Matplotlib Challenge

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Observations and Insights " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Capomulin was used the most amount among all the drug regimens for treatments.\n", "2. Male patients receiving treatments are slightly more than females.\n", "3. The average tumor volume measured correlated with weight for Capomulin. The correlation between mouse weight and average tumor volume is 0.59,which is strong and Positive. \n", "4. Capomulin and Ramicane drugs were most successful in reducing the tumors." ] }, { "cell_type": "code", "execution_count": 18, "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>Mouse ID</th>\n", " <th>Drug Regimen</th>\n", " <th>Sex</th>\n", " <th>Age_months</th>\n", " <th>Weight (g)</th>\n", " <th>Timepoint</th>\n", " <th>Tumor Volume (mm3)</th>\n", " <th>Metastatic Sites</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>45.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>38.825898</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>10</td>\n", " <td>35.014271</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>15</td>\n", " <td>34.223992</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>32.997729</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>25</td>\n", " <td>63.145652</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>30</td>\n", " <td>65.841013</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>35</td>\n", " <td>69.176246</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1891</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>40</td>\n", " <td>70.314904</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1892</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>45</td>\n", " <td>73.867845</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1893 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " Mouse ID Drug Regimen Sex Age_months Weight (g) Timepoint \\\n", "0 k403 Ramicane Male 21 16 0 \n", "1 k403 Ramicane Male 21 16 5 \n", "2 k403 Ramicane Male 21 16 10 \n", "3 k403 Ramicane Male 21 16 15 \n", "4 k403 Ramicane Male 21 16 20 \n", "... ... ... ... ... ... ... \n", "1888 z969 Naftisol Male 9 30 25 \n", "1889 z969 Naftisol Male 9 30 30 \n", "1890 z969 Naftisol Male 9 30 35 \n", "1891 z969 Naftisol Male 9 30 40 \n", "1892 z969 Naftisol Male 9 30 45 \n", "\n", " Tumor Volume (mm3) Metastatic Sites \n", "0 45.000000 0 \n", "1 38.825898 0 \n", "2 35.014271 1 \n", "3 34.223992 1 \n", "4 32.997729 1 \n", "... ... ... \n", "1888 63.145652 2 \n", "1889 65.841013 3 \n", "1890 69.176246 4 \n", "1891 70.314904 4 \n", "1892 73.867845 4 \n", "\n", "[1893 rows x 8 columns]" ] }, "execution_count": 18, "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", "import numpy as np\n", "from scipy.stats import linregress\n", "\n", "# Study data files\n", "mouse_metadata_path = \"data/Mouse_metadata.csv\"\n", "study_results_path = \"data/Study_results.csv\"\n", "\n", "# Read the mouse data and the study results\n", "mouse_metadata = pd.read_csv(mouse_metadata_path)\n", "study_results = pd.read_csv(study_results_path)\n", "\n", "# Combine the data into a single dataset\n", "newdata= pd.merge(mouse_metadata,study_results,on=\"Mouse ID\",how=\"left\")\n", "\n", "# Display the data table for preview\n", "newdata" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "249" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the number of mice.\n", "len(newdata[\"Mouse ID\"].unique())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['g989'], dtype=object)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint. \n", "duplicated_ID = newdata.loc[newdata.duplicated(subset=[\"Mouse ID\",\"Timepoint\"]),\"Mouse ID\"].unique()\n", "duplicated_ID" ] }, { "cell_type": "code", "execution_count": 21, "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>Mouse ID</th>\n", " <th>Drug Regimen</th>\n", " <th>Sex</th>\n", " <th>Age_months</th>\n", " <th>Weight (g)</th>\n", " <th>Timepoint</th>\n", " <th>Tumor Volume (mm3)</th>\n", " <th>Metastatic Sites</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>908</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>0</td>\n", " <td>45.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>909</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>0</td>\n", " <td>45.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>910</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>5</td>\n", " <td>48.786801</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>911</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>5</td>\n", " <td>47.570392</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>912</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>10</td>\n", " <td>51.745156</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>913</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>10</td>\n", " <td>49.880528</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>914</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>15</td>\n", " <td>51.325852</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>915</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>15</td>\n", " <td>53.442020</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>916</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>20</td>\n", " <td>55.326122</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>917</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>20</td>\n", " <td>54.657650</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>918</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>25</td>\n", " <td>56.045564</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>919</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>30</td>\n", " <td>59.082294</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>920</th>\n", " <td>g989</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>35</td>\n", " <td>62.570880</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mouse ID Drug Regimen Sex Age_months Weight (g) Timepoint \\\n", "908 g989 Propriva Female 21 26 0 \n", "909 g989 Propriva Female 21 26 0 \n", "910 g989 Propriva Female 21 26 5 \n", "911 g989 Propriva Female 21 26 5 \n", "912 g989 Propriva Female 21 26 10 \n", "913 g989 Propriva Female 21 26 10 \n", "914 g989 Propriva Female 21 26 15 \n", "915 g989 Propriva Female 21 26 15 \n", "916 g989 Propriva Female 21 26 20 \n", "917 g989 Propriva Female 21 26 20 \n", "918 g989 Propriva Female 21 26 25 \n", "919 g989 Propriva Female 21 26 30 \n", "920 g989 Propriva Female 21 26 35 \n", "\n", " Tumor Volume (mm3) Metastatic Sites \n", "908 45.000000 0 \n", "909 45.000000 0 \n", "910 48.786801 0 \n", "911 47.570392 0 \n", "912 51.745156 0 \n", "913 49.880528 0 \n", "914 51.325852 1 \n", "915 53.442020 0 \n", "916 55.326122 1 \n", "917 54.657650 1 \n", "918 56.045564 1 \n", "919 59.082294 1 \n", "920 62.570880 2 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Optional: Get all the data for the duplicate mouse ID. \n", "duplicated_data= newdata.loc[newdata[\"Mouse ID\"]=='g989']\n", "duplicated_data" ] }, { "cell_type": "code", "execution_count": 14, "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>Mouse ID</th>\n", " <th>Drug Regimen</th>\n", " <th>Sex</th>\n", " <th>Age_months</th>\n", " <th>Weight (g)</th>\n", " <th>Timepoint</th>\n", " <th>Tumor Volume (mm3)</th>\n", " <th>Metastatic