mod-1/day-4/manipulating_data_with_pandas_full.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1 - Manipulating data with Pandas\n",
    "## Pandas Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![austin](http://www.austintexas.gov/sites/default/files/aac_logo.jpg)\n",
    "\n",
    "## Scenario:\n",
    "You have decided that you want to start your own animal shelter, but you want to get an idea of what that will entail and get more information about planning. In this lecture, we are continue to look at a real data set collected by Austin Animal Center over several years and use our pandas skills from the last lecture and learn some new ones in order to explore this data further.\n",
    "\n",
    "#### *Our goals today are to be able to*: <br/>\n",
    "\n",
    "- Get summary info about a dataset and its variables\n",
    "  - Apply and use info, describe and dtypes\n",
    "  - Use mean, min, max, and value_counts \n",
    "- Use apply and applymap to transform columns and create new values\n",
    "\n",
    "- Explain lambda functions and use them to use an apply on a DataFrame\n",
    "- Explain what a groupby object is and split a DataFrame using a groupby\n",
    "- Reshape a DataFrame using joins, merges, pivoting, stacking, and melting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "\n",
    "Let's take a moment to examine the [Austin Animal Center data set](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Outcomes/9t4d-g238/data). What kinds of questions can we ask this data and what kinds of information can we get back?\n",
    "\n",
    "In pairs and as a class, let's generate ideas.\n",
    "\n",
    "## Switch gears\n",
    "\n",
    "Before we answer those questions about the animal shelter data, let's practice on a simpler dataset.\n",
    "Read about this dataset here: https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "![heart-data](images/heartbloodpres.jpeg)\n",
    "\n",
    "The dataset is most often used to practice classification algorithms. Can one develop a model to predict the likelihood of heart disease based on other measurable characteristics? We will return to that specific question in a few weeks, but for now we wish to use the dataset to practice some pandas methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Get summary info about a dataset and its variables\n",
    "\n",
    "Applying and using `info`, `describe`, `mean`, `min`, `max`, `apply`, and `applymap` from the Pandas library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pandas library has several useful tools built in. Let's explore some of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/muratguner/Documents/lectures/kitchen/after_class/day-5-most-pandas\n",
      "total 288\n",
      "drwxr-xr-x   7 muratguner  staff     224 Jun  5 10:54 \u001b[1m\u001b[36m.\u001b[m\u001b[m\n",
      "drwxr-xr-x@ 15 muratguner  staff     480 Jun  5 11:59 \u001b[1m\u001b[36m..\u001b[m\u001b[m\n",
      "drwxr-xr-x   4 muratguner  staff     128 Jun  5 10:22 \u001b[1m\u001b[36m.ipynb_checkpoints\u001b[m\u001b[m\n",
      "drwxr-xr-x   5 muratguner  staff     160 Mar  8 21:06 \u001b[1m\u001b[36mdata\u001b[m\u001b[m\n",
      "drwxr-xr-x   3 muratguner  staff      96 Mar  8 21:06 \u001b[1m\u001b[36mimages\u001b[m\u001b[m\n",
      "-rw-r--r--   1 muratguner  staff   23760 Jun  5 10:54 manipulating_data_with_pandas.ipynb\n",
      "-rw-r--r--   1 muratguner  staff  119838 Jun  5 10:50 manipulating_data_with_pandas_Murat.ipynb\n",
      "ds_chars.csv \u001b[31mheart.csv\u001b[m\u001b[m    states.csv\n"
     ]
    }
   ],
   "source": [
    "!pwd\n",
    "!ls -al\n",
    "\n",
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "﻿age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target\r",
      "\r\n",
      "63,1,3,145,233,1,0,150,0,2.3,0,0,1,1\r",
      "\r\n",
      "37,1,2,130,250,0,1,187,0,3.5,0,0,2,1\r",
      "\r\n",
      "41,0,1,130,204,0,0,172,0,1.4,2,0,2,1\r",
      "\r\n",
      "56,1,1,120,236,0,1,178,0,0.8,2,0,2,1\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n5 data/heart.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "uci = pd.read_csv('data/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a  = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4405585376"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4444017696"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4444017696"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>trestbps</th>\n",
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       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>354</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The `.columns` and `.shape` Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The `.info() `and `.describe()` and `.dtypes` methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas DataFrames have many useful methods! Let's look at `.info()` , `.describe()`, and `dtypes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "# Call the .info() method on our dataset. What do you observe?\n",
