ErrorsAndExplanations.ipynb 106 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55078a6a",
   "metadata": {},
   "source": [
    "# Manipulating DataFrames\n",
    "\n",
    "Some examples to help understand common errors in dataframes, and the difference between a dataframe and a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b33c47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species\n",
       "78            6.0          2.9           4.5          1.5  versicolor\n",
       "117           7.7          3.8           6.7          2.2   virginica\n",
       "88            5.6          3.0           4.1          1.3  versicolor\n",
       "30            4.8          3.1           1.6          0.2      setosa\n",
       "72            6.3          2.5           4.9          1.5  versicolor\n",
       "60            5.0          2.0           3.5          1.0  versicolor\n",
       "135           7.7          3.0           6.1          2.3   virginica\n",
       "41            4.5          2.3           1.3          0.3      setosa\n",
       "75            6.6          3.0           4.4          1.4  versicolor\n",
       "56            6.3          3.3           4.7          1.6  versicolor"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starting with a simple example dataset we've seen before: the iris dataset.\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "iris_dataframe = sns.load_dataset(\"iris\")\n",
    "iris_dataframe.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f3d77a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1 -> what's the result of this command?\n",
    "# A -> This gives a series (single column).\n",
    "# Note that a Dataframe is collection of series (one for each column)\n",
    "# which are aligned on the same index.\n",
    "iris_dataframe['sepal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41f4ec0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width\n",
       "0             5.1          3.5\n",
       "1             4.9          3.0\n",
       "2             4.7          3.2\n",
       "3             4.6          3.1\n",
       "4             5.0          3.6\n",
       "..            ...          ...\n",
       "145           6.7          3.0\n",
       "146           6.3          2.5\n",
       "147           6.5          3.0\n",
       "148           6.2          3.4\n",
       "149           5.9          3.0\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q2 -> what's the result of this command?\n",
    "# A -> This gives a two-column dataframe.\n",
    "iris_dataframe[['sepal_length', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae132736",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length\n",
       "0             5.1\n",
       "1             4.9\n",
       "2             4.7\n",
       "3             4.6\n",
       "4             5.0\n",
       "..            ...\n",
       "145           6.7\n",
       "146           6.3\n",
       "147           6.5\n",
       "148           6.2\n",
       "149           5.9\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q3 -> what's the result of this command?\n",
    "# A -> This gives one two-column dataframe.\n",
    "# Notes: in general this column-selection syntax can be broken down as:\n",
    "#    iris_dataframe[..something...]\n",
    "# The outer set of square brackets indicate that we are selecting some data.\n",
    "# In python this is referred to as slicing.\n",
    "# If 'something' is just one string, we get back one column as a series.\n",
    "# If 'something' is a list of strings, we get back a dataframe with the\n",
    "# selected columns.\n",
    "# If this list has only one entry, we still get a dataframe, it just has\n",
    "# only one column.\n",
    "# So, the two square brackets [[...]] mean we are selecting with a list of\n",
    "# length one.\n",
    "\n",
    "iris_dataframe[['sepal_length']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f34c0121",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['sepal_lingth'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_9573/2938404256.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# the index (row labels) and columns (column labels) as 'indexes' of some sort,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# so an error message 'not in index' may indicate a failed column lookup.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sepal_lingth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal_width'\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[0m",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\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-> 3461\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m   3462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\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[0;32m-> 1377\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\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   1378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['sepal_lingth'] not in index\""
     ]
    }
   ],
   "source": [
    "# Q4 -> what's the result of this command?\n",
    "# A -> KeyError. KeyError is common for lookup errors, we couldn't find the\n",
    "# column name. Note the error message 'not in index'; pandas considers both\n",
    "# the index (row labels) and columns (column labels) as 'indexes' of some sort,\n",
    "# so an error message 'not in index' may indicate a failed column lookup.\n",
    "iris_dataframe[['sepal_lingth', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8991f60a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that we still get KeyErrors in the next two cases, but sometimes\n",
    "# the error message is slightly different (in this case, it is actually\n",
    "# more informative!).\n",
    "iris_dataframe[['sepal_lingth', 'sepal_wedth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49073fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another error... one thing to note: you can mostly ignore the initial part\n",
    "# of the error message which refers to pandas internal code. This is called\n",
    "# a stack trace. In some cases it will be useful in helping you debug your own\n",
    "# code, but for simple one-line operations like this, the stack trace just\n",
    "# looks at pandas internal code. To figure out what has gone wrong in this case\n",
    "# focus on the error message itself (right at the bottom of all this output).\n",
    "iris_dataframe[['sepal_lingth', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9370647",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['sepal_lingth'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_9573/774806270.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sepal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal_width'\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     14\u001b[0m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'petal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal_width'\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[0;32m---> 15\u001b[0;31m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sepal_lingth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal_width'\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[0m\u001b[1;32m     16\u001b[0m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sepal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'petal_width'\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     17\u001b[0m \u001b[0miris_dataframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sepal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'petal_length'\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[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\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-> 3461\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m   3462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\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[0;32m-> 1377\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\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   1378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['sepal_lingth'] not in index\""
     ]
    }
   ],
   "source": [
    "# This piece of code is a bit contrived, but here is a case where looking\n",
    "# at the stack trace is helpful.\n",
    "# The very first part of the trace refers to the code in our notebook cell:\n",
    "#\n",
    "#       1 iris_dataframe[['sepal_length', 'sepal_width']]\n",
    "#       2 iris_dataframe[['petal_length', 'sepal_width']]\n",
    "# ----> 3 iris_dataframe[['sepal_lingth', 'sepal_width']]\n",
    "#       4 iris_dataframe[['sepal_length', 'petal_width']]\n",
    "#       5 iris_dataframe[['sepal_length', 'petal_length']]\n",
    "#\n",
    "# This indicates that the error originated from the 3rd line of our code.\n",
    "\n",
    "iris_dataframe[['sepal_length', 'sepal_width']]\n",
    "iris_dataframe[['petal_length', 'sepal_width']]\n",
    "iris_dataframe[['sepal_lingth', 'sepal_width']]\n",
    "iris_dataframe[['sepal_length', 'petal_width']]\n",
    "iris_dataframe[['sepal_length', 'petal_length']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90dce1de",
   "metadata": {},
   "source": [
    "# Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0783d68c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataframe.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a38303b",
   "metadata": {},
   "source": [
    "**Question**: if there are nan values would the datatype be 'object'?\n",
    "\n",
    "**Answer**: not necessarily, numeric columns can still have NaN values. More specifically:\n",
    "\n",
    "* Floating-point (type = 'float') columns can have NaN values.\n",
    "* An integer column **cannot** have NaN values. If you include NaN values in an integer column the entire column is (automatically) turned into floating point. This is the result of some internal implementation in pandas.\n",
    "* Object columns typically indicate string values, a common example is un-converted datetimes read from csv. Use pd.to_datetime to convert these to pandas' native type so that they are handled properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c071566",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         setosa\n",
       "1         setosa\n",
       "2         setosa\n",
       "3         setosa\n",
       "4         setosa\n",
       "         ...    \n",
       "145    virginica\n",
       "146    virginica\n",
       "147    virginica\n",
       "148    virginica\n",
       "149    virginica\n",
       "Name: species, Length: 150, dtype: category\n",
       "Categories (3, object): ['setosa', 'versicolor', 'virginica']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is also a categorical type conversion we can do:\n",
    "iris_dataframe[\"species\"].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc07ad2e",
   "metadata": {},
   "source": [
    "# Plots\n",
    "\n",
    "Similar errors occur when using pandas + seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51755d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa44d9faa30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 444.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q5 -> what's the result of this command?\n",
    "# A -> a scatter plot (this is the default for relplot). In this case\n",
    "# the function call indicates that the plot data should be sourced from\n",
    "# the 'iris_dataframe' dataframe. This means all columns referred to must\n",
    "# be present in this dataframe. Here the x/y position of points comes\n",
    "# from sepal_width/sepal_length, and the category colour comes from the\n",
    "# species column. Hence we get 3 unique colours.\n",
    "sns.relplot(\n",
    "    data=iris_dataframe, # source dataframe\n",
    "    x=\"sepal_width\",\n",
    "    y=\"sepal_length\",\n",
    "    hue='species',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ea6bc0e",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret value `sepal_wodth` for parameter `x`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_9573/3922716469.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# first ----> in the stack trace to figure out which line of your code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# the error came from, and read the final error message carefully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m sns.relplot(\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miris_dataframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sepal_wodth\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\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[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mrelplot\u001b[0;34m(x, y, hue, size, style, data, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, units, **kwargs)\u001b[0m\n\u001b[1;32m    945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m     \u001b[0;31m# Use the full dataset to map the semantics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m     p = plotter(\n\u001b[0m\u001b[1;32m    948\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m         \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables, x_bins, y_bins, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend)\u001b[0m\n\u001b[1;32m    585\u001b[0m         )\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 587\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\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    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\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    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\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    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_semantic_mappings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_format\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"long\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m             plot_data, variables = self._assign_variables_longform(\n\u001b[0m\u001b[1;32m    669\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m             )\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m                 \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret value `{val}` for parameter `{key}`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret value `sepal_wodth` for parameter `x`"
     ]
    }
   ],
   "source": [
    "# Q6 -> what's the result of this command?\n",
    "# A -> ValueError. We mis-spelled a column, but with seaborn we get\n",
    "# a ValueError instead of a KeyError. Why? I'm not really sure, it's\n",
    "# just a different choice made by the developers of seaborn vs. pandas.\n",
    "# But the take-home message is: ignore the error type. Look for the\n",
    "# first ----> in the stack trace to figure out which line of your code\n",
    "# the error came from, and read the final error message carefully.\n",
    "sns.relplot(\n",
    "    data=iris_dataframe,\n",
    "    x=\"sepal_wodth\",\n",
    "    y=\"sepal_length\",\n",
    "    hue='species',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b141373e",
   "metadata": {},
   "source": [
    "# KeyError vs ValueError\n",
    "\n",
    "**Question**: What's the difference between key error and value error?\n",
    "\n",
    "**Answer**: All python errors have types. Typically KeyError means \"you\n",
    "tried to look something up, but I couldn't find it\" and ValueError means\n",
    "\"you gave me some input I don't know how to deal with\". More important\n",
    "that the type, though, is the error message itself, which should give you\n",
    "a clue as to the root cause of the error."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f67b8a",
   "metadata": {},
   "source": [
    "# Split, Fit, Predict, Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f788487",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46226fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "Name: species, Length: 150, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gives a series of true/false values. Specifically, we extract the 'species'\n",
    "# column and compare every entry to the string 'versicolor'.\n",
    "iris_dataframe['species'] == 'versicolor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f0a9108",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_versicolor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
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       "      <td>6.9</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "49            5.0          3.3           1.4          0.2      setosa   \n",
       "97            6.2          2.9           4.3          1.3  versicolor   \n",
       "118           7.7          2.6           6.9          2.3   virginica   \n",
       "58            6.6          2.9           4.6          1.3  versicolor   \n",
       "141           6.9          3.1           5.1          2.3   virginica   \n",
       "104           6.5          3.0           5.8          2.2   virginica   \n",
       "50            7.0          3.2           4.7          1.4  versicolor   \n",
       "134           6.1          2.6           5.6          1.4   virginica   \n",
       "139           6.9          3.1           5.4          2.1   virginica   \n",
       "120           6.9          3.2           5.7          2.3   virginica   \n",
       "\n",
       "     is_versicolor  \n",
       "49               0  \n",
       "97               1  \n",
       "118              0  \n",
       "58               1  \n",
       "141              0  \n",
       "104              0  \n",
       "50               1  \n",
       "134              0  \n",
       "139              0  \n",
       "120              0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the Logistic Regression notebook.\n",
    "# Here we create another column using the boolean column from the previous cell.\n",
    "iris_dataframe['is_versicolor'] = (iris_dataframe['species'] == 'versicolor')\n",
    "# We then convert it to a 0/1 value (integer type). This allowed me to plot\n",
    "# the classification on the y-axis in the examples.\n",
    "iris_dataframe['is_versicolor'] = iris_dataframe['is_versicolor'].astype(int)\n",
    "# Select 10 random columns. Otherwise we'll only see the top and bottom 10 values.\n",
    "# None of these are 'versicolor' ... so my fancy new column would appear\n",
    "# to be all zeroes!\n",
    "iris_dataframe.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e524f0ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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