Sites</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>45.000000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>38.825898</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>10</td>\n", " <td>35.014271</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>15</td>\n", " <td>34.223992</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>k403</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>21</td>\n", " <td>16</td>\n", " <td>20</td>\n", " <td>32.997729</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>25</td>\n", " <td>63.145652</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>30</td>\n", " <td>65.841013</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>35</td>\n", " <td>69.176246</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1891</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>40</td>\n", " <td>70.314904</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1892</th>\n", " <td>z969</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>45</td>\n", " <td>73.867845</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1880 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " Mouse ID Drug Regimen Sex Age_months Weight (g) Timepoint \\\n", "0 k403 Ramicane Male 21 16 0 \n", "1 k403 Ramicane Male 21 16 5 \n", "2 k403 Ramicane Male 21 16 10 \n", "3 k403 Ramicane Male 21 16 15 \n", "4 k403 Ramicane Male 21 16 20 \n", "... ... ... ... ... ... ... \n", "1888 z969 Naftisol Male 9 30 25 \n", "1889 z969 Naftisol Male 9 30 30 \n", "1890 z969 Naftisol Male 9 30 35 \n", "1891 z969 Naftisol Male 9 30 40 \n", "1892 z969 Naftisol Male 9 30 45 \n", "\n", " Tumor Volume (mm3) Metastatic Sites \n", "0 45.000000 0 \n", "1 38.825898 0 \n", "2 35.014271 1 \n", "3 34.223992 1 \n", "4 32.997729 1 \n", "... ... ... \n", "1888 63.145652 2 \n", "1889 65.841013 3 \n", "1890 69.176246 4 \n", "1891 70.314904 4 \n", "1892 73.867845 4 \n", "\n", "[1880 rows x 8 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a clean DataFrame by dropping the duplicate mouse by its ID.\n", "cleandata= newdata[newdata['Mouse ID'].isin(duplicated_ID)== False]\n", "cleandata\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "248" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the number of mice in the clean DataFrame.\n", "len(cleandata[\"Mouse ID\"].unique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary Statistics" ] }, { "cell_type": "code", "execution_count": 23, "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>Mean</th>\n", " <th>Median</th>\n", " <th>Variance</th>\n", " <th>Standard_deviation</th>\n", " <th>SEM</th>\n", " </tr>\n", " <tr>\n", " <th>Drug Regimen</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Capomulin</th>\n", " <td>40.675741</td>\n", " <td>41.557809</td>\n", " <td>24.947764</td>\n", " <td>4.994774</td>\n", " <td>0.329346</td>\n", " </tr>\n", " <tr>\n", " <th>Ceftamin</th>\n", " <td>52.591172</td>\n", " <td>51.776157</td>\n", " <td>39.290177</td>\n", " <td>6.268188</td>\n", " <td>0.469821</td>\n", " </tr>\n", " <tr>\n", " <th>Infubinol</th>\n", " <td>52.884795</td>\n", " <td>51.820584</td>\n", " <td>43.128684</td>\n", " <td>6.567243</td>\n", " <td>0.492236</td>\n", " </tr>\n", " <tr>\n", " <th>Ketapril</th>\n", " <td>55.235638</td>\n", " <td>53.698743</td>\n", " <td>68.553577</td>\n", " <td>8.279709</td>\n", " <td>0.603860</td>\n", " </tr>\n", " <tr>\n", " <th>Naftisol</th>\n", " <td>54.331565</td>\n", " <td>52.509285</td>\n", " <td>66.173479</td>\n", " <td>8.134708</td>\n", " <td>0.596466</td>\n", " </tr>\n", " <tr>\n", " <th>Placebo</th>\n", " <td>54.033581</td>\n", " <td>52.288934</td>\n", " <td>61.168083</td>\n", " <td>7.821003</td>\n", " <td>0.581331</td>\n", " </tr>\n", " <tr>\n", " <th>Propriva</th>\n", " <td>52.320930</td>\n", " <td>50.446266</td>\n", " <td>43.852013</td>\n", " <td>6.622085</td>\n", " <td>0.544332</td>\n", " </tr>\n", " <tr>\n", " <th>Ramicane</th>\n", " <td>40.216745</td>\n", " <td>40.673236</td>\n", " <td>23.486704</td>\n", " <td>4.846308</td>\n", " <td>0.320955</td>\n", " </tr>\n", " <tr>\n", " <th>Stelasyn</th>\n", " <td>54.233149</td>\n", " <td>52.431737</td>\n", " <td>59.450562</td>\n", " <td>7.710419</td>\n", " <td>0.573111</td>\n", " </tr>\n", " <tr>\n", " <th>Zoniferol</th>\n", " <td>53.236507</td>\n", " <td>51.818479</td>\n", " <td>48.533355</td>\n", " <td>6.966589</td>\n", " <td>0.516398</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean Median Variance Standard_deviation SEM\n", "Drug Regimen \n", "Capomulin 40.675741 41.557809 24.947764 4.994774 0.329346\n", "Ceftamin 52.591172 51.776157 39.290177 6.268188 0.469821\n", "Infubinol 52.884795 51.820584 43.128684 6.567243 0.492236\n", "Ketapril 55.235638 53.698743 68.553577 8.279709 0.603860\n", "Naftisol 54.331565 52.509285 66.173479 8.134708 0.596466\n", "Placebo 54.033581 52.288934 61.168083 7.821003 0.581331\n", "Propriva 52.320930 50.446266 43.852013 6.622085 0.544332\n", "Ramicane 40.216745 40.673236 23.486704 4.846308 0.320955\n", "Stelasyn 54.233149 52.431737 59.450562 7.710419 0.573111\n", "Zoniferol 53.236507 51.818479 48.533355 6.966589 0.516398" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n", "\n", "# This method is the most straighforward, creating multiple series and putting them all together at the end.\n", "Mean=cleandata.groupby(\"Drug Regimen\").mean()['Tumor Volume (mm3)']\n", "Median=cleandata.groupby(\"Drug Regimen\").median()['Tumor Volume (mm3)']\n", "Variance=cleandata.groupby(\"Drug Regimen\").var()['Tumor Volume (mm3)']\n", "Standard_deviation=cleandata.groupby(\"Drug Regimen\").std()['Tumor Volume (mm3)']\n", "SEM=cleandata.groupby(\"Drug Regimen\").sem()['Tumor Volume (mm3)']\n", "stat_table =pd.DataFrame({'Mean': Mean,\n", " 'Median':Median,\n", " 'Variance': Variance,\n", " 'Standard_deviation': Standard_deviation,\n", " 'SEM':SEM})\n", "stat_table\n" ] }, { "cell_type": "code", "execution_count": 17, "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 tr th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe thead tr:last-of-type th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"5\" halign=\"left\">Tumor Volume (mm3)</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>mean</th>\n", " <th>median</th>\n", " <th>var</th>\n", " <th>std</th>\n", " <th>sem</th>\n", " </tr>\n", " <tr>\n", " <th>Drug Regimen</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Capomulin</th>\n", " <td>40.675741</td>\n", " <td>41.557809</td>\n", " <td>24.947764</td>\n", " <td>4.994774</td>\n", " <td>0.329346</td>\n", " </tr>\n", " <tr>\n", " <th>Ceftamin</th>\n", " <td>52.591172</td>\n", " <td>51.776157</td>\n", " <td>39.290177</td>\n", " <td>6.268188</td>\n", " <td>0.469821</td>\n", " </tr>\n", " <tr>\n", " <th>Infubinol</th>\n", " <td>52.884795</td>\n", " <td>51.820584</td>\n", " <td>43.128684</td>\n", " <td>6.567243</td>\n", " <td>0.492236</td>\n", " </tr>\n", " <tr>\n", " <th>Ketapril</th>\n", " <td>55.235638</td>\n", " <td>53.698743</td>\n", " <td>68.553577</td>\n", " <td>8.279709</td>\n", " <td>0.603860</td>\n", " </tr>\n", " <tr>\n", " <th>Naftisol</th>\n", " <td>54.331565</td>\n", " <td>52.509285</td>\n", " <td>66.173479</td>\n", " <td>8.134708</td>\n", " <td>0.596466</td>\n", " </tr>\n", " <tr>\n", " <th>Placebo</th>\n", " <td>54.033581</td>\n", " <td>52.288934</td>\n", " <td>61.168083</td>\n", " <td>7.821003</td>\n", " <td>0.581331</td>\n", " </tr>\n", " <tr>\n", " <th>Propriva</th>\n", " <td>52.320930</td>\n", " <td>50.446266</td>\n", " <td>43.852013</td>\n", " <td>6.622085</td>\n", " <td>0.544332</td>\n", " </tr>\n", " <tr>\n", " <th>Ramicane</th>\n", " <td>40.216745</td>\n", " <td>40.673236</td>\n", " <td>23.486704</td>\n", " <td>4.846308</td>\n", " <td>0.320955</td>\n", " </tr>\n", " <tr>\n", " <th>Stelasyn</th>\n", " <td>54.233149</td>\n", " <td>52.431737</td>\n", " <td>59.450562</td>\n", " <td>7.710419</td>\n", " <td>0.573111</td>\n", " </tr>\n", " <tr>\n", " <th>Zoniferol</th>\n", " <td>53.236507</td>\n", " <td>51.818479</td>\n", " <td>48.533355</td>\n", " <td>6.966589</td>\n", " <td>0.516398</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tumor