    "\n",
    "uci.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the .describe() method on our dataset. What do you observe?\n",
    "\n",
    "uci.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the code below. How does the output differ from info() ?\n",
    "uci.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `.mean()`, .`min()`,` .max()`, `.sum()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The methods `.mean()`, `.min()`, and `.max()` will perform just the way you think they will!\n",
    "\n",
    "Note that these are methods both for Series and for DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7293729372937293"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.ca.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.oldpeak.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Axis Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      42.878571\n",
       "1      43.892857\n",
       "2      39.600000\n",
       "3      42.771429\n",
       "4      50.114286\n",
       "         ...    \n",
       "298    40.514286\n",
       "299    40.085714\n",
       "300    39.885714\n",
       "301    31.585714\n",
       "302    43.000000\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.mean(axis=1) # Try [shift] + [tab] here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### .`value_counts()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a DataFrame _Series_, the `.value_counts()` method will tell you how many of each value you've got."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a82ef28>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "%matplotlib inline\n",
    "uci['age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63, 37, 41, 56, 57, 44, 52, 54, 48, 49, 64, 58, 50, 66, 43, 69, 59,\n",
       "       42, 61, 40, 71, 51, 65, 53, 46, 45, 39, 47, 62, 34, 35, 29, 55, 60,\n",
       "       67, 68, 74, 76, 70, 38, 77])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here!\n",
    "uci['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([77, 76, 74, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58,\n",
       "       57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41,\n",
       "       40, 39, 38, 37, 35, 34, 29])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.sort(uci.age.unique())[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dcba'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'abcd'[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply to Animal Shelter Data\n",
    "Using `.info()` and `.describe()` and `dtypes` what observations can we make about the data?\n",
    "\n",
    "What are the breed value counts?\n",
    "\n",
    "How about age counts for dogs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_outcomes = pd.read_csv('https://data.austintexas.gov/api/views/9t4d-g238/rows.csv?accessType=DOWNLOAD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 117974 entries, 0 to 117973\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Animal ID         117974 non-null  object\n",
      " 1   Name              81011 non-null   object\n",
      " 2   DateTime          117974 non-null  object\n",
      " 3   MonthYear         117974 non-null  object\n",
      " 4   Date of Birth     117974 non-null  object\n",
      " 5   Outcome Type      117966 non-null  object\n",
      " 6   Outcome Subtype   53631 non-null   object\n",
      " 7   Animal Type       117974 non-null  object\n",
      " 8   Sex upon Outcome  117970 non-null  object\n",
      " 9   Age upon Outcome  117919 non-null  object\n",
      " 10  Breed             117974 non-null  object\n",
      " 11  Color             117974 non-null  object\n",
      "dtypes: object(12)\n",
      "memory usage: 10.8+ MB\n"
     ]
    }
   ],
   "source": [
    "animal_outcomes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal ID               0\n",
       "Name                36963\n",
       "DateTime                0\n",
       "MonthYear               0\n",
       "Date of Birth           0\n",
       "Outcome Type            8\n",
       "Outcome Subtype     64343\n",
       "Animal Type             0\n",
       "Sex upon Outcome        4\n",
       "Age upon Outcome       55\n",
       "Breed                   0\n",
       "Color                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_outcomes.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Outcome Subtype</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117974</td>\n",
       "      <td>81011</td>\n",
       "      <td>117974</td>\n",
       "      <td>117974</td>\n",
       "      <td>117974</td>\n",
       "      <td>117966</td>\n",
       "      <td>53631</td>\n",
       "      <td>117974</td>\n",
       "      <td>117970</td>\n",
       "      <td>117919</td>\n",
       "      <td>117974</td>\n",
       "      <td>117974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>105458</td>\n",
       "      <td>18943</td>\n",
       "      <td>97217</td>\n",
       "      <td>97217</td>\n",
       "      <td>6850</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>2571</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>A721033</td>\n",
       "      <td>Max</td>\n",
       "      <td>04/18/2016 12:00:00 AM</td>\n",
       "      <td>04/18/2016 12:00:00 AM</td>\n",
       "      <td>09/01/2015</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Black/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>33</td>\n",
       "      <td>531</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>117</td>\n",
       "      <td>51961</td>\n",
       "      <td>29267</td>\n",
       "      <td>67112</td>\n",
       "      <td>41423</td>\n",
       "      <td>21232</td>\n",
       "      <td>30779</td>\n",
       "      <td>12394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Animal ID   Name                DateTime               MonthYear  \\\n",
       "count     117974  81011                  117974                  117974   \n",
       "unique    105458  18943                   97217                   97217   \n",
       "top      A721033    Max  04/18/2016 12:00:00 AM  04/18/2016 12:00:00 AM   \n",
       "freq          33    531                      39                      39   \n",
       "\n",
       "       Date of Birth Outcome Type Outcome Subtype Animal Type  \\\n",
       "count         117974       117966           53631      117974   \n",
       "unique          6850            9              22           5   \n",
       "top       09/01/2015     Adoption         Partner         Dog   \n",
       "freq             117        51961           29267       67112   \n",
       "\n",
       "       Sex upon Outcome Age upon Outcome                   Breed        Color  \n",
       "count            117970           117919                  117974       117974  \n",
       "unique                5               50                    2571          585  \n",
       "top       Neutered Male           1 year  Domestic Shorthair Mix  Black/White  \n",
       "freq              41423            21232                   30779        12394  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_outcomes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the breed `value_counts`?\n",
    "What's the top breed for adopted dogs?\n",
    "\n",
    "How about outcome counts for dogs?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic Shorthair Mix     30779\n",
       "Pit Bull Mix                8302\n",
       "Labrador Retriever Mix      6643\n",
       "Chihuahua Shorthair Mix     6174\n",
       "Domestic Shorthair          5223\n",
       "Name: Breed, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_outcomes.Breed.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.  Changing data\n",
    "\n",
    "#### DataFrame.applymap() and Series.map()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```.applymap()``` method takes a function as input that it will then apply to every entry in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def successor(x):\n",
    "    try:\n",
    "        return x + 1\n",
    "    except TypeError as e:\n",
    "        return x + 'y'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(successor, [1,2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>146</td>\n",
       "      <td>234</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "      <td>251</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>355</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   64    2   4       146   234    2        1      151      1      3.3      1   \n",
       "1   38    2   3       131   251    1        2      188      1      4.5      1   \n",
       "2   42    1   2       131   205    1        1      173      1      2.4      3   \n",
       "3   57    2   2       121   237    1        2      179      1      1.8      3   \n",
       "4   58    1   1       121   355    1        2      164      2      1.6      3   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   1     2       2  \n",
       "1   1     3       2  \n",
       "2   1     3       2  \n",
       "3   1     3       2  \n",
       "4   1     3       2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.applymap(successor).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal ID           object\n",
       "Name                object\n",
       "DateTime            object\n",
       "MonthYear           object\n",
       "Date of Birth       object\n",
       "Outcome Type        object\n",
       "Outcome Subtype     object\n",
       "Animal Type         object\n",
       "Sex upon Outcome    object\n",
       "Age upon Outcome    object\n",
       "Breed               object\n",
       "Color               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_outcomes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Outcome Type</th>\n",
       "      <th>Outcome Subtype</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A794011y</td>\n",
       "      <td>Chunky</td>\n",
       "      <td>05/08/2019 06:20:00 PMy</td>\n",
       "      <td>05/08/2019 06:20:00 PMy</td>\n",
       "      <td>05/02/2017y</td>\n",
       "      <td>Rto-Adopty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Caty</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>2 yearsy</td>\n",
       "      <td>Domestic Shorthair Mixy</td>\n",
       "      <td>Brown Tabby/Whitey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A776359y</td>\n",
       "      <td>Gizmoy</td>\n",
       "      <td>07/18/2018 04:02:00 PMy</td>\n",
       "      <td>07/18/2018 04:02:00 PMy</td>\n",
       "      <td>07/12/2017y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dogy</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>1 yeary</td>\n",
       "      <td>Chihuahua Shorthair Mixy</td>\n",
       "      <td>White/Browny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A720371y</td>\n",
       "      <td>Moosey</td>\n",
       "      <td>02/13/2016 05:59:00 PMy</td>\n",
       "      <td>02/13/2016 05:59:00 PMy</td>\n",
       "      <td>10/08/2015y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dogy</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>4 monthsy</td>\n",
       "      <td>Anatol Shepherd/Labrador Retrievery</td>\n",
       "      <td>Buffy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A674754y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03/18/2014 11:47:00 AMy</td>\n",
       "      <td>03/18/2014 11:47:00 AMy</td>\n",
       "      <td>03/12/2014y</td>\n",
       "      <td>Transfery</td>\n",
       "      <td>Partnery</td>\n",
       "      <td>Caty</td>\n",
       "      <td>Intact Maley</td>\n",
       "      <td>6 daysy</td>\n",
       "      <td>Domestic Shorthair Mixy</td>\n",
       "      <td>Orange Tabbyy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A689724y</td>\n",
       "      <td>*Donatelloy</td>\n",
       "      <td>10/18/2014 06:52:00 PMy</td>\n",
       "      <td>10/18/2014 06:52:00 PMy</td>\n",
       "      <td>08/01/2014y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Caty</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>2 monthsy</td>\n",
       "      <td>Domestic Shorthair Mixy</td>\n",
       "      <td>Blacky</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117969</th>\n",
       "      <td>A797484y</td>\n",
       "      <td>Duskyy</td>\n",
       "      <td>08/01/2019 07:08:00 PMy</td>\n",
       "      <td>08/01/2019 07:08:00 PMy</td>\n",
       "      <td>04/22/2019y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Caty</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>3 monthsy</td>\n",
       "      <td>Domestic Shorthairy</td>\n",
       "      <td>Orange Tabbyy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117970</th>\n",
       "      <td>A818327y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06/05/2020 01:50:00 PMy</td>\n",
       "      <td>06/05/2020 01:50:00 PMy</td>\n",
       "      <td>06/05/2018y</td>\n",
       "      <td>Euthanasiay</td>\n",
       "      <td>Rabies Risky</td>\n",
       "      <td>Othery</td>\n",
       "      <td>Unknowny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baty</td>\n",
       "      <td>Browny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117971</th>\n",
       "      <td>A646084y</td>\n",
       "      <td>Maxy</td>\n",
       "      <td>06/05/2020 01:02:00 PMy</td>\n",
       "      <td>06/05/2020 01:02:00 PMy</td>\n",
       "      <td>01/17/2009y</td>\n",
       "      <td>Return to Ownery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dogy</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>11 yearsy</td>\n",
       "      <td>Labrador Retriever Mixy</td>\n",
       "      <td>Blacky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117972</th>\n",
       "      <td>A817483y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06/05/2020 01:12:00 PMy</td>\n",
       "      <td>06/05/2020 01:12:00 PMy</td>\n",
       "      <td>05/17/2018y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dogy</td>\n",
       "      <td>Neutered Maley</td>\n",
       "      <td>2 yearsy</td>\n",
       "      <td>Cardigan Welsh Corgiy</td>\n",
       "      <td>Red/Whitey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117973</th>\n",
       "      <td>A813080y</td>\n",
       "      <td>*Mayay</td>\n",
       "      <td>06/05/2020 01:36:00 PMy</td>\n",
       "      <td>06/05/2020 01:36:00 PMy</td>\n",
       "      <td>02/03/2013y</td>\n",
       "      <td>Adoptiony</td>\n",
       "      <td>Fostery</td>\n",
       "      <td>Dogy</td>\n",
       "      <td>Intact Femaley</td>\n",
       "      <td>7 yearsy</td>\n",
       "      <td>Labrador Retriever/Beagley</td>\n",
       "      <td>Black/Whitey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117974 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Animal ID         Name                 DateTime  \\\n",
       "0       A794011y       Chunky  05/08/2019 06:20:00 PMy   \n",
       "1       A776359y       Gizmoy  07/18/2018 04:02:00 PMy   \n",
       "2       A720371y       Moosey  02/13/2016 05:59:00 PMy   \n",
       "3       A674754y          NaN  03/18/2014 11:47:00 AMy   \n",
       "4       A689724y  *Donatelloy  10/18/2014 06:52:00 PMy   \n",
       "...          ...          ...                      ...   \n",
       "117969  A797484y       Duskyy  08/01/2019 07:08:00 PMy   \n",
       "117970  A818327y          NaN  06/05/2020 01:50:00 PMy   \n",
       "117971  A646084y         Maxy  06/05/2020 01:02:00 PMy   \n",
       "117972  A817483y          NaN  06/05/2020 01:12:00 PMy   \n",
       "117973  A813080y       *Mayay  06/05/2020 01:36:00 PMy   \n",
       "\n",
       "                      MonthYear Date of Birth      Outcome Type  \\\n",
       "0       05/08/2019 06:20:00 PMy   05/02/2017y        Rto-Adopty   \n",
       "1       07/18/2018 04:02:00 PMy   07/12/2017y         Adoptiony   \n",
       "2       02/13/2016 05:59:00 PMy   10/08/2015y         Adoptiony   \n",
       "3       03/18/2014 11:47:00 AMy   03/12/2014y         Transfery   \n",
       "4       10/18/2014 06:52:00 PMy   08/01/2014y         Adoptiony   \n",
       "...                         ...           ...               ...   \n",
       "117969  08/01/2019 07:08:00 PMy   04/22/2019y         Adoptiony   \n",
       "117970  06/05/2020 01:50:00 PMy   06/05/2018y       Euthanasiay   \n",
       "117971  06/05/2020 01:02:00 PMy   01/17/2009y  Return to Ownery   \n",
       "117972  06/05/2020 01:12:00 PMy   05/17/2018y         Adoptiony   \n",
       "117973  06/05/2020 01:36:00 PMy   02/03/2013y         Adoptiony   \n",
       "\n",
       "       Outcome Subtype Animal Type Sex upon Outcome Age upon Outcome  \\\n",
       "0                  NaN        Caty   Neutered Maley         2 yearsy   \n",
       "1                  NaN        Dogy   Neutered Maley          1 yeary   \n",
       "2                  NaN        Dogy   Neutered Maley        4 monthsy   \n",
       "3             Partnery        Caty     Intact Maley          6 daysy   \n",
       "4                  NaN        Caty   Neutered Maley        2 monthsy   \n",
       "...                ...         ...              ...              ...   \n",
       "117969             NaN        Caty   Neutered Maley        3 monthsy   \n",
       "117970    Rabies Risky      Othery         Unknowny              NaN   \n",
       "117971             NaN        Dogy   Neutered Maley        11 yearsy   \n",
       "117972             NaN        Dogy   Neutered Maley         2 yearsy   \n",
       "117973         Fostery        Dogy   Intact Femaley         7 yearsy   \n",
       "\n",
       "                                      Breed               Color  \n",
       "0                   Domestic Shorthair Mixy  Brown Tabby/Whitey  \n",
       "1                  Chihuahua Shorthair Mixy        White/Browny  \n",
       "2       Anatol Shepherd/Labrador Retrievery               Buffy  \n",
       "3                   Domestic Shorthair Mixy       Orange Tabbyy  \n",
       "4                   Domestic Shorthair Mixy              Blacky  \n",
       "...                                     ...                 ...  \n",
       "117969                  Domestic Shorthairy       Orange Tabbyy  \n",
       "117970                                 Baty              Browny  \n",
       "117971              Labrador Retriever Mixy              Blacky  \n",
       "117972                Cardigan Welsh Corgiy          Red/Whitey  \n",
       "117973           Labrador Retriever/Beagley        Black/Whitey  \n",
       "\n",
       "[117974 rows x 12 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_outcomes.applymap(successor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.map()` method takes a function as input that it will then apply to every entry in the Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      64\n",
       "1      38\n",
       "2      42\n",
       "3      57\n",
       "4      58\n",
       "       ..\n",
       "298    58\n",
       "299    46\n",
       "300    69\n",
       "301    58\n",
       "302    58\n",
       "Name: age, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci['age'].apply(successor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Anonymous Functions (Lambda Abstraction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple functions can be defined right in the function call. This is called 'lambda abstraction'; the function thus defined has no name and hence is \"anonymous\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anon_function(x):\n",
    "    return round(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_func(x): return round(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.37</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.97</td>\n",
       "      <td>131.62</td>\n",
       "      <td>246.26</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.53</td>\n",
       "      <td>149.65</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.08</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.03</td>\n",
       "      <td>17.54</td>\n",
       "      <td>51.83</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.53</td>\n",
       "      <td>22.91</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>133.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>274.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>166.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>564.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age     sex      cp  trestbps    chol     fbs  restecg  thalach  \\\n",
       "count  303.00  303.00  303.00    303.00  303.00  303.00   303.00   303.00   \n",
       "mean    54.37    0.68    0.97    131.62  246.26    0.15     0.53   149.65   \n",
       "std      9.08    0.47    1.03     17.54   51.83    0.36     0.53    22.91   \n",
       "min     29.00    0.00    0.00     94.00  126.00    0.00     0.00    71.00   \n",
       "25%     47.50    0.00    0.00    120.00  211.00    0.00     0.00   133.50   \n",
       "50%     55.00    1.00    1.00    130.00  240.00    0.00     1.00   153.00   \n",
       "75%     61.00    1.00    2.00    140.00  274.50    0.00     1.00   166.00   \n",
       "max     77.00    1.00    3.00    200.00  564.00    1.00     2.00   202.00   \n",
       "\n",
       "        exang  oldpeak   slope      ca    thal  target  \n",
       "count  303.00   303.00  303.00  303.00  303.00  303.00  \n",
       "mean     0.33     1.04    1.40    0.73    2.31    0.54  \n",
       "std      0.47     1.16    0.62    1.02    0.61    0.50  \n",
       "min      0.00     0.00    0.00    0.00    0.00    0.00  \n",
       "25%      0.00     0.00    1.00    0.00    2.00    0.00  \n",
       "50%      0.00     0.80    1.00    0.00    2.00    1.00  \n",
       "75%      1.00     1.60    2.00    1.00    3.00    1.00  \n",
       "max      1.00     6.20    2.00    4.00    3.00    1.00  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.describe().applymap(lambda joe: round(joe, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "7    0\n",
       "8    0\n",
       "9    2\n",
       "Name: oldpeak, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci['oldpeak'].apply(my_func)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "7    0\n",
       "8    0\n",
       "9    2\n",
       "Name: oldpeak, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.oldpeak.apply(round).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: Use an anonymous function to turn the entries in age to strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      63\n",
       "1      37\n",
       "2      41\n",
       "3      56\n",
       "4      57\n",
       "       ..\n",
       "298    57\n",
       "299    45\n",
       "300    68\n",
       "301    57\n",
       "302    57\n",
       "Name: age, Length: 303, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.age.map(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      63\n",
       "1      37\n",
       "2      41\n",
       "3      56\n",
       "4      57\n",
       "       ..\n",
       "298    57\n",
       "299    45\n",
       "300    68\n",
       "301    57\n",
       "302    57\n",
       "Name: age, Length: 303, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci.age.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply to Animal Shelter Data\n",
    "\n",
    "Use an `apply` to change the dates from strings to datetime objects. Similarly, use an apply to change the ages of the animals from strings to floats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Methods for Re-Organizing DataFrames\n",
    "#### `.groupby()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those of you familiar with SQL have probably used the GROUP BY command. Pandas has this, too.\n",
    "\n",
    "The `.groupby()` method is especially useful for aggregate functions applied to the data grouped in particular ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci.groupby('sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `.groups` and `.get_group()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci.groupby('sex').groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci.groupby('sex').get_group(0) # .tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "uci.groupby('sex').std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: Tell me the average cholesterol level for those with heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply to Animal Shelter Data\n",
    "\n",
    "#### Task 1\n",
    "- Use a groupby to show the average age of the different kinds of animal types.\n",
    "- What about by animal types **and** gender?\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 2:\n",
    "- Create new columns `year` and `month` by using a lambda function x.year on date\n",
    "- Use `groupby` and `.size()` to tell me how many animals are adopted by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Reshaping a DataFrame\n",
    "\n",
    "### `.pivot()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those of you familiar with Excel have probably used Pivot Tables. Pandas has a similar functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci.pivot(values='sex', columns='target').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods for Combining DataFrames: `.join()`, `.merge()`, `.concat()`, `.melt()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `.join()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "toy1 = pd.DataFrame([[63, 142], [33, 47]], columns=['age', 'HP'])\n",
    "toy2 = pd.DataFrame([[63, 100], [33, 200]], columns=['age', 'HP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "toy1.join(toy2.set_index('age'),\n",
    "          on='age',\n",
    "          lsuffix='_A',\n",
    "          rsuffix='_B').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `.merge()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chars = pd.read_csv('data/ds_chars.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = pd.read_csv('data/states.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chars.merge(states,\n",
    "               left_on='home_state',\n",
    "               right_on='state',\n",
    "               how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `pd.concat()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: Look up the documentation on pd.concat (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html) and use it to concatenate ds_chars and states.\n",
    "<br/>\n",
    "Your result should still have only five rows!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([ds_chars, states])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `pd.melt()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melting removes the structure from your DataFrame and puts the data in a 'variable' and 'value' format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_chars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.melt(ds_chars,\n",
    "        id_vars=['name'],\n",
    "        value_vars=['HP', 'home_state'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bringing it all together with the Animal Shelter Data\n",
    "\n",
    "Join the data from the [Austin Animal Shelter Intake dataset](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Intakes/wter-evkm) to the outcomes dataset by Animal ID.\n",
    "\n",
    "Use the dates from each dataset to see how long animals spend in the shelter. Does it differ by time of year? By outcome?\n",
    "\n",
    "The Url for the Intake Dataset is here: https://data.austintexas.gov/api/views/wter-evkm/rows.csv?accessType=DOWNLOAD\n",
    "\n",
    "_Hints_ :\n",
    "- import and clean the intake dataset first\n",
    "- use apply/applymap/lambda to change the variables to their proper format in the intake data\n",
    "- rename the columns in the intake dataset *before* joining\n",
    "- create a new days-in-shelter variable\n",
    "- Notice that some values in \"days_in_shelter\" column are NaN or values < 0 (remove these rows using the \"<\" operator and ~is.na())\n",
    "- Use group_by to get some interesting information about the dataset\n",
    "\n",
    "Make sure to export and save your cleaned dataset. We will use it in a later lecture!\n",
    "\n",
    "use the notation `df.to_csv()` to write the `df` to a csv. Read more about the `to_csv()` documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#code here"
   ]
  }
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