DataFrames_Initial_Lesson_Notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "961e4b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c129427",
   "metadata": {},
   "outputs": [],
   "source": [
    "books = [\n",
    "    {\n",
    "        \"title\": \"Genetic Algorithms and Machine Learning for Programmers\",\n",
    "        \"price\": 36.99,\n",
    "        \"author\": \"Frances Buontempo\"\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"The Visual Display of Quantitative Information\",\n",
    "        \"price\": 38.00,\n",
    "        \"author\": \"Edward Tufte\"\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Practical Object-Oriented Design\",\n",
    "        \"author\": \"Sandi Metz\",\n",
    "        \"price\": 30.47\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Weapons of Math Destruction\",\n",
    "        \"author\": \"Cathy O'Neil\",\n",
    "        \"price\": 17.44\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "020648a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'title': 'Genetic Algorithms and Machine Learning for Programmers',\n",
       "  'price': 36.99,\n",
       "  'author': 'Frances Buontempo'},\n",
       " {'title': 'The Visual Display of Quantitative Information',\n",
       "  'price': 38.0,\n",
       "  'author': 'Edward Tufte'},\n",
       " {'title': 'Practical Object-Oriented Design',\n",
       "  'author': 'Sandi Metz',\n",
       "  'price': 30.47},\n",
       " {'title': 'Weapons of Math Destruction',\n",
       "  'author': \"Cathy O'Neil\",\n",
       "  'price': 17.44}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7e91ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "books = pd.DataFrame(books)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34c7929c",
   "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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Genetic Algorithms and Machine Learning for Pr...</td>\n",
       "      <td>36.99</td>\n",
       "      <td>Frances Buontempo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Visual Display of Quantitative Information</td>\n",
       "      <td>38.00</td>\n",
       "      <td>Edward Tufte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Practical Object-Oriented Design</td>\n",
       "      <td>30.47</td>\n",
       "      <td>Sandi Metz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Weapons of Math Destruction</td>\n",
       "      <td>17.44</td>\n",
       "      <td>Cathy O'Neil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  price             author\n",
       "0  Genetic Algorithms and Machine Learning for Pr...  36.99  Frances Buontempo\n",
       "1     The Visual Display of Quantitative Information  38.00       Edward Tufte\n",
       "2                   Practical Object-Oriented Design  30.47         Sandi Metz\n",
       "3                        Weapons of Math Destruction  17.44       Cathy O'Neil"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a77553c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(books) \n",
    "#this is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19c4e034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.shape \n",
    "#this is a method.  vs a function (where you put the object in the parentheses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bfe4342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "480deb5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122.9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.price.sum()\n",
    "#dot notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "210f2c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    36.99\n",
      "1    38.00\n",
      "2    30.47\n",
      "3    17.44\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(books.price)\n",
    "type(books.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e14bb9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122.9"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books['price'].sum()\n",
    "#bracket notation\n",
    "#df[\"column_name\"]\n",
    "#handy if the column_name has a space in it--gives an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3309f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.mean of 0    36.99\n",
       "1    38.00\n",
       "2    30.47\n",
       "3    17.44\n",
       "Name: price, dtype: float64>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.price.mean #etc etc methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "075e304c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d62021f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.index == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b171e27",
   "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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Genetic Algorithms and Machine Learning for Pr...</td>\n",
       "      <td>36.99</td>\n",
       "      <td>Frances Buontempo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  price             author\n",
       "0  Genetic Algorithms and Machine Learning for Pr...  36.99  Frances Buontempo"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books[books.index == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b264c68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36.99\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books['price'][books.index == 0] #great example, think the pets ex on 20 python data manip exs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d18ae98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title     Genetic Algorithms and Machine Learning for Pr...\n",
       "price                                                 36.99\n",
       "author                                    Frances Buontempo\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb4b74ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.price.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e5c0212",
   "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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Visual Display of Quantitative Information</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Edward Tufte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title  price        author\n",
       "1  The Visual Display of Quantitative Information   38.0  Edward Tufte"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books[books.index == books.price.idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db073d7f",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 1",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-839da0fcde6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbooks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#doesn't work b/c it is looking for a column named '1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "books[books.price.idxmax()] #doesn't work b/c it is looking for a column named '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1efb635",
   "metadata": {},
   "outputs": [],
   "source": [
    "books.iloc[books.price.idxmax()]#this works instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41203fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#or creat a variable:\n",
    "lowest_price_mask = books.index == books.price.idxmin()\n",
    "books[lowest_price_mask]\n",
    "\n",
    "#REMEMBER THIS ONE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "621fb750",
   "metadata": {},
   "outputs": [],
   "source": [
    "books[books.index >= 2] #one of the best pandas tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf153929",
   "metadata": {},
   "outputs": [],
   "source": [
    "sandi_metz_dataframe = books[books.author == 'Sandi Metz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f570c5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sandi_metz_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b06d466",
   "metadata": {},
   "outputs": [],
   "source": [
    "books[books.author.str.contains('ndi Metz')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e780ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.Series(range(1,11))\n",
    "x >= 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9aed4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[x>=5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e0ab07",
   "metadata": {},
   "source": [
    "##  An Example Dataframe\n",
    "\n",
    "The code below will create a data frame that represents grades for multiple students. We pass a dictionary where the keys will correspond to the names of the columns, and the values associated with those keys will make up the data. We will talk in more detail about different ways to create a dataframe in a coming lesson.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e76d8a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "students = ['Sally', 'Jane', 'Suzie', 'Billy', 'Ada', 'John', 'Thomas',\n",
    "            'Marie', 'Albert', 'Richard', 'Isaac', 'Alan']\n",
    "\n",
    "# randomly generate scores for each student for each subject\n",
    "# note that all the values need to have the same length here\n",
    "math_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "english_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "reading_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "\n",
    "df = pd.DataFrame({'name': students,\n",
    "                   'math': math_grades,\n",
    "                   'english': english_grades,\n",
    "                   'reading': reading_grades})\n",
    "\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2fddb132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       name  math  english  reading\n",
      "0     Sally    62       85       80\n",
      "1      Jane    88       79       67\n",
      "2     Suzie    94       74       95\n",
      "3     Billy    98       96       88\n",
      "4       Ada    77       92       98\n",
      "5      John    79       76       93\n",
      "6    Thomas    82       64       81\n",
      "7     Marie    93       63       90\n",
      "8    Albert    92       62       87\n",
      "9   Richard    69       80       94\n",
      "10    Isaac    92       99       93\n",
      "11     Alan    92       62       72\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecdbc1ae",
   "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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5c0aab68",
   "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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  math  english  reading\n",
       "1  Jane    88       79       67"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['name'] == 'Jane']\n",
    "#specific row by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "049048a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    92\n",
       "Name: math, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#albert's math score??\n",
    "df[df.name == \"Albert\"].math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7763a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.name == \"Albert\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8733fb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "albert_mask = df.name == \"Albert\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c5c1d67b",
   "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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  math  english  reading\n",
       "8  Albert    92       62       87"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[albert_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46ce5e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "albert_records = df[albert_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8a42b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    92\n",
       "Name: math, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "albert_records.math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9afd17",
   "metadata": {},
   "source": [
    "# Summarizing Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "82074833",
   "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>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29803c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12 entries, 0 to 11\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   name     12 non-null     object\n",
      " 1   math     12 non-null     int64 \n",
      " 2   english  12 non-null     int64 \n",
      " 3   reading  12 non-null     int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 512.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e2098b6d",
   "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>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>84.833333</td>\n",
       "      <td>77.666667</td>\n",
       "      <td>86.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.134168</td>\n",
       "      <td>13.371158</td>\n",
       "      <td>9.643651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>80.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>92.250000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>93.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            math    english    reading\n",
       "count  12.000000  12.000000  12.000000\n",
       "mean   84.833333  77.666667  86.500000\n",
       "std    11.134168  13.371158   9.643651\n",
       "min    62.000000  62.000000  67.000000\n",
       "25%    78.500000  63.750000  80.750000\n",
       "50%    90.000000  77.500000  89.000000\n",
       "75%    92.250000  86.750000  93.250000\n",
       "max    98.000000  99.000000  98.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1980eaa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c73c7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       object\n",
       "math        int64\n",
       "english     int64\n",
       "reading     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes #super helpful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "386d8e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5135d7be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.shape)\n",
    "#you can call on a tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "813aafaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of rows?\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "47ec733d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number columns?\n",
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a1af0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'math', 'english', 'reading'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1fc5e425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=12, step=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a5640133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME', 'MATH', 'ENGLISH', 'READING']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col.upper() for col in df.columns] #not the alias in the list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4a1c42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [col.upper() for col in df.columns] \n",
    "#capitalizes yourj column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "825cf71d",
   "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>NAME</th>\n",
       "      <th>MATH</th>\n",
       "      <th>ENGLISH</th>\n",
       "      <th>READING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a8fd5d",
   "metadata": {},
   "source": [
    "## Subsetting Dataframes\n",
    "We can see multiple columns in the df by subsetting the df with a list of strings. Next 2 examples are functionally equivalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "be96b7a2",
   "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>NAME</th>\n",
       "      <th>MATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH\n",
       "0     Sally    62\n",
       "1      Jane    88\n",
       "2     Suzie    94\n",
       "3     Billy    98\n",
       "4       Ada    77\n",
       "5      John    79\n",
       "6    Thomas    82\n",
       "7     Marie    93\n",
       "8    Albert    92\n",
       "9   Richard    69\n",
       "10    Isaac    92\n",
       "11     Alan    92"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['NAME','MATH']]\n",
    "#DOUBLE brackets; otherwise it would be like searching for a tuple with those names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4dd536df",
   "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>NAME</th>\n",
       "      <th>MATH</th>\n",
       "      <th>ENGLISH</th>\n",
       "      <th>READING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NAME  MATH  ENGLISH  READING\n",
       "0  Sally    62       85       80\n",
       "1   Jane    88       79       67\n",
       "2  Suzie    94       74       95\n",
       "3  Billy    98       96       88\n",
       "4    Ada    77       92       98"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "29171a1c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'name_math_columns' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-10b156ef23d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname_math_columns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'name_math_columns' is not defined"
     ]
    }
   ],
   "source": [
    "math_df = df[name_math_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a3de1ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     62\n",
       "1     88\n",
       "2     94\n",
       "3     98\n",
       "4     77\n",
       "5     79\n",
       "6     82\n",
       "7     93\n",
       "8     92\n",
       "9     69\n",
       "10    92\n",
       "11    92\n",
       "Name: MATH, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MATH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5ac54766",
   "metadata": {},
   "outputs": [],
   "source": [
    "#.head, .tail, .sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2525443",
   "metadata": {},
   "source": [
    "Like numpy arrays and pandas Series, pandas dataframes can also be indexed into with a boolean series.\n",
    "\n",
    "For example, suppose we wanted to find the observations in our dataframe where the math grade is below an 80. We know that we can produce a boolean series of values using a vectorized comparison operation:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8ad5d7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4      True\n",
       "5      True\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9      True\n",
       "10    False\n",
       "11    False\n",
       "Name: MATH, dtype: bool"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MATH < 80 \n",
    "#the boolean mask that meets the condition\n",
    "#indexing syntax\n",
    "#df[df.column operator operand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4f0c90d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>NAME</th>\n",
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       "      <td>Suzie</td>\n",
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       "      <td>95</td>\n",
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       "      <th>5</th>\n",
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       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       NAME  MATH  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "2     Suzie    94       74       95\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using multiple operators\n",
    "#df[(mask1)|(mask2)]\n",
    "df[(df.MATH < 80) | (df.READING >90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3a7aeb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#or make variables \n",
    "#df[math_less_than_80 & reading_greater_than_90]\n",
    "#for instance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22818717",
   "metadata": {},
   "source": [
    "## Dropping and Renaming Columns\n",
    "`.drop` method; `.rename` methos\n",
    "\n",
    "They don't change the original df.  There is an argument that can be passed that will change it (inside the parentheses).\n",
    "\n",
    "`.drop(['column'], inplace=True`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4b990a30",
   "metadata": {},
   "outputs": [
    {
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       "      <td>93</td>\n",
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      "text/plain": [
       "       NAME  MATH  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "31efb243",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    MATH  ENGLISH  READING\n",
       "0     62       85       80\n",
       "1     88       79       67\n",
       "2     94       74       95\n",
       "3     98       96       88\n",
       "4     77       92       98\n",
       "5     79       76       93\n",
       "6     82       64       81\n",
       "7     93       63       90\n",
       "8     92       62       87\n",
       "9     69       80       94\n",
       "10    92       99       93\n",
       "11    92       62       72"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns = ['NAME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4b5a0692",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "34079d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = df.copy()\n",
    "#create a copy of the og df\n",
    "#otherwise, any change to one will also change the other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "13f97fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH\n",
       "0     Sally    62\n",
       "1      Jane    88\n",
       "2     Suzie    94\n",
       "3     Billy    98\n",
       "4       Ada    77\n",
       "5      John    79\n",
       "6    Thomas    82\n",
       "7     Marie    93\n",
       "8    Albert    92\n",
       "9   Richard    69\n",
       "10    Isaac    92\n",
       "11     Alan    92"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.drop returns a copy, so reassigning is the 1st way to hold the new copy\n",
    "copy = copy.drop(columns=['ENGLISH','READING'])\n",
    "\n",
    "copy\n",
    "#then i can go back to the previous cell to get back everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "de37dc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "copy.drop(columns=['MATH'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a47dc4c5",
   "metadata": {},
   "outputs": [
    {
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      ]
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     "execution_count": 61,
     "metadata": {},
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    "copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ff83d630",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       NAME  MATH  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3ae5bee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>87</td>\n",
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       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  ENGLISH  READING\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.rename to reassign specific columns.  pass in a dictionary with same # of values\n",
    "df.rename(columns={'NAME':'name', 'MATH': 'math'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "123ed6a6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
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       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "      <td>92</td>\n",
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       "      <th>11</th>\n",
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       "      <td>92</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student  MATH\n",
       "0     Sally    62\n",
       "1      Jane    88\n",
       "2     Suzie    94\n",
       "3     Billy    98\n",
       "4       Ada    77\n",
       "5      John    79\n",
       "6    Thomas    82\n",
       "7     Marie    93\n",
       "8    Albert    92\n",
       "9   Richard    69\n",
       "10    Isaac    92\n",
       "11     Alan    92"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['ENGLISH', 'READING']).rename(columns={'NAME':'student'})\n",
    "#NOTE HOW .DROP TAKES A LIST AND RENAME TAKES A DICTIONARY!!!!WOW"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47489ab",
   "metadata": {},
   "source": [
    "## Creating New Columns\n",
    "\n",
    "Usually a new column is based on existing ones\n",
    "\n",
    "EG, new boolean col that indicates whether each student is passing math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "587613bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "11     True\n",
       "Name: MATH, dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MATH > 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0c2f80f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['passing_math']  = df.MATH > 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "481f35cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING  passing_math\n",
       "0     Sally    62       85       80         False\n",
       "1      Jane    88       79       67          True\n",
       "2     Suzie    94       74       95          True\n",
       "3     Billy    98       96       88          True\n",
       "4       Ada    77       92       98          True\n",
       "5      John    79       76       93          True\n",
       "6    Thomas    82       64       81          True\n",
       "7     Marie    93       63       90          True\n",
       "8    Albert    92       62       87          True\n",
       "9   Richard    69       80       94         False\n",
       "10    Isaac    92       99       93          True\n",
       "11     Alan    92       62       72          True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "16edec55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.passing_math.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3f202b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.passing_math.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205e8fa5",
   "metadata": {},
   "source": [
    "Assign also works by returning a new df w/o altering the original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "56d49bdc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
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      "text/plain": [
       "       NAME  MATH  ENGLISH  READING  passing_math  passing_english\n",
       "0     Sally    62       85       80         False             True\n",
       "1      Jane    88       79       67          True             True\n",
       "2     Suzie    94       74       95          True             True\n",
       "3     Billy    98       96       88          True             True\n",
       "4       Ada    77       92       98          True             True\n",
       "5      John    79       76       93          True             True\n",
       "6    Thomas    82       64       81          True            False\n",
       "7     Marie    93       63       90          True            False\n",
       "8    Albert    92       62       87          True            False\n",
       "9   Richard    69       80       94         False             True\n",
       "10    Isaac    92       99       93          True             True\n",
       "11     Alan    92       62       72          True            False"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.assign(passing_english=df.ENGLISH >= 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "106a0d36",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING  passing_math   school\n",
       "0     Sally    62       85       80         False  S.A. HS\n",
       "1      Jane    88       79       67          True  S.A. HS\n",
       "2     Suzie    94       74       95          True  S.A. HS\n",
       "3     Billy    98       96       88          True  S.A. HS\n",
       "4       Ada    77       92       98          True  S.A. HS\n",
       "5      John    79       76       93          True  S.A. HS\n",
       "6    Thomas    82       64       81          True  S.A. HS\n",
       "7     Marie    93       63       90          True  S.A. HS\n",
       "8    Albert    92       62       87          True  S.A. HS\n",
       "9   Richard    69       80       94         False  S.A. HS\n",
       "10    Isaac    92       99       93          True  S.A. HS\n",
       "11     Alan    92       62       72          True  S.A. HS"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['school'] = 'S.A. HS'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "431ba596",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>False</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING  passing_math  school\n",
       "0     Sally    62       85       80         False  Codeup\n",
       "1      Jane    88       79       67          True  Codeup\n",
       "2     Suzie    94       74       95          True  Codeup\n",
       "3     Billy    98       96       88          True  Codeup\n",
       "4       Ada    77       92       98          True  Codeup\n",
       "5      John    79       76       93          True  Codeup\n",
       "6    Thomas    82       64       81          True  Codeup\n",
       "7     Marie    93       63       90          True  Codeup\n",
       "8    Albert    92       62       87          True  Codeup\n",
       "9   Richard    69       80       94         False  Codeup\n",
       "10    Isaac    92       99       93          True  Codeup\n",
       "11     Alan    92       62       72          True  Codeup"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['school'] = 'Codeup'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a04c82",
   "metadata": {},
   "source": [
    "## Sorting DFs\n",
    "\n",
    "`.sort_values()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c7249976",
   "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",
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       "    }\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>NAME</th>\n",
       "      <th>MATH</th>\n",
       "      <th>ENGLISH</th>\n",
       "      <th>READING</th>\n",
       "      <th>passing_math</th>\n",
       "      <th>school</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>False</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  MATH  ENGLISH  READING  passing_math  school\n",
       "10    Isaac    92       99       93          True  Codeup\n",
       "3     Billy    98       96       88          True  Codeup\n",
       "4       Ada    77       92       98          True  Codeup\n",
       "0     Sally    62       85       80         False  Codeup\n",
       "9   Richard    69       80       94         False  Codeup\n",
       "1      Jane    88       79       67          True  Codeup\n",
       "5      John    79       76       93          True  Codeup\n",
       "2     Suzie    94       74       95          True  Codeup\n",
       "6    Thomas    82       64       81          True  Codeup\n",
       "7     Marie    93       63       90          True  Codeup\n",
       "8    Albert    92       62       87          True  Codeup\n",
       "11     Alan    92       62       72          True  Codeup"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"ENGLISH\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3653a381",
   "metadata": {},
   "source": [
    "## Chaining DF Methods\n",
    "\n",
    "Bc most methods return a DF, it's common to chain them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "124b45c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hELLO'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#simple ex\n",
    "\"hello\".capitalize().swapcase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5623ec5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    Ada\n",
       "Name: NAME, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.ENGLISH > 90].sort_values(by='ENGLISH').head(1).NAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f9a2a49b",
   "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>NAME</th>\n",
       "      <th>MATH</th>\n",
       "      <th>ENGLISH</th>\n",
       "      <th>READING</th>\n",
       "      <th>passing_math</th>\n",
       "      <th>school</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>Codeup</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NAME  MATH  ENGLISH  READING  passing_math  school\n",
       "3   Billy    98       96       88          True  Codeup\n",
       "4     Ada    77       92       98          True  Codeup\n",
       "10  Isaac    92       99       93          True  Codeup"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.ENGLISH  > 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8507aa62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    Ada\n",
       "Name: NAME, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.ENGLISH >90].sort_values(by='ENGLISH').head(1).NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdca9672",
   "metadata": {},
   "source": [
    "### SELF CHECK: WHERE'S YOUR BOOLEAN MASK ABOVE??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b32b906",
   "metadata": {},
   "source": [
    "Let's break down the above expression piece by piece:\n",
    "\n",
    "1. df: our initial variable that holds our dataframe\n",
    "2. [df.ENGLISH > 90]: here we subset the datframe to find just the rows where the english grade is greater than 90\n",
    "3. .sort_values(by='ENGLISH'): now we take the remaining rows and sort them by the english grade\n",
    "4. .head(1): take just the first record. Because we sorted previously, this will give us the student with lowest english grade\n",
    "5. .NAME: extract just the name part of the record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d223b77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a092edd6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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