Volume (mm3) \n", " mean median var std sem\n", "Drug Regimen \n", "Capomulin 40.675741 41.557809 24.947764 4.994774 0.329346\n", "Ceftamin 52.591172 51.776157 39.290177 6.268188 0.469821\n", "Infubinol 52.884795 51.820584 43.128684 6.567243 0.492236\n", "Ketapril 55.235638 53.698743 68.553577 8.279709 0.603860\n", "Naftisol 54.331565 52.509285 66.173479 8.134708 0.596466\n", "Placebo 54.033581 52.288934 61.168083 7.821003 0.581331\n", "Propriva 52.320930 50.446266 43.852013 6.622085 0.544332\n", "Ramicane 40.216745 40.673236 23.486704 4.846308 0.320955\n", "Stelasyn 54.233149 52.431737 59.450562 7.710419 0.573111\n", "Zoniferol 53.236507 51.818479 48.533355 6.966589 0.516398" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen\n", "\n", "# This method produces everything in a single groupby function\n", "summary_table= cleandata.groupby(\"Drug Regimen\").agg({\"Tumor Volume (mm3)\":[\"mean\",\"median\",\"var\",\"std\",\"sem\"]})\n", "summary_table\n", "\n", "## Bar and Pie Charts" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pandas. \n", "count=cleandata[\"Drug Regimen\"].value_counts()\n", "count.plot(kind=\"bar\")\n", "plt.xlabel('Treatment')\n", "plt.ylabel('Total Number')\n", "plt.title(\"Drug Treatment\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a bar plot showing the total number of mice for each treatment throughout the course of the study using pyplot.\n", "count=cleandata[\"Drug Regimen\"].value_counts()\n", "plt.bar(count.index.values,count.values)\n", "plt.xticks(rotation=90)\n", "plt.xlabel('Treatment')\n", "plt.ylabel('Total Number')\n", "plt.title(\"Drug Treatment\")\n", "plt.savefig(\"Images/bar_plot.png\", bbox_inches = \"tight\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a pie plot showing the distribution of female versus male mice using pandas\n", "count=cleandata[\"Sex\"].value_counts()\n", "count.plot(kind=\"pie\",autopct='%1.1f%%')\n", "plt.title(\"Female VS Male\")\n", "plt.legend(loc=\"best\")\n", "plt.show()\n", "plt.savefig(\"Images/pie_plot.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a pie plot showing the distribution of female versus male mice using pyplot\n", "count=cleandata[\"Sex\"].value_counts()\n", "plt.pie(count.values,labels=count.index.values,autopct='%1.1f%%')\n", "plt.title(\"Female VS Male\")\n", "plt.legend(loc='best')\n", "plt.savefig(\"Images/pie_plot.png\", bbox_inches = \"tight\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quartiles, Outliers and Boxplots" ] }, { "cell_type": "code", "execution_count": 22, "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>Mouse ID</th>\n", " <th>Timepoint</th>\n", " <th>Drug Regimen</th>\n", " <th>Sex</th>\n", " <th>Age_months</th>\n", " <th>Weight (g)</th>\n", " <th>Tumor Volume (mm3)</th>\n", " <th>Metastatic Sites</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>a203</td>\n", " <td>45</td>\n", " <td>Infubinol</td>\n", " <td>Female</td>\n", " <td>20</td>\n", " <td>23</td>\n", " <td>67.973419</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>a251</td>\n", " <td>45</td>\n", " <td>Infubinol</td>\n", " <td>Female</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>65.525743</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>a262</td>\n", " <td>45</td>\n", " <td>Placebo</td>\n", " <td>Female</td>\n", " <td>17</td>\n", " <td>29</td>\n", " <td>70.717621</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>a275</td>\n", " <td>45</td>\n", " <td>Ceftamin</td>\n", " <td>Female</td>\n", " <td>20</td>\n", " <td>28</td>\n", " <td>62.999356</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>a366</td>\n", " <td>30</td>\n", " <td>Stelasyn</td>\n", " <td>Female</td>\n", " <td>16</td>\n", " <td>29</td>\n", " <td>63.440686</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>z435</td>\n", " <td>10</td>\n", " <td>Propriva</td>\n", " <td>Female</td>\n", " <td>12</td>\n", " <td>26</td>\n", " <td>48.710661</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>244</th>\n", " <td>z578</td>\n", " <td>45</td>\n", " <td>Ramicane</td>\n", " <td>Male</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>30.638696</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>245</th>\n", " <td>z581</td>\n", " <td>45</td>\n", " <td>Infubinol</td>\n", " <td>Female</td>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>62.754451</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>z795</td>\n", " <td>45</td>\n", " <td>Naftisol</td>\n", " <td>Female</td>\n", " <td>13</td>\n", " <td>29</td>\n", " <td>65.741070</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>z969</td>\n", " <td>45</td>\n", " <td>Naftisol</td>\n", " <td>Male</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>73.867845</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>248 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " Mouse ID Timepoint Drug Regimen Sex Age_months Weight (g) \\\n", "0 a203 45 Infubinol Female 20 23 \n", "1 a251 45 Infubinol Female 21 25 \n", "2 a262 45 Placebo Female 17 29 \n", "3 a275 45 Ceftamin Female 20 28 \n", "4 a366 30 Stelasyn Female 16 29 \n", ".. ... ... ... ... ... ... \n", "243 z435 10 Propriva Female 12 26 \n", "244 z578 45 Ramicane Male 11 16 \n", "245 z581 45 Infubinol Female 24 25 \n", "246 z795 45 Naftisol Female 13 29 \n", "247 z969 45 Naftisol Male 9 30 \n", "\n", " Tumor Volume (mm3) Metastatic Sites \n", "0 67.973419 2 \n", "1 65.525743 1 \n", "2 70.717621 4 \n", "3 62.999356 3 \n", "4 63.440686 1 \n", ".. ... ... \n", "243 48.710661 0 \n", "244 30.638696 0 \n", "245 62.754451 3 \n", "246 65.741070 3 \n", "247 73.867845 4 \n", "\n", "[248 rows x 8 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the final tumor volume of each mouse across four of the treatment regimens: \n", "# Capomulin, Ramicane, Infubinol, and Ceftamin\n", "\n", "# Start by getting the last (greatest) timepoint for each mouse\n", "Max= cleandata.groupby(\"Mouse ID\")[\"Timepoint\"].max()\n", "Max= Max.reset_index()\n", "Max\n", "\n", "# Merge this group df with the original dataframe to get the tumor volume at the last timepoint\n", "merge_data= Max.merge(cleandata,on=[\"Mouse ID\",\"Timepoint\"],how=\"left\")\n", "merge_data\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Capomulin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n", "Ramicane's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n", "Infubinol's potential outliers: 31 36.321346\n", "Name: Tumor Volume (mm3), dtype: float64\n", "Ceftamin's potential outliers: Series([], Name: Tumor Volume (mm3), dtype: float64)\n" ] } ], "source": [ "# Put treatments into a list for for loop (and later for plot labels)\n", "treatment_list=[\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"]\n", "\n", "\n", "# Create empty list to fill with tumor vol data (for plotting)\n", "\n", "tumordata=[]\n", "\n", "# Calculate the IQR and quantitatively determine if there are any potential outliers. \n", "for drug in treatment_list: \n", " \n", " # Locate the rows which contain mice on each drug and get the tumor volumes\n", " final_volumes= merge_data.loc[merge_data[\"Drug Regimen\"]== drug,\"Tumor Volume (mm3)\" ]\n", " \n", " # add subset \n", " tumordata.append(final_volumes)\n", " \n", " # Determine outliers using upper and lower bounds\n", " quartiles = final_volumes.quantile([.25,.5,.75])\n", " lowerq = quartiles[0.25]\n", " upperq = quartiles[0.75]\n", " iqr = upperq-lowerq\n", " lower_bound = lowerq - (1.5*iqr)\n", " upper_bound = upperq + (1.5*iqr)\n", " outliers = final_volumes.loc[(final_volumes < lower_bound) | (final_volumes > upper_bound)]\n", " print(f\"{drug}'s potential outliers: {outliers}\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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SPJJkDfAx4C1JHgDe0s0vJk0N+Sxw9uVg2Z+D1XR/pqrmuwZJ0ix4JqYkNcoAl6RGNRXgSf5Rks8k+VqS+5J8Ick/nu+6xiW5NclYN/2FJEfPd039SPJ8kjuT3JPkTwZVd5K3JTloz9JN8p0+lnlDknu7/n/pfpZ7KMkPTtL+3iT/apb1vSnJ52dz3/kw0+d/kouSbE9ydfdYXz/AWq5Mcsqg1jdbzQR4kgDXA7dW1Y9W1SnAbwDHzm9lk6uqn66qJ+e7jj49U1WrqmolvbNu3zeIlVbVjVW12D7gHrR3A7/d9f8zM71zVX26qv7XEOpaUGb5/P8V4Ker6t3Am4CBBXhV/VJV3Teo9c1WMwEOnA3srqpPjzdU1Z3AHUk2Jrk9yd1JzgNIcmKS+5OsT3JXkuuSvKz73blJ7uiW//0kL+naH0rym0luS7I1yWuT/Gn3iv/ebpk99lqSXJHkPXsXO77H1NWxPcn/6Pa0bt7fntYCcBtwPECSl0/Tt1d2e+1XJ3lzkr/sLnB2erfce5Jc0U0fm+T6JF/tfl7ftf+fJNu6vhk/Y5ck30ny0W7ZLyc5tmsfTfLHSf6q+zlzjvtnxrpt5tZuG7y/668k+SXgZ4H/MGEvcX/b1oeTfKX7eXW3zGVJPtRN35rk493v/ybJG7r2w5P8Qfc/vCPJ2XP36Adm0ud/VX0pyYe7beGuJB8BSPJp4EeAG5N8EHgv8MH03um8Ick/S7Kl648/m7B9XdZlxs3dc/ifJ/kvXd/dlOTQbrmJ77Yn3VbnQksBvhLYNkn7s8Dbq+q19P7J/zVJut+dBKyrqh8HngJ+Jcnh9C7Q9XNV9U/onY36byas75tV9TrgS91y7wDOAP7jAdS+HPidqvox4EngXxzAuoYmyRLgXHrH+cP++/bVwCeBHwdOBv4lcBbwIXp7Rnv778BfVNWpwGuBe7v2X6yq04Ax4KIkr+zajwC+3C3/ReBfd+2fBC6vqp+g149XHvADnxuvAT4AnEIvWM6sqivp9fWHu73E6TxVVacDVwCfmGKZQ7plPgBc2rW9D6Db3i8A1nfPg5ZM+vxP8lZ6z6/TgVXAaUneWFXvBR4Fzq6qy4FP09tuVlXVl4DNwBlV9RrgM8CvTVjtjwI/Q++aT38IbOr67pmufW9TbatD11KATyXAbya5C/gzenuP46+A36yqv+ym/5BewJwEfL2q/qZrXw+8ccL6xsPrbmBLVT1dVTuBZzP7seGvd+8WoLcRnjjL9QzLS5PcCXwLGAFu6dr317dfr6q7q+r79MJ4Y/WOSb2byR/fOcDvAVTV81X17a79oiRfBb4M/BC9JyPAPwDje6MT++zNwBVdvTcCRyZ5xQE89rnylap6pOuvO5ndNrBhwu3rpljmc93txD47C/jfAFV1P/ANYMF8dnSA3tr93AHcTm9nYvl+79FzAvCnSe4GPgz82ITf/d+q2k1vW14C3NS1T7VtT7WtDl1LAX4vcNok7e8GRoHTqmoV8Dgwvnex90HuRS+U9ud73e33J0yPzx8CPMee/dbPnszE9TzPHFyDZoae6fruh4HDeHEMfH99u3ffTOy3vh5fkjfRC+TXdXsvd0xY/+568SSFiX32A93yq7qf46vq6b4f6fzpZxuYbtuqKaYn+zsT/8Z023wLpnr+B/itCdvDq6vqqj7W9yngim7P+pfZs6+/B9C92E7cDqfatqfaVoeupQD/c+AlSV54e5LkJ+iFzo6q2t2N7U28ateyJON7KhfQe9t0P3Di+Bgi8PPAX8ygjm8ApyR5SZKj6A05LArdXvFFwIe6sb6jmLpvZ2oj3VBVkiVJjuzWv6uqvpvkZHpDVdO5Gfi34zNJVh1ATQvNdNvWz024vW0G6/0ivRdj0jtqYxnQwlVBJ5rq+f8U8ItJXt61HZ/Jr476NDDxndpRwN9106v3XbwNzQR49wr3dnqn8H8tyb3AZcAXgLEkW+ltpPdPuNt2YHU3BDAC/F5VPQv8AvDZ7u3T9+mNj/VbxzeBa4G7gKvp7TUuGlV1B/BV4F30Ht9UfTtT7wfO7vp8G723rDcBh3T/n/9EbxhlOhd1Nd2V5D56H04tCn1sWy9JsoVeX35wBqv+XWBJ1/fXAO+pqu9Nc58FZT/P/z/qfm7rHt917BnU4/4EePv4h5jdfT+b5Eu0cTnZSS3aU+mTnAh8vjs0TpIWnWb2wCVJe1q0e+CStNi5By5JjTLAJalRBrgkNcoAl6RGGeCS1Kj/D0KhDtaJlNpGAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a box plot of the final tumor volume of each mouse across four regimens of interest\n", "top_4 = ['Capomulin', 'Ramicane', 'Infubinol','Ceftamin']\n", "\n", "best_regimes = cleandata[cleandata[\"Drug Regimen\"].isin([\"Capomulin\", \"Ramicane\", \"Infubinol\", \"Ceftamin\"])]\n", "best_regimes = best_regimes.sort_values([\"Timepoint\"], ascending=True)\n", "best_regimes\n", "\n", "best_regimes_data = best_regimes[[\"Drug Regimen\", \"Mouse ID\", \"Timepoint\", \"Tumor Volume (mm3)\"]]\n", "\n", "best_regimes_data\n", "best_regimens_sort = best_regimes_data.groupby(['Drug Regimen', 'Mouse ID']).last()['Tumor Volume (mm3)']\n", "best_regimens_sort.head()\n", "\n", "best_regimen_df = best_regimens_sort.to_frame()\n", "best_regimen_df\n", "\n", "final_df = best_regimen_df.reset_index()\n", "tumor_lists = final_df.groupby('Drug Regimen')['Tumor Volume (mm3)'].apply(list)\n", "tumor_list_df = pd.DataFrame(tumor_lists)\n", "tumor_list_df = tumor_list_df.reindex(top_4)\n", "tumor_vols = [vol for vol in tumor_list_df['Tumor Volume (mm3)']]\n", "plt.boxplot(tumor_vols, labels=top_4)\n", "plt.ylim(10, 80)\n", "plt.savefig(\"Images/boxplot.png\", bbox_inches = \"tight\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line and Scatter Plots" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin\n", "time_vs_tumer = cleandata[cleandata[\"Mouse ID\"].isin([\"j119\"])]\n", "time_vs_tumer\n", "\n", "time_vs_tumer_data = time_vs_tumer[[\"Mouse ID\", \"Timepoint\", \"Tumor Volume (mm3)\"]]\n", "time_vs_tumer_data\n", "\n", "line_plot_df = time_vs_tumer_data.reset_index()\n", "line_plot_df\n", "\n", "line_plot_final = line_plot_df[[\"Mouse ID\", \"Timepoint\", \"Tumor Volume (mm3)\"]]\n", "line_plot_final\n", "\n", "lines = line_plot_final.plot.line()\n", "\n", "tumorvolume_list = line_plot_final['Tumor Volume (mm3)'].tolist()\n", "timepoint_list = line_plot_final['Timepoint'].tolist()\n", "plt.savefig(\"Images/line_plot.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "x and y must be the same size", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-30-1e4376c9b29e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mcapomulin_grouped_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcapomulin_scatter_plot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Weight (g)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Tumor Volume (mm3)\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen\n", "\n", "capomulin_scatter = cleandata[cleandata[\"Drug Regimen\"].isin([\"Capomulin\"])]\n", "\n", "capomulin_scatter_df = capomulin_scatter[[\"Mouse ID\",\"Weight (g)\", \"Tumor Volume (mm3)\"]]\n", "\n", "capomulin_scatter_plot = capomulin_scatter_df.reset_index()\n", "\n", "capomulin_sorted = capomulin_scatter_plot.sort_values([\"Weight (g)\"], ascending=True)\n", "\n", "capomulin_grouped_weight = capomulin_scatter_plot.groupby(\"Weight (g)\")[\"Tumor Volume (mm3)\"].mean()\n", "\n", "plt.scatter(capomulin_grouped_weight, capomulin_sorted, marker=\"o\", facecolors=\"red\", edgecolors=\"black\")\n", "plt.xlimit(0,100)\n", "plt.ylimit(0,100)\n", "plt.title(\"Mouse Weight vs Average Tumor Volume for Capomulin Regimen\")\n", "plt.xlabel(\"Mouse Weight\")\n", "plt.ylabel(\"Average Tumor Volume\")\n", "plt.savefig(\"Images/scatterplot.png\", bbox_inches = \"tight\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation and Regression" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The correlation between mouse weight and average tumor volume is 0.59\n", "y = 1.33x + 16.25\n", "slope:1.3322996070684112\n", "intercept:16.24834358087913\n", "rvalue (Correlation coefficient):0.5881275121987511\n", "pandas (Correlation coefficient):0.59\n", "stderr:0.04227724572365242\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculate the correlation coefficient and linear regression model \n", "# for mouse weight and average tumor volume for the Capomulin regimen\n", "corr=round(st.pearsonr(cleandata['Weight (g)'],cleandata['Tumor Volume (mm3)'])[0],2)\n", "print(f\"The correlation between mouse weight and average tumor volume is {corr}\")\n", "\n", "x_values = cleandata['Weight (g)']\n", "y_values = cleandata['Tumor Volume (mm3)']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "print(f\"slope:{slope}\")\n", "print(f\"intercept:{intercept}\")\n", "print(f\"rvalue (Correlation coefficient):{rvalue}\")\n", "print(f\"pandas (Correlation coefficient):{corr}\")\n", "print(f\"stderr:{stderr}\")\n", "\n", "fig1, ax1 = plt.subplots(figsize=(15, 10))\n", "plt.scatter(x_values,y_values,s=175, color=\"blue\")\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.xlabel('Weight(g)',fontsize =14)\n", "plt.ylabel('Average Tumore Volume (mm3)',fontsize =14)\n", "ax1.annotate(line_eq, xy=(20, 40), xycoords='data',xytext=(0.8, 0.95), textcoords='axes fraction',horizontalalignment='right', verticalalignment='top',fontsize=30,color=\"red\")\n", "plt.savefig(\"Images/linear_regression.png\", bbox_inches = \"tight\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }