{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79b4924f",
   "metadata": {},
   "source": [
    "# Intro to Data Cleaning\n",
    "\n",
    "\n",
    "\n",
    "This week, we'll be cleaning data.\n",
    "\n",
    "Cleaning data is **labor intensive** and requires making *subjective* choices.  \n",
    "We'll focus on, and assess you on, manipulating data correctly, making reasonable\n",
    "choices, and documenting the choices you make carefully.\n",
    "\n",
    "We'll focus on the programming tools that get used in cleaning data in class\n",
    "this week:\n",
    "- reshaping data\n",
    "- handling missing or incorrect values\n",
    "- renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61d9de64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# sns.set_theme(pallete='colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9064e3",
   "metadata": {},
   "source": [
    "## Tidy Data\n",
    "\n",
    "Read in the three csv files described below and store them in a list of dataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff9c746a",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_base = 'https://raw.githubusercontent.com/rhodyprog4ds/rhodyds/main/data/'\n",
    "\n",
    "datasets = ['study_a.csv','study_b.csv','study_c.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04f5ed37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [pd.read_csv(url_base + current) for current in datasets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45c881e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6c0ebfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2a334b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>treatmenta</th>\n",
       "      <th>treatmentb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>-</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name treatmenta  treatmentb\n",
       "0    John Smith          -           2\n",
       "1      Jane Doe         16          11\n",
       "2  Mary Johnson          3           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76b1cb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>intervention</th>\n",
       "      <th>John Smith</th>\n",
       "      <th>Jane Doe</th>\n",
       "      <th>Mary Johnson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>treatmenta</td>\n",
       "      <td>-</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>treatmentb</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  intervention John Smith  Jane Doe  Mary Johnson\n",
       "0   treatmenta          -        16             3\n",
       "1   treatmentb          2        11             1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffdafb3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>treatment</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>a</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>b</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         person treatment result\n",
       "0    John Smith         a      -\n",
       "1      Jane Doe         a     16\n",
       "2  Mary Johnson         a      3\n",
       "3    John Smith         b      2\n",
       "4      Jane Doe         b     11\n",
       "5  Mary Johnson         b      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b16352",
   "metadata": {},
   "source": [
    "These three all show the same data, but let's say we have two goals:\n",
    "- find the average  effect per person across treatments\n",
    "- find the average effect per treatment across people\n",
    "\n",
    "This works differently for these three versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8c7c884",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/2274987639.py:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_list[0].mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "treatmenta   -54.333333\n",
       "treatmentb     4.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7d2559",
   "metadata": {},
   "source": [
    "we get the average per treatment, but to get the average per person, we have to go across rows, which we can do here, but doesn't work as well with plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9777dd30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/1371725361.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_list[0].mean(axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1    11.0\n",
       "2     1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef20a20f",
   "metadata": {},
   "source": [
    "and this is not well labeled.\n",
    "\n",
    "\n",
    "Let's try the next one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "978399c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/1105758803.py:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_list[1].mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "John Smith      -1.0\n",
       "Jane Doe        13.5\n",
       "Mary Johnson     2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8608b949",
   "metadata": {},
   "source": [
    "Now we get the average per person, but what about per treatment? again we have to go across rows instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cbbb973",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/1112167831.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df_list[1].mean(axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    9.5\n",
       "1    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[1].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e4121d",
   "metadata": {},
   "source": [
    "For the third one, however, we can use groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff3c484d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/906930014.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_list[2].groupby('person').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jane Doe</th>\n",
       "      <td>805.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Smith</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary Johnson</th>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              result\n",
       "person              \n",
       "Jane Doe       805.5\n",
       "John Smith      -1.0\n",
       "Mary Johnson    15.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[2].groupby('person').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39c0b277",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/2480310521.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_list[2].groupby('treatment').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-54.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>703.666667</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               result\n",
       "treatment            \n",
       "a          -54.333333\n",
       "b          703.666667"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[2].groupby('treatment').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ee6c5d",
   "metadata": {},
   "source": [
    "The original [Tidy Data](https://www.jstatsoft.org/article/view/v059i10) paper is worth reading to build a deeper understanding of these ideas.  \n",
    "\n",
    "\n",
    "## Tidying Data\n",
    "\n",
    "\n",
    "Let's reshape the first one to match the tidy one. First, we\n",
    "will save it to a DataFrame, this makes things easier to read\n",
    "and enables us to use the built in help in jupyter, because it can't check types too many levels into a data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b95191ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "treat_df = df_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48600f3",
   "metadata": {},
   "source": [
    "Let's look at it again, so we can see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c86221f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>treatmenta</th>\n",
       "      <th>treatmentb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>-</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name treatmenta  treatmentb\n",
       "0    John Smith          -           2\n",
       "1      Jane Doe         16          11\n",
       "2  Mary Johnson          3           1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "533b9567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'treatmenta', 'treatmentb'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8379b1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a = df_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0852abdb",
   "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>treatment</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>treatmenta</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>treatmenta</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>treatmenta</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>treatmentb</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jane Doe</td>\n",
       "      <td>treatmentb</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mary Johnson</td>\n",
       "      <td>treatmentb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name   treatment result\n",
       "0    John Smith  treatmenta      -\n",
       "1      Jane Doe  treatmenta     16\n",
       "2  Mary Johnson  treatmenta      3\n",
       "3    John Smith  treatmentb      2\n",
       "4      Jane Doe  treatmentb     11\n",
       "5  Mary Johnson  treatmentb      1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a.melt(id_vars=['name'],value_vars=['treatmenta','treatmentb'],\n",
    "          value_name='result',var_name='treatment')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6077f2dd",
   "metadata": {},
   "source": [
    "When we melt a dataset:\n",
    "- the `id_vars` stay as columns\n",
    "- the data from the `value_vars` columns become the values in the `value` column\n",
    "- the column names from the `value_vars` become the values in the `variable` column\n",
    "- we can rename the value and the variable columns.\n",
    "\n",
    "Let's do it for our coffee data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a381dfa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "arabica_data_url = 'https://raw.githubusercontent.com/jldbc/coffee-quality-database/master/data/arabica_data_cleaned.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ac4fdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee_df = pd.read_csv(arabica_data_url)\n",
    "scores_of_interest = ['Balance','Aroma','Body','Aftertaste']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c4a495b",
   "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>Country.of.Origin</th>\n",
       "      <th>Color</th>\n",
       "      <th>Score</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Green</td>\n",
       "      <td>Balance</td>\n",
       "      <td>8.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Green</td>\n",
       "      <td>Balance</td>\n",
       "      <td>8.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Balance</td>\n",
       "      <td>8.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Green</td>\n",
       "      <td>Balance</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Green</td>\n",
       "      <td>Balance</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country.of.Origin  Color    Score  value\n",
       "0          Ethiopia  Green  Balance   8.42\n",
       "1          Ethiopia  Green  Balance   8.42\n",
       "2         Guatemala    NaN  Balance   8.42\n",
       "3          Ethiopia  Green  Balance   8.25\n",
       "4          Ethiopia  Green  Balance   8.33"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee_tall = coffee_df.melt(id_vars=['Country.of.Origin','Color'],\n",
    "                             value_vars=scores_of_interest\n",
    "               ,var_name = 'Score')\n",
    "coffee_tall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0de674",
   "metadata": {},
   "source": [
    "Notice that the actual column names inside the `scores_of_interest` variable become the values in the variable column.\n",
    "\n",
    "This one we can plot in more ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "664a770d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2091/3603277008.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n",
