iRUR8LCJmAqcCBwF/JumHaRdVs4Gp6wFYpGH7Tgd1S9W6joLPMt0HEXFDRLyHZDDcF4CX5pkps7HKdgAWeS/nfinbd5qnQU5VU9d7sSvazFlSdSMjIzE6Olp0NqyPytJzZ2zDMFS373xZvtO8zVl0Vcuu19OnTuGGBb5DdDNJyyNiZGx6lkF5Zlso4iRTlnEodertUpbvNG9un5k4Bwsbt2HvcukTT/VUcVDneOV9AZepzULSoZLekT7fVdI+fcuBVc6wd7kctobhdqrUblP39plBtMl0DRaSTiGZKnxhmjQZ+HbfcmCVM+xX1nU/8WRRtXub1LXRuWEQF3BZqqHeQNJt9maAiFgjaYfOH7E6G4YifSdVmPQxb1Vst6lz+8wgLuCyBIsnIiIkBfxpMkHro6r1SPGkdvU+8WQx7KXLQRjPeWEQF3BZ2iyWSvoaMFXSu4AfA//etxwMuaoV56H+RXrrzu02+RrveWEQVaOZxlmkkwq+huRGRMsi4oq+5SAnVRln4f7fVkV1GmtSRr2cF/pVQzGhcRZpcCh9gKgiF+etitxuk69ezgt5V41muZ/Fo2w5GfRvgVHgwxHxizwyNiyGvbHYqmvY223yVMbzQpY2izOB+STzQu0JfISkzeI84Oz8sjYc3A3TrFhlHC9SxvNClmqowyPixU2vF0v6aUR8WtLH88rYsHBxvt6q1tNt2JR1NoIynheyBIunJB0DnJ++PqrpvXrOQjhgLs7XU1lPRPa0Mo8XKdt5IUs11FuAtwEPAg+kz98qaQrwnhzzZjYweVRFDPu0KFXgDibZdS1ZpA3Yr2/z9vX9zY7Z4OVVAvCJqPzK2JBcVlnmhtpW0smSvirp7MZjEJmrijI2kFl2eZUAPHCt/MrYkFxWWaqhvgU8G5gLXEvSI+rRPDNVJVUcgW2by6sE4BNR+Xk2guyyNHD/RUQcLenIiFgi6TvAsrwzVhVlbiCzbPKqiihjjxbbUtkakssqS7DYmP5dL2l/4H+AGbnlqGJcL119eU6M6BORjUeZu1pnCRaLJe0EfAK4BNge+Kdcc1UhbiCrPpcArAzK3tW6Y7CQtBXwu4h4BLgOeM5AclUhnq67HlwCsKKVvUq7Y7CIiKckvQdYOqD8VE5drkrLXPw1a1bXfbXsVdpZqqGukPQR4HvAY43EiHg4t1xVTNWvSste/DVrqPO+WvYq7SxdZ98JnExSDbU8fZT/RhGWmUcaW1XUeV8te1frLCO49xlERqw4ZS/+mjXUeV8te5V2lvtZbAd8CNg7Ik6SNAuYHRGX5p47G4iyF3/NGuq+r5a5SjtLNdQ5wBPAIenrVcBncsuRDVzZi79mDd5Xi5OlgXtmRBwr6c0AEbFBknLOlw1Q2Yu/Zg3eV4uTJVg8kU5HHgCSZgKP55orG7gyF3/NmnlfLUaWaqhTgcuBvSSdC1wJfLTXFUraS9LVku6SdIek96fpO0u6QtI96d+dmj6zUNK9klZKmtvrus3MrDdZekP9SNJy4CWAgPdHxEMTWOeTwIcj4mZJOwDLJV0BvB24MiIWSVoALAA+Jmlf4DhgP2AP4MeSnhsRm9r8fzMz67Ms97O4BHgNcE1EXDrBQEFErI2Im9PnjwJ3AdOBI4El6WJLgHnp8yOB8yLi8Yi4D7gXOHgieTAzs/HJUg11BvAy4E5J35d0lKRt+7FySTOAg4CfAbtHxFpIAgqwW7rYdODXTR9blaa1+n8nSRqVNLpu3bp+ZLFvfIMkM6uyrsEiIq6NiH8gmURwMXAMyf24J0TS9sAFwAci4nedFm2VrTZ5XRwRIxExsuuuu040i33jGySZWdVlKVmQ9oZ6E/D3wP/i6eqinkiaTBIozo2IC9PkByRNS9+fxtMBaRWwV9PH9wTWTGT9g1bnKQrMbDhkabP4Hkm7wmHAV0jGXby31xWmYzS+AdwVEWc2vXUJcEL6/ATg4qb04yRtI2kfYBZwU6/rL0Kdpygws+GQZZzFOcDxjd5HkuZIOj4iTu5xnXOAtwG3SbolTfs4sAhYKulE4H7gaICIuEPSUuBOkp5UJ1etJ1Tdpygws/rL0nX2ckkvSEdwHwvcB1zY5WOd/t/1tG6HAHhVm8+cBpzW6zqL5hskmVnVtQ0Wkp5LMr7hzcBvSO5noYh45YDyVhueosDMqq5TyeJu4L+A10fEvQCSPjiQXNWQpygwsyrrFCzeRFKyuFrS5cB5tK8+qq263sLRzGw82vaGioj/jIhjgecB1wAfBHaXdJak1wwof4Xy+Agzs0SWQXmPRcS5EXEEyRiHW0jmbao9j48wM0tkGpTXEBEPR8TXIuKwvDJUJh4fYWaWGFewGDbtxkF4fISZDRsHiw58C0czs0SWEdxDy+MjzMwSDhZdeHyEmZmroczMLAMHCzMz68rBwszMunKwMDOzrhwszMysKwcLMzPrysHCzMy6crAwM7OuHCzMzKwrBwszM+vKwcLMzLpysDAzs648kWDF+J7gZlYEB4sKadwTvHGr18Y9wQEHDDPLlauhKsT3BDezojhYVIjvCW5mRXGwqBDfE9zMiuJgUSG+J7iZFcUN3BXie4KbWVEcLCrG9wQ3syK4GsrMzLpysDAzs64cLMzMrCsHCzMz68rBwszMulJEFJ2HXEhaB/yq4GzsAjxUcB4GwdtZH8OwjeDt7OTPI2LXsYm1DRZlIGk0IkaKzkfevJ31MQzbCN7OXrgayszMunKwMDOzrhws8rW46AwMiLezPoZhG8HbOW5uszAzs65csjAzs64cLMzMrCsHiwmQdLakByXd3pS2s6QrJN2T/t2p6b2Fku6VtFLS3GJyPX5ttvNUSasl3ZI+Xtf0XuW2U9Jekq6WdJekOyS9P02vze/ZYRvr9ltuK+kmSbem2/mpNL02vyV03M58fs+I8KPHB/By4IXA7U1pnwUWpM8XAP+aPt8XuBXYBtgH+H/ApKK3YQLbeSrwkRbLVnI7gWnAC9PnOwD/nW5LbX7PDttYt99SwPbp88nAz4CX1Om37LKdufyeLllMQERcBzw8JvlIYEn6fAkwryn9vIh4PCLuA+4FDh5EPieqzXa2U8ntjIi1EXFz+vxR4C5gOjX6PTtsYzuV20aASPw+fTk5fQQ1+i2h43a2M6HtdLDov90jYi0kByewW5o+Hfh103Kr6HygVsF7JP08raZqFOkrv52SZgAHkVyp1fL3HLONULPfUtIkSbcADwJXREQtf8s22wk5/J4OFoOjFmlV7rd8FjATeAGwFjgjTa/0dkraHrgA+EBE/K7Toi3SKrGdLbaxdr9lRGyKiBcAewIHS9q/w+J1285cfk8Hi/57QNI0gPTvg2n6KmCvpuX2BNYMOG99ExEPpDvqU8C/83RxtrLbKWkyyUn03Ii4ME2u1e/Zahvr+Fs2RMR64BrgcGr2WzZr3s68fk8Hi/67BDghfX4CcHFT+nGStpG0DzALuKmA/PVF46BLvQFo9JSq5HZKEvAN4K6IOLPprdr8nu22sYa/5a6SpqbPpwCvBu6mRr8ltN/O3H7Polv0q/wAvktSzNtIErVPBJ4FXAnck/7duWn5fyTpgbASeG3R+Z/gdn4LuA34eboTTqvydgKHkhTJfw7ckj5eV6ffs8M21u23PABYkW7P7cAn0/Ta/JZdtjOX39PTfZiZWVeuhjIzs64cLMzMrCsHCzMz68rBwszMunKwMDOzrhwsrBYkfV7SB5peL5P09abXZ0j6UIfPf1rSq7us41RJH2mRPlXSP3T43E+6bsDmy39T0n3pjKE3S3rpeD4/XpKukTSS5zqs+hwsrC5+AhwCIGkrYBdgv6b3DwFuaPfhiPhkRPy4x3VPBdoGi4g4pIf/OT+SaRwWAF/rLVtm/eNgYXVxA2mwIAkStwOPStpJ0jbAXwIrJL1I0rWSlqelj8b0D9+UdFT6/HWS7pZ0vaQvSbq0aT37plfiv5D0vjRtETAzLQmcPjZjkn6f/n1F+tnz0/9/bjqqupPrgL9IP/8hSbenjw+kaTO0+X1GPiLp1PT5NZL+Nb3nwX9LelmaPkXSeelEc98DpqTpk9Lv4XZJt0n6YJYv3obD1kVnwKwfImKNpCcl7U0SNG4kmVHzpcBvSUazBvBl4MiIWCfpWOA04J2N/yNpW5Ir+ZdHxH2SvjtmVc8DXklyP4iVks4iufrfPy0JdHMQSTBbQxLg5gDXd1j+9cBtkl4EvAN4McmEcD+TdC3wSJf1bR0RByu5Ac4pJFNCvBv4Q0QcIOkA4OZ02RcA0yNif0iq1zJsjw0JlyysThqli0awuLHp9U+A2cD+wBXptM6fIJlMrdnzgF9EMt8/JFOdNLsskvsBPEQyEd3u48zjTRGxKpJJ3m4BZrRZ7vQ0jyeRTK9yKPCfEfFYJPcwuBB4WYb1NSZEXN60rpcD3waIiJ+TBFKAXwDPkfRlSYcDnWbdtSHjkoXVSaPd4vkk1VC/Bj5MctI7m+SK/I6I6NRg3K1a6PGm55sY/zGU9fPzI+L8P2WqfeP7k2x+0bdtm/WNXdcW8/xExCOSDgTmAicDx9BU6rLh5pKF1ckNwBHAw5FM0fwwSePzS0lKGSuBXRu9iyRNlrTfmP9xN8nV9Yz09bEZ1vsoSbVUnq4D5knaTtIzSGYT/S/gAWA3Sc9K22aOyPi/3gKg5P4HB6TPdwG2iogLgH8iuZWuGeCShdXLbSS9oL4zJm37tNqItBH7S5KeSbL/fwG4o7FwRGxIu8FeLukhMkzhHBG/kXRD2tD8w4iY368NalrHzZK+2ZSfr0fECki6/ZLc8e4+kmDXzVnAOZIas882/uf0NL1xEbmwP7m3OvCss2ZjSNo+In6f9lT6CnBPRHy+6HyZFcnVUGZbelfauHwH8Ew8zsHMJQszM+vOJQszM+vKwcLMzLpysDAzs64cLMzMrCsHCzMz6+r/A4qFH0I3QAOPAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a scatter plot of average antibody level vs. patient weight in pounds for the Pfizer vaccine\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation and Regression" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The correlation between patient weight and the average antibody level is 0.06\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculate the correlation coefficient and linear regression model \n", "# for patient weight and average antibody level for the pfizer vaccine\n", "\n", "# correlation determines if there is a positive, negative or no relationship between the variables\n", "# positive number = postitive relationship\n", "# negative number = negative relationship\n", "# number close to 0 = little to no relationship\n", "\n", "\n", "\n", "#model # gets the y = mx + b\n", " #index 0 is the slope,\n", " #index 1 is the y intercept\n", " \n", " # calculate the y values for the linear regression model using the slope intercept equation\n", " # objective is to display the trendline on the plot of the average weight v. average antibody level graph \n", " \n", "\n", "\n", "# create the scatter plot again\n", "\n", "\n", "# draw the line with the linear regression line on the plot\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }