{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "af6850ba-bf23-4bbd-b58c-fea6b29e9500",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "# Review\n",
    "\n",
    "Today's class was review.\n",
    "\n",
    "[Recording of lecture from 1/28/2022](https://uci.zoom.us/rec/share/OxfhTaj6tVZ4ysRJC_U2Nj3vGo0Hiqn-vm8WgGK-ANcfV5hahZLYPRCQFj60xI2a.MTVnvxxTkIXzkvLS?startTime=1643385481000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "bf0ed2ee-ca49-4236-976e-ddd5a4c7978d",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 300,
    "execution_start": 1643385441255,
    "source_hash": "7baafd37",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionaries, and their relationship to pandas Series and pandas DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": "e17de02e-bfb1-44b7-8007-98ecad8f04c7",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643385790842,
    "source_hash": "6a294ecd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "d = {\"a\":5,\"b\":10,\"chris\":30}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "d9c35a6f-e750-48e9-9ad6-b181ca907ae7",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 345,
    "execution_start": 1643385807114,
    "source_hash": "15f90960",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"b\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "75287e85-6ff8-4d16-9a24-178df9b8875c",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_start": 1643385840275,
    "source_hash": "62db6b48",
    "tags": []
   },
   "outputs": [],
   "source": [
    "e = {\"a\":[5,6,7],\"b\":10,\"chris\":[30,2.4,-20]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making a DataFrame from a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "ccb3054b-9dce-4cc3-9093-64fcac225051",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643385925252,
    "source_hash": "e08c1f7f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": "cdc110ed-cbdd-4afc-ba91-0bc9b5420537",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 304,
    "execution_start": 1643385956633,
    "source_hash": "f804c160",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b  chris\n",
       "0  5  10   30.0\n",
       "1  6  10    2.4\n",
       "2  7  10  -20.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "b7347bc9-62cb-49f2-abc7-f176f0f8ff96",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643385932131,
    "source_hash": "b165b385",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    6\n",
       "2    7\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "540229e4-e89b-448f-8305-dd73476a3e2d",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1643385940313,
    "source_hash": "38ded7f3",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    6\n",
       "2    7\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making a Series from a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cell_id": "48e7e248-31bd-46f2-92b7-49aad02a2d0e",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 18,
    "execution_start": 1643385881115,
    "source_hash": "ef1cac7a",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a         5\n",
       "b        10\n",
       "chris    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "3d472dc7-3ab9-4753-b973-bb384589c577",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 21,
    "execution_start": 1643385993312,
    "source_hash": "f804c160",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 3,
       "columns": [
        {
         "dtype": "int64",
         "name": "a",
         "stats": {
          "histogram": [
           {
            "bin_end": 5.2,
            "bin_start": 5,
            "count": 1
           },
           {
            "bin_end": 5.4,
            "bin_start": 5.2,
            "count": 0
           },
           {
            "bin_end": 5.6,
            "bin_start": 5.4,
            "count": 0
           },
           {
            "bin_end": 5.8,
            "bin_start": 5.6,
            "count": 0
           },
           {
            "bin_end": 6,
            "bin_start": 5.8,
            "count": 0
           },
           {
            "bin_end": 6.2,
            "bin_start": 6,
            "count": 1
           },
           {
            "bin_end": 6.4,
            "bin_start": 6.2,
            "count": 0
           },
           {
            "bin_end": 6.6,
            "bin_start": 6.4,
            "count": 0
           },
           {
            "bin_end": 6.8,
            "bin_start": 6.6,
            "count": 0
           },
           {
            "bin_end": 7,
            "bin_start": 6.8,
            "count": 1
           }
          ],
          "max": "7",
          "min": "5",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "b",
         "stats": {
          "histogram": [
           {
            "bin_end": 9.6,
            "bin_start": 9.5,
            "count": 0
           },
           {
            "bin_end": 9.7,
            "bin_start": 9.6,
            "count": 0
           },
           {
            "bin_end": 9.8,
            "bin_start": 9.7,
            "count": 0
           },
           {
            "bin_end": 9.9,
            "bin_start": 9.8,
            "count": 0
           },
           {
            "bin_end": 10,
            "bin_start": 9.9,
            "count": 0
           },
           {
            "bin_end": 10.1,
            "bin_start": 10,
            "count": 3
           },
           {
            "bin_end": 10.2,
            "bin_start": 10.1,
            "count": 0
           },
           {
            "bin_end": 10.3,
            "bin_start": 10.2,
            "count": 0
           },
           {
            "bin_end": 10.4,
            "bin_start": 10.3,
            "count": 0
           },
           {
            "bin_end": 10.5,
            "bin_start": 10.4,
            "count": 0
           }
          ],
          "max": "10",
          "min": "10",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "float64",
         "name": "chris",
         "stats": {
          "histogram": [
           {
            "bin_end": -15,
            "bin_start": -20,
            "count": 1
           },
           {
            "bin_end": -10,
            "bin_start": -15,
            "count": 0
           },
           {
            "bin_end": -5,
            "bin_start": -10,
            "count": 0
           },
           {
            "bin_end": 0,
            "bin_start": -5,
            "count": 0
           },
           {
            "bin_end": 5,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 10,
            "bin_start": 5,
            "count": 0
           },
           {
            "bin_end": 15,
            "bin_start": 10,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 15,
            "count": 0
           },
           {
            "bin_end": 25,
            "bin_start": 20,
            "count": 0
           },
           {
            "bin_end": 30,
            "bin_start": 25,
            "count": 1
           }
          ],
          "max": "30.0",
          "min": "-20.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "_deepnote_index_column": 0,
         "a": 5,
         "b": 10,
         "chris": 30
        },
        {
         "_deepnote_index_column": 1,
         "a": 6,
         "b": 10,
         "chris": 2.4
        },
        {
         "_deepnote_index_column": 2,
         "a": 7,
         "b": 10,
         "chris": -20
        }
       ]
      },
      "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b  chris\n",
       "0  5  10   30.0\n",
       "1  6  10    2.4\n",
       "2  7  10  -20.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "6c425055-3982-4910-99f4-f64724a14206",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1643385993338,
    "source_hash": "25d43fa0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'chris'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List comprehension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "b2c78b4e-4d99-4824-99e5-6a2d2a83acc5",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Practice exercise:\n",
    "* Make a list of `True`s (bool type) using list comprehension, where the length of the list is the number of rows in `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": "07ce0717-e02a-4a20-8561-3e4a974f9234",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 6,
    "execution_start": 1643386278671,
    "source_hash": "5d8ad24d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['true', 'true', 'true']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are strings, not bools\n",
    "['true' for x in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "0cfaacfb-6711-491b-91d4-5f66b8481c34",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643386256002,
    "source_hash": "7cf516b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['true', 'true', 'true']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"true\" for x in range(len(df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cell_id": "ae2ab7a4-28b6-4f71-b4e1-be49e864a051",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1643386406029,
    "source_hash": "ac64be90",
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'true' 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/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/3159580370.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mtrue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\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[0m",
      "\u001b[0;32m/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/3159580370.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mtrue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\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[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'true' is not defined"
     ]
    }
   ],
   "source": [
    "[true for x in range(len(df))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the correct answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cell_id": "72829fa8-e4cb-4638-af02-a19a67ca9bd2",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643386417846,
    "source_hash": "4a58405d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, True]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[True for x in range(len(df))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting a new column in a DataFrame\n",
    "\n",
    "One way to create a new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": "9d4cf038-cd0e-4c04-a5d6-f9fe714697e9",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1,
    "execution_start": 1643386477651,
    "source_hash": "96acba45",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"new column\"] = [True for x in range(len(df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cell_id": "8a603460-c5fd-4fee-a76b-020128bbcf98",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     118.953125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 21,
    "execution_start": 1643386499453,
    "source_hash": "f804c160",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b  chris  new column\n",
       "0  5  10   30.0        True\n",
       "1  6  10    2.4        True\n",
       "2  7  10  -20.0        True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the new column is filled with a single value, then you can make the new column faster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "162ef89e-ed72-477c-94cf-5ceb6fc8af3e",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1643386548048,
    "source_hash": "8e76f58a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"new column 2\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "7858a04b-ade8-436f-909b-70260f367ba4",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     118.953125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 31,
    "execution_start": 1643386552003,
    "source_hash": "f804c160",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "columns": [
        {
         "dtype": "int64",
         "name": "a",
         "stats": {
          "histogram": [
           {
            "bin_end": 5.2,
            "bin_start": 5,
            "count": 1
           },
           {
            "bin_end": 5.4,
            "bin_start": 5.2,
            "count": 0
           },
           {
            "bin_end": 5.6,
            "bin_start": 5.4,
            "count": 0
           },
           {
            "bin_end": 5.8,
            "bin_start": 5.6,
            "count": 0
           },
           {
            "bin_end": 6,
            "bin_start": 5.8,
            "count": 0
           },
           {
            "bin_end": 6.2,
            "bin_start": 6,
            "count": 1
           },
           {
            "bin_end": 6.4,
            "bin_start": 6.2,
            "count": 0
           },
           {
            "bin_end": 6.6,
            "bin_start": 6.4,
            "count": 0
           },
           {
            "bin_end": 6.8,
            "bin_start": 6.6,
            "count": 0
           },
           {
            "bin_end": 7,
            "bin_start": 6.8,
            "count": 1
           }
          ],
          "max": "7",
          "min": "5",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "b",
         "stats": {
          "histogram": [
           {
            "bin_end": 9.6,
            "bin_start": 9.5,
            "count": 0
           },
           {
            "bin_end": 9.7,
            "bin_start": 9.6,
            "count": 0
           },
           {
            "bin_end": 9.8,
            "bin_start": 9.7,
            "count": 0
           },
           {
            "bin_end": 9.9,
            "bin_start": 9.8,
            "count": 0
           },
           {
            "bin_end": 10,
            "bin_start": 9.9,
            "count": 0
           },
           {
            "bin_end": 10.1,
            "bin_start": 10,
            "count": 3
           },
           {
            "bin_end": 10.2,
            "bin_start": 10.1,
            "count": 0
           },
           {
            "bin_end": 10.3,
            "bin_start": 10.2,
            "count": 0
           },
           {
            "bin_end": 10.4,
            "bin_start": 10.3,
            "count": 0
           },
           {
            "bin_end": 10.5,
            "bin_start": 10.4,
            "count": 0
           }
          ],
          "max": "10",
          "min": "10",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "float64",
         "name": "chris",
         "stats": {
          "histogram": [
           {
            "bin_end": -15,
            "bin_start": -20,
            "count": 1
           },
           {
            "bin_end": -10,
            "bin_start": -15,
            "count": 0
           },
           {
            "bin_end": -5,
            "bin_start": -10,
            "count": 0
           },
           {
            "bin_end": 0,
            "bin_start": -5,
            "count": 0
           },
           {
            "bin_end": 5,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 10,
            "bin_start": 5,
            "count": 0
           },
           {
            "bin_end": 15,
            "bin_start": 10,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 15,
            "count": 0
           },
           {
            "bin_end": 25,
            "bin_start": 20,
            "count": 0
           },
           {
            "bin_end": 30,
            "bin_start": 25,
            "count": 1
           }
          ],
          "max": "30.0",
          "min": "-20.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "bool",
         "name": "new column",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "True"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "bool",
         "name": "new column 2",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "False"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "_deepnote_index_column": 0,
         "a": 5,
         "b": 10,
         "chris": 30,
         "new column": true,
         "new column 2": false
        },
        {
         "_deepnote_index_column": 1,
         "a": 6,
         "b": 10,
         "chris": 2.4,
         "new column": true,
         "new column 2": false
        },
        {
         "_deepnote_index_column": 2,
         "a": 7,
         "b": 10,
         "chris": -20,
         "new column": true,
         "new column 2": false
        }
       ]
      },
      "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "      <th>new column 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b  chris  new column  new column 2\n",
       "0  5  10   30.0        True         False\n",
       "1  6  10    2.4        True         False\n",
       "2  7  10  -20.0        True         False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "Indexing using `iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "cell_id": "ba6032d3-f47b-411d-a47f-e3501c9cd4a3",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 8,
    "execution_start": 1643386757914,
    "source_hash": "549c0088",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris  new column\n",
       "0   5  10   30.0        True\n",
       "1  20  10    2.4        True\n",
       "2   7  10  -20.0        True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,0] = 20\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing using `loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "cell_id": "64d8f5af-84aa-4706-9e76-2858824728f8",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 50,
    "execution_start": 1643386786790,
    "source_hash": "82352452",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris  new column\n",
       "0   5  10   30.0        True\n",
       "1  20  10    2.4        True\n",
       "2   7  10  -20.0        True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1,'a'] = 20\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "a8a1ef54-7fe9-4dd3-8cb9-bee16851af20",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "## Second largest value in a column.\n",
    "\n",
    "Find the second largest value in the \"a\" column of df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "f331642a-cea2-457a-96de-76dad08ce281",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 10,
    "execution_start": 1643386855790,
    "source_hash": "38ded7f3",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    20\n",
       "2     7\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "c5022c3e-d91b-4de6-ac34-06553827fe2b",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643386948119,
    "source_hash": "e21b8e18",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "2     7\n",
       "1    20\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "cfcca3cc-ba57-4fc8-a39e-50dedf4e53de",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 6,
    "execution_start": 1643386992997,
    "source_hash": "765ab8e8",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    20\n",
       "2     7\n",
       "0     5\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I kept accidentally trying to use `[1]` instead of `iloc[1]`.  Here is the correct way to find the element at index 1 in a pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cell_id": "2887e8ce-6d9c-4744-9d6c-2f3f073ff020",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1643387027488,
    "source_hash": "f57a6f13",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"].sort_values(ascending=False).iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without the `ascending` keyword argument, we have to take the second-to-last entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cell_id": "180f3cb3-29ba-425e-82d5-aa080e61d658",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 8,
    "execution_start": 1643387069710,
    "source_hash": "e9017366",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"a\"].sort_values().iloc[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice with axis\n",
    "\n",
    "We haven't used `median` before, I don't think, but the following should make sense.  The most important part is recognizing what `axis=0` means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris  new column\n",
       "0   5  10   30.0        True\n",
       "1  20  10    2.4        True\n",
       "2   7  10  -20.0        True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "cell_id": "320bf021-7f54-4bd6-b9f3-1d079c085c21",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     1
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643387140975,
    "source_hash": "8a04b452",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a              7.0\n",
       "b             10.0\n",
       "chris          2.4\n",
       "new column     1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flattening a NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cell_id": "f2660896-1b10-4465-a69d-a696a3233338",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643387221160,
    "source_hash": "801acc99",
    "tags": []
   },
   "outputs": [],
   "source": [
    "A = np.array([[2,5,1],[3,1,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cell_id": "5fa02e49-4464-484f-9746-6f836b345372",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 12,
    "execution_start": 1643387292722,
    "source_hash": "aea5aa59",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  1,  3,  1, 10])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.reshape((-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "b4c14ac2-fc37-4a58-922a-cd79ce4b5870",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 258,
    "execution_start": 1643387327197,
    "source_hash": "f804c160",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "columns": [
        {
         "dtype": "int64",
         "name": "a",
         "stats": {
          "histogram": [
           {
            "bin_end": 6.5,
            "bin_start": 5,
            "count": 1
           },
           {
            "bin_end": 8,
            "bin_start": 6.5,
            "count": 1
           },
           {
            "bin_end": 9.5,
            "bin_start": 8,
            "count": 0
           },
           {
            "bin_end": 11,
            "bin_start": 9.5,
            "count": 0
           },
           {
            "bin_end": 12.5,
            "bin_start": 11,
            "count": 0
           },
           {
            "bin_end": 14,
            "bin_start": 12.5,
            "count": 0
           },
           {
            "bin_end": 15.5,
            "bin_start": 14,
            "count": 0
           },
           {
            "bin_end": 17,
            "bin_start": 15.5,
            "count": 0
           },
           {
            "bin_end": 18.5,
            "bin_start": 17,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 18.5,
            "count": 1
           }
          ],
          "max": "20",
          "min": "5",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "b",
         "stats": {
          "histogram": [
           {
            "bin_end": 9.6,
            "bin_start": 9.5,
            "count": 0
           },
           {
            "bin_end": 9.7,
            "bin_start": 9.6,
            "count": 0
           },
           {
            "bin_end": 9.8,
            "bin_start": 9.7,
            "count": 0
           },
           {
            "bin_end": 9.9,
            "bin_start": 9.8,
            "count": 0
           },
           {
            "bin_end": 10,
            "bin_start": 9.9,
            "count": 0
           },
           {
            "bin_end": 10.1,
            "bin_start": 10,
            "count": 3
           },
           {
            "bin_end": 10.2,
            "bin_start": 10.1,
            "count": 0
           },
           {
            "bin_end": 10.3,
            "bin_start": 10.2,
            "count": 0
           },
           {
            "bin_end": 10.4,
            "bin_start": 10.3,
            "count": 0
           },
           {
            "bin_end": 10.5,
            "bin_start": 10.4,
            "count": 0
           }
          ],
          "max": "10",
          "min": "10",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "float64",
         "name": "chris",
         "stats": {
          "histogram": [
           {
            "bin_end": -15,
            "bin_start": -20,
            "count": 1
           },
           {
            "bin_end": -10,
            "bin_start": -15,
            "count": 0
           },
           {
            "bin_end": -5,
            "bin_start": -10,
            "count": 0
           },
           {
            "bin_end": 0,
            "bin_start": -5,
            "count": 0
           },
           {
            "bin_end": 5,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 10,
            "bin_start": 5,
            "count": 0
           },
           {
            "bin_end": 15,
            "bin_start": 10,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 15,
            "count": 0
           },
           {
            "bin_end": 25,
            "bin_start": 20,
            "count": 0
           },
           {
            "bin_end": 30,
            "bin_start": 25,
            "count": 1
           }
          ],
          "max": "30.0",
          "min": "-20.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "bool",
         "name": "new column",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "True"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "bool",
         "name": "new column 2",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "False"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "_deepnote_index_column": 0,
         "a": 5,
         "b": 10,
         "chris": 30,
         "new column": true,
         "new column 2": false
        },
        {
         "_deepnote_index_column": 1,
         "a": 20,
         "b": 10,
         "chris": 2.4,
         "new column": true,
         "new column 2": false
        },
        {
         "_deepnote_index_column": 2,
         "a": 7,
         "b": 10,
         "chris": -20,
         "new column": true,
         "new column 2": false
        }
       ]
      },
      "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "      <th>new column 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris  new column  new column 2\n",
       "0   5  10   30.0        True         False\n",
       "1  20  10    2.4        True         False\n",
       "2   7  10  -20.0        True         False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access every other element in the row with label 1 using slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "cell_id": "aeffc566-d5a5-461d-b4c5-90c360aa683c",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643387403078,
    "source_hash": "fff7ff58",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a         20\n",
       "chris    2.4\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1,::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change every other element in the row with label 1 using slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cell_id": "28b7a6a2-6530-4abc-9ae1-ecb06349cb89",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 4,
    "execution_start": 1643387479303,
    "source_hash": "b0e70448",
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must have equal len keys and value when setting with an iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/1568438968.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\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[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\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~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m         \u001b[0miloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"iloc\"\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 723\u001b[0;31m         \u001b[0miloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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    724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0mint\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~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[0;34m(self, indexer, value, name)\u001b[0m\n\u001b[1;32m   1728\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtake_split_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1729\u001b[0m             \u001b[0;31m# We have to operate column-wise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1730\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer_split_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   1731\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   1732\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_single_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer_split_path\u001b[0;34m(self, indexer, value, name)\u001b[0m\n\u001b[1;32m   1806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1807\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;32m-> 1808\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1809\u001b[0m                     \u001b[0;34m\"Must have equal len keys and value \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1810\u001b[0m                     \u001b[0;34m\"when setting with an iterable\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Must have equal len keys and value when setting with an iterable"
     ]
    }
   ],
   "source": [
    "df.loc[1,::2] = [i**2 for i in range(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change every other element in the row with label 1 using slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "bfea788c-addb-45e5-bfc8-cf2a45a3d99c",
    "deepnote_cell_type": "code",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.loc[1,::2] = [0,1,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "c353bf34-0a49-4655-962b-e650fb70e997",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 37,
    "execution_start": 1643387479310,
    "source_hash": "f804c160",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "columns": [
        {
         "dtype": "int64",
         "name": "a",
         "stats": {
          "histogram": [
           {
            "bin_end": 0.7,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 1.4,
            "bin_start": 0.7,
            "count": 0
           },
           {
            "bin_end": 2.0999999999999996,
            "bin_start": 1.4,
            "count": 0
           },
           {
            "bin_end": 2.8,
            "bin_start": 2.0999999999999996,
            "count": 0
           },
           {
            "bin_end": 3.5,
            "bin_start": 2.8,
            "count": 0
           },
           {
            "bin_end": 4.199999999999999,
            "bin_start": 3.5,
            "count": 0
           },
           {
            "bin_end": 4.8999999999999995,
            "bin_start": 4.199999999999999,
            "count": 0
           },
           {
            "bin_end": 5.6,
            "bin_start": 4.8999999999999995,
            "count": 1
           },
           {
            "bin_end": 6.3,
            "bin_start": 5.6,
            "count": 0
           },
           {
            "bin_end": 7,
            "bin_start": 6.3,
            "count": 1
           }
          ],
          "max": "7",
          "min": "0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "b",
         "stats": {
          "histogram": [
           {
            "bin_end": 9.6,
            "bin_start": 9.5,
            "count": 0
           },
           {
            "bin_end": 9.7,
            "bin_start": 9.6,
            "count": 0
           },
           {
            "bin_end": 9.8,
            "bin_start": 9.7,
            "count": 0
           },
           {
            "bin_end": 9.9,
            "bin_start": 9.8,
            "count": 0
           },
           {
            "bin_end": 10,
            "bin_start": 9.9,
            "count": 0
           },
           {
            "bin_end": 10.1,
            "bin_start": 10,
            "count": 3
           },
           {
            "bin_end": 10.2,
            "bin_start": 10.1,
            "count": 0
           },
           {
            "bin_end": 10.3,
            "bin_start": 10.2,
            "count": 0
           },
           {
            "bin_end": 10.4,
            "bin_start": 10.3,
            "count": 0
           },
           {
            "bin_end": 10.5,
            "bin_start": 10.4,
            "count": 0
           }
          ],
          "max": "10",
          "min": "10",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "float64",
         "name": "chris",
         "stats": {
          "histogram": [
           {
            "bin_end": -15,
            "bin_start": -20,
            "count": 1
           },
           {
            "bin_end": -10,
            "bin_start": -15,
            "count": 0
           },
           {
            "bin_end": -5,
            "bin_start": -10,
            "count": 0
           },
           {
            "bin_end": 0,
            "bin_start": -5,
            "count": 0
           },
           {
            "bin_end": 5,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 10,
            "bin_start": 5,
            "count": 0
           },
           {
            "bin_end": 15,
            "bin_start": 10,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 15,
            "count": 0
           },
           {
            "bin_end": 25,
            "bin_start": 20,
            "count": 0
           },
           {
            "bin_end": 30,
            "bin_start": 25,
            "count": 1
           }
          ],
          "max": "30.0",
          "min": "-20.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "bool",
         "name": "new column",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "True"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "new column 2",
         "stats": {
          "categories": [
           {
            "count": 2,
            "name": "False"
           },
           {
            "count": 1,
            "name": "4"
           }
          ],
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "_deepnote_index_column": 0,
         "a": 5,
         "b": 10,
         "chris": 30,
         "new column": true,
         "new column 2": "False"
        },
        {
         "_deepnote_index_column": 1,
         "a": 0,
         "b": 10,
         "chris": 1,
         "new column": true,
         "new column 2": "4"
        },
        {
         "_deepnote_index_column": 2,
         "a": 7,
         "b": 10,
         "chris": -20,
         "new column": true,
         "new column 2": "False"
        }
       ]
      },
      "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "      <th>new column</th>\n",
       "      <th>new column 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b  chris  new column new column 2\n",
       "0  5  10   30.0        True        False\n",
       "1  0  10    1.0        True            4\n",
       "2  7  10  -20.0        True        False"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Square every element in a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_id": "e5476dc2-8793-4f66-af07-ece0ed9708df",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 15,
    "execution_start": 1643387718414,
    "source_hash": "8e461c83",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris\n",
       "0   5  10   30.0\n",
       "1  20  10    2.4\n",
       "2   7  10  -20.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.iloc[:,:3]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "06a97bc0-7695-4538-ac7a-3ebf0a8684c9",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643387734047,
    "source_hash": "62b9b55b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2.loc[1] = df2.loc[1]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "ac03692d-e58d-4fe4-8340-9cc125eaf5a3",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 16,
    "execution_start": 1643387736160,
    "source_hash": "caa55e2e",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 3,
       "columns": [
        {
         "dtype": "float64",
         "name": "a",
         "stats": {
          "histogram": [
           {
            "bin_end": 0.7,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 1.4,
            "bin_start": 0.7,
            "count": 0
           },
           {
            "bin_end": 2.0999999999999996,
            "bin_start": 1.4,
            "count": 0
           },
           {
            "bin_end": 2.8,
            "bin_start": 2.0999999999999996,
            "count": 0
           },
           {
            "bin_end": 3.5,
            "bin_start": 2.8,
            "count": 0
           },
           {
            "bin_end": 4.199999999999999,
            "bin_start": 3.5,
            "count": 0
           },
           {
            "bin_end": 4.8999999999999995,
            "bin_start": 4.199999999999999,
            "count": 0
           },
           {
            "bin_end": 5.6,
            "bin_start": 4.8999999999999995,
            "count": 1
           },
           {
            "bin_end": 6.3,
            "bin_start": 5.6,
            "count": 0
           },
           {
            "bin_end": 7,
            "bin_start": 6.3,
            "count": 1
           }
          ],
          "max": "7.0",
          "min": "0.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "b",
         "stats": {
          "histogram": [
           {
            "bin_end": 19,
            "bin_start": 10,
            "count": 2
           },
           {
            "bin_end": 28,
            "bin_start": 19,
            "count": 0
           },
           {
            "bin_end": 37,
            "bin_start": 28,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 37,
            "count": 0
           },
           {
            "bin_end": 55,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 64,
            "bin_start": 55,
            "count": 0
           },
           {
            "bin_end": 73,
            "bin_start": 64,
            "count": 0
           },
           {
            "bin_end": 82,
            "bin_start": 73,
            "count": 0
           },
           {
            "bin_end": 91,
            "bin_start": 82,
            "count": 0
           },
           {
            "bin_end": 100,
            "bin_start": 91,
            "count": 1
           }
          ],
          "max": "100.0",
          "min": "10.0",
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "float64",
         "name": "chris",
         "stats": {
          "histogram": [
           {
            "bin_end": -15,
            "bin_start": -20,
            "count": 1
           },
           {
            "bin_end": -10,
            "bin_start": -15,
            "count": 0
           },
           {
            "bin_end": -5,
            "bin_start": -10,
            "count": 0
           },
           {
            "bin_end": 0,
            "bin_start": -5,
            "count": 0
           },
           {
            "bin_end": 5,
            "bin_start": 0,
            "count": 1
           },
           {
            "bin_end": 10,
            "bin_start": 5,
            "count": 0
           },
           {
            "bin_end": 15,
            "bin_start": 10,
            "count": 0
           },
           {
            "bin_end": 20,
            "bin_start": 15,
            "count": 0
           },
           {
            "bin_end": 25,
            "bin_start": 20,
            "count": 0
           },
           {
            "bin_end": 30,
            "bin_start": 25,
            "count": 1
           }
          ],
          "max": "30.0",
          "min": "-20.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "_deepnote_index_column": 0,
         "a": 5,
         "b": 10,
         "chris": 30
        },
        {
         "_deepnote_index_column": 1,
         "a": 0,
         "b": 100,
         "chris": 1
        },
        {
         "_deepnote_index_column": 2,
         "a": 7,
         "b": 10,
         "chris": -20
        }
       ]
      },
      "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a      b  chris\n",
       "0  5.0   10.0   30.0\n",
       "1  0.0  100.0    1.0\n",
       "2  7.0   10.0  -20.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cell_id": "d1872d4c-3889-466f-804c-3286e6adbb86",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 29,
    "execution_start": 1643387760375,
    "source_hash": "b5f73824",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>100</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>100</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b   chris\n",
       "0   25  100  900.00\n",
       "1  400  100    5.76\n",
       "2   49  100  400.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "fd98728f-abe6-4572-bc6c-d3672b497207",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Try doing that same thing (squaring every entry in df2) using `map`, `apply`, or `applymap`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "cell_id": "355adbf3-c513-439d-8365-45b34a7809ad",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643387920495,
    "source_hash": "8061dcae",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>100</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>100</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b   chris\n",
       "0   25  100  900.00\n",
       "1  400  100    5.76\n",
       "2   49  100  400.00"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.applymap(lambda x: x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: notice that `applymap` doesn't change the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "cell_id": "7f22f075-8067-4ee7-b007-ba24616f19d5",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643387979938,
    "source_hash": "caa55e2e",
    "tags": []
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>chris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b  chris\n",
       "0   5  10   30.0\n",
       "1  20  10    2.4\n",
       "2   7  10  -20.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "fe74e8ba-8cec-4220-bfd5-921f9a3c8cfb",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     117.21875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1643388184581,
    "source_hash": "5cc90411",
    "tags": []
   },
   "source": [
    "## Example using apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cell_id": "6874da94-7aeb-41cf-926c-c2ae40280861",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     78.8125
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643388015610,
    "source_hash": "339b2fc5",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a        32.0\n",
       "b        30.0\n",
       "chris    12.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.apply(lambda c: c.sum(), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45.0\n",
       "1    32.4\n",
       "2    -3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.apply(lambda r: r.sum(), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we tried to use `applymap` instead?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "d0801cea-4262-4830-b507-29b01d36c6cd",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 26,
    "execution_start": 1643388141729,
    "source_hash": "adda5cc5",
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'sum'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-46e48127eb5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\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[0m",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapplymap\u001b[0;34m(self, func, na_action)\u001b[0m\n\u001b[1;32m   7842\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_na\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7844\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"applymap\"\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   7845\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7846\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7766\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m         )\n\u001b[0;32m-> 7768\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\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   7769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\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    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\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    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\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    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\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    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\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    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\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    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36minfer\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   7840\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7841\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_na\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7842\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_na\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   7843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7844\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"applymap\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-62-46e48127eb5f>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(a)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\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[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'sum'"
     ]
    }
   ],
   "source": [
    "df2.applymap(lambda a: a.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "8a2f01ac-e621-4f78-a062-397e31c77383",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Sample exercise: What causes the above error?\n",
    "\n",
    "Sample answer: `a` will be a number in the dataframe, and `number.sum()` does not make sense.  Should use `apply` and `axis` instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A smaller piece of code that raises the same error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'sum'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/223743580.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\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;31mAttributeError\u001b[0m: 'float' object has no attribute 'sum'"
     ]
    }
   ],
   "source": [
    "a = 5.1\n",
    "a.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the three methods, `map`, `applymap`, and `apply`, definitely `apply` is the trickiest to understand.  The most natural example with `apply` we have seen was using `pd.to_numeric` on every column.  Notice how the input to `pd.to_numeric` should be an entire Series, not an individual entry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with datetime entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "cell_id": "25702ca0-1d3f-44ab-9232-1307f50f61ea",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 23,
    "execution_start": 1643388402781,
    "source_hash": "6b2531b2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/spotify_dataset.csv\", na_values=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "cell_id": "5bbb132a-192d-4928-9db1-392a78349626",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     136.421875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 16,
    "execution_start": 1643388407679,
    "source_hash": "25d43fa0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Index', 'Highest Charting Position', 'Number of Times Charted',\n",
       "       'Week of Highest Charting', 'Song Name', 'Streams', 'Artist',\n",
       "       'Artist Followers', 'Song ID', 'Genre', 'Release Date', 'Weeks Charted',\n",
       "       'Popularity', 'Danceability', 'Energy', 'Loudness', 'Speechiness',\n",
       "       'Acousticness', 'Liveness', 'Tempo', 'Duration (ms)', 'Valence',\n",
       "       'Chord'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way of getting a column from a DataFrame is called *attribute* access:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "cell_id": "6c00faa6-0850-4acc-b4e5-a508ef83467b",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     232.4375
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643388429000,
    "source_hash": "9634eaf3",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  ['indie rock italiano', 'italian pop']\n",
       "1                                  ['australian hip hop']\n",
       "2                                                 ['pop']\n",
       "3                                       ['pop', 'uk pop']\n",
       "4                           ['lgbtq+ hip hop', 'pop rap']\n",
       "                              ...                        \n",
       "1551                       ['dance pop', 'pop', 'uk pop']\n",
       "1552             ['sertanejo', 'sertanejo universitario']\n",
       "1553    ['dance pop', 'electropop', 'pop', 'post-teen ...\n",
       "1554                       ['brega funk', 'funk carioca']\n",
       "1555                             ['pop', 'post-teen pop']\n",
       "Name: Genre, Length: 1556, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It doesn't always work.  For example, in the following, we can't use attribute access because *Release Date* has a space in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "cell_id": "12da1ee5-195e-42ad-9bbb-3aecc608e1b5",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643388445735,
    "source_hash": "c2d0c103",
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3791632194.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/3791632194.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df.Release Date\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df.Release Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "cell_id": "ed0f2a84-b0c9-47db-bf5d-0a9e9c5c09ca",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     232.4375
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643388460752,
    "source_hash": "9e0b7036",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-12-08\n",
       "1       2021-07-09\n",
       "2       2021-05-21\n",
       "3       2021-06-25\n",
       "4       2021-07-23\n",
       "           ...    \n",
       "1551    2017-06-02\n",
       "1552    2019-10-11\n",
       "1553    2018-01-12\n",
       "1554    2019-09-25\n",
       "1555    2019-11-13\n",
       "Name: Release Date, Length: 1556, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Release Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "cell_id": "aa45def3-1eec-4144-8913-ee496e4e4bdb",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     232.4375
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 9,
    "execution_start": 1643388521349,
    "source_hash": "a05f272f",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        8.0\n",
       "1        9.0\n",
       "2       21.0\n",
       "3       25.0\n",
       "4       23.0\n",
       "        ... \n",
       "1551     2.0\n",
       "1552    11.0\n",
       "1553    12.0\n",
       "1554    25.0\n",
       "1555    13.0\n",
       "Name: Release Date, Length: 1556, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df[\"Release Date\"]).dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "cell_id": "8dda9b06-7f4d-444d-a9a9-9261982253ba",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     232.4375
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 0,
    "execution_start": 1643388602047,
    "source_hash": "993d219d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "year_series = pd.to_datetime(df[\"Release Date\"]).dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample exercise: How many `2019`s are there in `year_series`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "cell_id": "35d0392f-2ff8-4fc7-9a39-f808d5927712",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 3,
    "execution_start": 1643388715520,
    "source_hash": "6ae1b884",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(year_series == 2019).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're trying a different method, but it doesn't work at first because of null values.  I realized after class that we could have used a keyword argument to `map` called `na_action`, but during class we removed the null values by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "cell_id": "333509a2-a623-4627-a978-f3dced88082d",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     233
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 30,
    "execution_start": 1643388788276,
    "source_hash": "ddf125b8",
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/337003375.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Release Date\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2019\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~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   4159\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4160\u001b[0m         \"\"\"\n\u001b[0;32m-> 4161\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\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   4162\u001b[0m         return self._constructor(new_values, index=self.index).__finalize__(\n\u001b[1;32m   4163\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\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    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/math11/lib/python3.9/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/var/folders/8j/gshrlmtn7dg4qtztj4d4t_w40000gn/T/ipykernel_37915/337003375.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(s)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Release Date\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2019\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'float' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "df[\"Release Date\"].map(lambda s: s[:4] == 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "752d9dc3-4693-430b-a8f1-fcc167dd82e9",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 11,
    "execution_start": 1643388856109,
    "source_hash": "47061172",
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-1b4e472a49c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'float' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "np.nan[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During class I made another mistake before getting to the next cell, but I deleted it from this notebook because it's more confusing than helpful.\n",
    "\n",
    "Here is an alternate approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "cell_id": "327bc4a8-199a-4fe6-a45a-5bef11ad74de",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 2,
    "execution_start": 1643389005077,
    "source_hash": "d9d052d5",
    "tags": []
   },
   "outputs": [],
   "source": [
    "clean = df[\"Release Date\"][~df[\"Release Date\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "cell_id": "f31583c0-a0a2-4463-8cc6-c5d1dbd196f5",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643389110528,
    "source_hash": "9eb0b2f4",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017-12-08\n",
       "1       2021-07-09\n",
       "2       2021-05-21\n",
       "3       2021-06-25\n",
       "4       2021-07-23\n",
       "           ...    \n",
       "1551    2017-06-02\n",
       "1552    2019-10-11\n",
       "1553    2018-01-12\n",
       "1554    2019-09-25\n",
       "1555    2019-11-13\n",
       "Name: Release Date, Length: 1545, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two different ways to count `2019`s in this pandas Series of strings.  Notice that they give the same answer as the `.dt.year` method from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "cell_id": "b3f67f74-26d5-41c0-a9dc-0fc321e62429",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 5,
    "execution_start": 1643389020671,
    "source_hash": "87192a9c",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean.map(lambda s: s[:4] == \"2019\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "cell_id": "821ba94b-c0ac-4122-bdd3-5f1231879659",
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 10,
    "execution_start": 1643389057991,
    "source_hash": "107a5169",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean.map(lambda s: int(s[:4]) == 2019).sum()"
   ]
  }
 ],
 "metadata": {
  "deepnote": {
   "is_reactive": false
  },
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "2cfe386f-b03b-4bf9-8aba-f1d8192309bc",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}