      "/tmp/ipykernel_2091/3603277008.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n",
      "/tmp/ipykernel_2091/3603277008.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n",
      "/tmp/ipykernel_2091/3603277008.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n",
      "/tmp/ipykernel_2091/3603277008.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n",
      "  sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe96985ae50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1249.25x1000 with 4 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-09-26_33_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=coffee_tall, x='value',hue='Country.of.Origin',\n",
    "            col='Score',kind='kde',col_wrap=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25e5a9a",
   "metadata": {},
   "source": [
    "## Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ba5b4e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(368, 44)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_prod = coffee_df[coffee_df['Number.of.Bags']>250]\n",
    "high_prod.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e84da86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1311, 44)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325f655e",
   "metadata": {},
   "source": [
    "We see that filters and reduces.  We can use any boolean expression in the square brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17f0e83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 44)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_balance = coffee_df[coffee_df['Balance']>coffee_df['Balance'].quantile(.75)]\n",
    "top_balance.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda7f187",
   "metadata": {},
   "source": [
    "We can confirm that we got only the top 25% of balance scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40ed73a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Number.of.Bags</th>\n",
       "      <th>Aroma</th>\n",
       "      <th>Flavor</th>\n",
       "      <th>Aftertaste</th>\n",
       "      <th>Acidity</th>\n",
       "      <th>Body</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Uniformity</th>\n",
       "      <th>Clean.Cup</th>\n",
       "      <th>Sweetness</th>\n",
       "      <th>Cupper.Points</th>\n",
       "      <th>Total.Cup.Points</th>\n",
       "      <th>Moisture</th>\n",
       "      <th>Category.One.Defects</th>\n",
       "      <th>Quakers</th>\n",
       "      <th>Category.Two.Defects</th>\n",
       "      <th>altitude_low_meters</th>\n",
       "      <th>altitude_high_meters</th>\n",
       "      <th>altitude_mean_meters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.00000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>273.535714</td>\n",
       "      <td>153.337302</td>\n",
       "      <td>7.808889</td>\n",
       "      <td>7.837659</td>\n",
       "      <td>7.734167</td>\n",
       "      <td>7.824881</td>\n",
       "      <td>7.780159</td>\n",
       "      <td>8.003095</td>\n",
       "      <td>9.885278</td>\n",
       "      <td>9.896667</td>\n",
       "      <td>9.885952</td>\n",
       "      <td>7.865714</td>\n",
       "      <td>84.52504</td>\n",
       "      <td>0.072579</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.150794</td>\n",
       "      <td>2.801587</td>\n",
       "      <td>1343.138168</td>\n",
       "      <td>1424.547053</td>\n",
       "      <td>1383.842611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>284.249040</td>\n",
       "      <td>126.498576</td>\n",
       "      <td>0.355319</td>\n",
       "      <td>0.318172</td>\n",
       "      <td>0.302481</td>\n",
       "      <td>0.320253</td>\n",
       "      <td>0.317712</td>\n",
       "      <td>0.213056</td>\n",
       "      <td>0.363303</td>\n",
       "      <td>0.470514</td>\n",
       "      <td>0.380433</td>\n",
       "      <td>0.383738</td>\n",
       "      <td>1.91563</td>\n",
       "      <td>0.051682</td>\n",
       "      <td>1.249923</td>\n",
       "      <td>0.692216</td>\n",
       "      <td>4.659771</td>\n",
       "      <td>480.927004</td>\n",
       "      <td>503.063351</td>\n",
       "      <td>484.286247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>7.170000</td>\n",
       "      <td>6.920000</td>\n",
       "      <td>7.080000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>5.330000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>5.170000</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>65.750000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>7.647500</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>83.42000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1162.500000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>171.500000</td>\n",
       "      <td>165.500000</td>\n",
       "      <td>7.780000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>84.46000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>378.250000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>85.52000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1260.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>8.670000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>8.580000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>90.58000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2560.000000</td>\n",
       "      <td>2560.000000</td>\n",
       "      <td>2560.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Number.of.Bags       Aroma      Flavor  Aftertaste  \\\n",
       "count   252.000000      252.000000  252.000000  252.000000  252.000000   \n",
       "mean    273.535714      153.337302    7.808889    7.837659    7.734167   \n",
       "std     284.249040      126.498576    0.355319    0.318172    0.302481   \n",
       "min       1.000000        0.000000    5.080000    7.170000    6.920000   \n",
       "25%      65.750000       12.750000    7.647500    7.670000    7.500000   \n",
       "50%     171.500000      165.500000    7.780000    7.830000    7.750000   \n",
       "75%     378.250000      275.000000    8.000000    8.000000    7.920000   \n",
       "max    1260.000000      360.000000    8.750000    8.830000    8.670000   \n",
       "\n",
       "          Acidity        Body     Balance  Uniformity   Clean.Cup   Sweetness  \\\n",
       "count  252.000000  252.000000  252.000000  252.000000  252.000000  252.000000   \n",
       "mean     7.824881    7.780159    8.003095    9.885278    9.896667    9.885952   \n",
       "std      0.320253    0.317712    0.213056    0.363303    0.470514    0.380433   \n",
       "min      7.080000    5.250000    7.830000    6.670000    5.330000    6.670000   \n",
       "25%      7.670000    7.670000    7.830000   10.000000   10.000000   10.000000   \n",
       "50%      7.830000    7.750000    7.920000   10.000000   10.000000   10.000000   \n",
       "75%      8.000000    7.920000    8.080000   10.000000   10.000000   10.000000   \n",
       "max      8.750000    8.580000    8.750000   10.000000   10.000000   10.000000   \n",
       "\n",
       "       Cupper.Points  Total.Cup.Points    Moisture  Category.One.Defects  \\\n",
       "count     252.000000         252.00000  252.000000            252.000000   \n",
       "mean        7.865714          84.52504    0.072579              0.361111   \n",
       "std         0.383738           1.91563    0.051682              1.249923   \n",
       "min         5.170000          77.00000    0.000000              0.000000   \n",
       "25%         7.670000          83.42000    0.000000              0.000000   \n",
       "50%         7.830000          84.46000    0.100000              0.000000   \n",
       "75%         8.080000          85.52000    0.110000              0.000000   \n",
       "max         9.250000          90.58000    0.210000             10.000000   \n",
       "\n",
       "          Quakers  Category.Two.Defects  altitude_low_meters  \\\n",
       "count  252.000000            252.000000           190.000000   \n",
       "mean     0.150794              2.801587          1343.138168   \n",
       "std      0.692216              4.659771           480.927004   \n",
       "min      0.000000              0.000000             1.000000   \n",
       "25%      0.000000              0.000000          1162.500000   \n",
       "50%      0.000000              1.000000          1400.000000   \n",
       "75%      0.000000              3.000000          1695.000000   \n",
       "max      6.000000             40.000000          2560.000000   \n",
       "\n",
       "       altitude_high_meters  altitude_mean_meters  \n",
       "count            190.000000            190.000000  \n",
       "mean            1424.547053           1383.842611  \n",
       "std              503.063351            484.286247  \n",
       "min                1.000000              1.000000  \n",
       "25%             1200.000000           1200.000000  \n",
       "50%             1500.000000           1450.000000  \n",
       "75%             1800.000000           1750.000000  \n",
       "max             2560.000000           2560.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_balance.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f883d71b",
   "metadata": {},
   "source": [
    "We can also use the `isin` method to filter by comparing to an iterable type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cbc85c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_per_country = coffee_df.groupby('Country.of.Origin')['Number.of.Bags'].sum()\n",
    "top_countries = total_per_country.sort_values(ascending=False)[:10].index\n",
    "top_coffee_df = coffee_df[coffee_df['Country.of.Origin'].isin(top_countries)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0233205d",
   "metadata": {},
   "source": [
    "## Questions after class\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### In the data we had about treatment a and treatment b.  Say there was another column that provided information about the age of the people.  Could we create another value variable to or would we put it in the value_vars list along with treatment a and treatmentb?\n",
    "We can have multiple variables as the `id_vars` so that the dataset would have 4 columns instead of 3.\n",
    "\n",
    "\n",
    "### Can we clean data within any row of the data as long as there is a column name for it?\n",
    "Yes\n",
    "\n",
    "### How to clean larger datasets\n",
    "\n",
    "Everything we will learn will work in any context that you can load the dataset into RAM on the computer you are working on; or process it in batches.  \n",
    "\n",
    "\n",
    "### with very large data sets, how can you tell id there are missing values or incorrect types present?\n",
    "\n",
    "We use ways of checking the values, like the `info` method to check the whole column.\n",
    "\n",
    "\n",
    "\n",
    "### Are there any prebuilt methods to identify and remove extreme outliers?\n",
    "\n",
    "There may be, that is a good thing to look in the documentation for.  However, typically the definition of an outlier is best made within context, so the filtering strategy that we just used would be the way to remove them, after doign some EDA.\n",
    "\n",
    "\n",
    "### Is there a specific metric to which a dataset must meet to consider it 'cleaned'?\n",
    "It's \"clean\" when it will work for the analysis you want to do. This means clean enough for one analysis may not be enough for another goal.  Domain expertise is alwasy important.\n",
    "\n",
    "### Are there any packages we may utilize that help us clean data more effectively?\n",
    "\n",
    "\n",
    "### Things we will do later this week:\n",
    "\n",
    "- adding more data to a DataFrame in jupyter notebook?\n",
    "- replace values\n",
    "- deal with NaN values in a dataset?\n",
    "\n",
    "### Next week\n",
    "\n",
    "- What is the difference between different types of merging data frames (inner, outer, left, right, etc)?"
   ]
  }
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