17_pandas_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Enhances Numpy with the following features:\n",
    "    1. Data labels[named columns] and descriptive indices\n",
    "    2. Robust handling of common data formats and missing data\n",
    "    3. Relational-database operations\n",
    " ## Learning Objectives\n",
    "    1. Creating Series objects from Python lists and dicts\n",
    "    2. Extraction indexes and values\n",
    "    3. Indexing Series objects implicitly and explicitly\n",
    "    4. Integer or Labeled Location explicitly mentioned\n",
    "    5. Advanced Indexing -> Boolean mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# installation\n",
    "# jupyter notebook\n",
    "!pip install pandas\n",
    "# Terminal \n",
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas -> follow the convention :)\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series \n",
    "    Series are like one-dimensional arrays with labels/names!\n",
    "    Series are building blocks of Pandas DataFrames which we'll cover in the next section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     4\n",
       "3     9\n",
       "4    16\n",
       "5    25\n",
       "Name: squares, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a series from a list\n",
    "squares_series = pd.Series([0, 1, 4, 9, 16, 25], name='squares')\n",
    "squares_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array value of a series object:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting arrays from series\n",
    "print(f\"array value of a series object:\")\n",
    "squares_series.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indices of a series object:\n",
      "[INFO] This is called implicit indexing because we as users didn't specify as a index for square_series\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting indices\n",
    "print(f\"indices of a series object:\")\n",
    "print(\"[INFO] This is called implicit indexing because we as users didn't specify as a index for square_series\")\n",
    "squares_series.index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using numeric indecies to extract items\n",
      "item at index 3: 9\n"
     ]
    }
   ],
   "source": [
    "print(\"Using numeric indecies to extract items\")\n",
    "print(f\"item at index 3: {squares_series[3]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slicing\n",
      "items from index 2 to 4(excluded):\n",
      "2    4\n",
      "3    9\n",
      "Name: squares, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Slicing\")\n",
    "print(f\"items from index 2 to 4(excluded):\\n{squares_series[2: 4]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting the name:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'squares'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Getting the name:\")\n",
    "squares_series.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computer programming languages' popularity in 2022\n",
      "Explicit indexing\n",
      "[NOTE] These numbers are not extracted from a survey/report whatsoever\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java           99.3\n",
       "C              92.4\n",
       "C++            95.5\n",
       "Python         95.0\n",
       "C#             84.5\n",
       "PHP            56.0\n",
       "JavaScript     84.8\n",
       "Ruby          100.0\n",
       "R              74.3\n",
       "Matlab         78.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Computer programming languages' popularity in 2022\")\n",
    "print(\"Explicit indexing\")\n",
    "print(\"[NOTE] These numbers are not extracted from a survey/report whatsoever\")\n",
    "values = [99.3, 92.4, 95.5, 95, 84.5, 56, 84.8, 100, 74.3, 78.9]\n",
    "indexes = ['Java', 'C', 'C++', 'Python', 'C#', 'PHP', 'JavaScript', 'Ruby', 'R', 'Matlab']\n",
    "popularity_2022 = pd.Series(values, index=indexes)\n",
    "popularity_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Series using dictionaries\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "C              99.9\n",
       "C#             91.3\n",
       "C++            99.4\n",
       "Java           92.0\n",
       "JavaScript     83.0\n",
       "Matlab         53.0\n",
       "PHP            83.0\n",
       "Python        100.0\n",
       "R              84.8\n",
       "Ruby           76.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Creating Series using dictionaries\")\n",
    "popularity_dict_2021_dict = {\"C\": 99.9,  \"C#\": 91.3, \"C++\": 99.4, \"Java\": 92,  \"JavaScript\": 83,\n",
    "                              \"Matlab\": 53, \"PHP\": 83,  \"Python\": 100,  \"R\": 84.8, \"Ruby\": 76.2}\n",
    "popularity_dict_2021 = pd.Series(popularity_dict_2021_dict)\n",
    "popularity_dict_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Java           99.3\n",
       "C              92.4\n",
       "C++            95.5\n",
       "Python         95.0\n",
       "C#             84.5\n",
       "PHP            56.0\n",
       "JavaScript     84.8\n",
       "Ruby          100.0\n",
       "R              74.3\n",
       "Matlab         78.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexes are names/labels not numbers/range!\n",
    "popularity_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indexes: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Java', 'C', 'C++', 'Python', 'C#', 'PHP', 'JavaScript', 'Ruby', 'R',\n",
       "       'Matlab'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('indexes: ')\n",
    "popularity_2022.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using numeric indecies to extract items\n",
      "item at index 0: 99.3\n"
     ]
    }
   ],
   "source": [
    "print(\"Using numeric indecies to extract items\")\n",
    "print(f\"item at index 0: {popularity_2022[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slicing\n",
      "items from index 0 to 2(excluded):\n",
      "Java    99.3\n",
      "C       92.4\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Slicing\")\n",
    "print(f\"items from index 0 to 2(excluded):\\n{popularity_2022[0: 2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting items using named indices\n",
      "item at index Python: 95.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Extracting items using named indices\")\n",
    "print(f\"item at index Python: {popularity_2022['Python']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Named Slicing\n",
      "items from index C++ to C#(Included!!!!):\n",
      "C++       95.5\n",
      "Python    95.0\n",
      "C#        84.5\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Named Slicing\")\n",
    "print(f\"items from index C++ to C#(Included!!!!):\\n{popularity_2022['C++':'C#']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integer or Labeled Location explicitly mentioned "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer Location. Although popularity series is indexed with a list of labels, integer indexing can be applied to it\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java    99.3\n",
       "C       92.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Integer Location. Although popularity series is indexed with a list of labels, integer indexing can be applied to it\")\n",
    "popularity_2022.iloc[0: 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Location\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java           99.3\n",
       "C              92.4\n",
       "C++            95.5\n",
       "Python         95.0\n",
       "C#             84.5\n",
       "PHP            56.0\n",
       "JavaScript     84.8\n",
       "Ruby          100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Label Location\")\n",
    "popularity_2022.loc[:'Ruby']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raises error if loc is used to get an item using integer indexes!\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "cannot do slice indexing on Index with these indexers [1] of type int",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRaises error if loc is used to get an item using integer indexes!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mpopularity_2022\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    964\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    966\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m--> 967\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexing.py:1180\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1179\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1180\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1181\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m   1182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexing.py:1214\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1211\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1213\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m-> 1214\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1217\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6274\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   6231\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   6232\u001b[0m \u001b[38;5;124;03mCompute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[0;32m   6233\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6270\u001b[0m \u001b[38;5;124;03mslice(1, 3, None)\u001b[39;00m\n\u001b[0;32m   6271\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   6272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecated_arg(kind, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_indexer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 6274\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6276\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[0;32m   6277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6490\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   6488\u001b[0m end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   6489\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 6490\u001b[0m     end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mright\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6491\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   6492\u001b[0m     end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6393\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   6389\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m   6391\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[0;32m   6392\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[1;32m-> 6393\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_cast_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mside\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6395\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[0;32m   6396\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\intro_py\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6340\u001b[0m, in \u001b[0;36mIndex._maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   6335\u001b[0m \u001b[38;5;66;03m# We are a plain index here (sub-class override this method if they\u001b[39;00m\n\u001b[0;32m   6336\u001b[0m \u001b[38;5;66;03m# wish to have special treatment for floats/ints, e.g. Float64Index and\u001b[39;00m\n\u001b[0;32m   6337\u001b[0m \u001b[38;5;66;03m# datetimelike Indexes\u001b[39;00m\n\u001b[0;32m   6338\u001b[0m \u001b[38;5;66;03m# reject them, if index does not contain label\u001b[39;00m\n\u001b[0;32m   6339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (is_float(label) \u001b[38;5;129;01mor\u001b[39;00m is_integer(label)) \u001b[38;5;129;01mand\u001b[39;00m label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m-> 6340\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalid_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, label)\n\u001b[0;32m   6342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on Index with these indexers [1] of type int"
     ]
    }
   ],
   "source": [
    "print(\"Raises error if loc is used to get an item using integer indexes!\")\n",
    "popularity_2022.loc[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### advance indexing like boolean mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition and its output. It's like a boolean mask in NumPy as a series.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java           True\n",
       "C              True\n",
       "C++            True\n",
       "Python         True\n",
       "C#            False\n",
       "PHP           False\n",
       "JavaScript    False\n",
       "Ruby           True\n",
       "R             False\n",
       "Matlab        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Condition and its output. It's like a boolean mask in NumPy as a series.\")\n",
    "popularity_2022 > 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True, False, False, False,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy boolean mask\n",
    "(popularity_2022 > 90).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying the boolean mask\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java       99.3\n",
       "C          92.4\n",
       "C++        95.5\n",
       "Python     95.0\n",
       "Ruby      100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Applying the boolean mask\")\n",
    "popularity_2022[popularity_2022 > 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting items with a list of noncontinuous indices\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java      99.3\n",
       "Python    95.0\n",
       "PHP       56.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Getting items with a list of noncontinuous indices\")\n",
    "popularity_2022[[0, 3, 5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assigning values to a list of noncontinuous indices\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Java           99.0\n",
       "C              92.4\n",
       "C++            95.5\n",
       "Python        100.0\n",
       "C#             84.5\n",
       "PHP            90.0\n",
       "JavaScript     84.8\n",
       "Ruby          100.0\n",
       "R              74.3\n",
       "Matlab         78.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Assigning values to a list of noncontinuous indices\")\n",
    "popularity_2022[[0, 3, 5]] = [99, 100, 90]\n",
    "popularity_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Java          100.0\n",
       "C             100.0\n",
       "C++           100.0\n",
       "Python        100.0\n",
       "C#             84.5\n",
       "PHP            90.0\n",
       "JavaScript     84.8\n",
       "Ruby          100.0\n",
       "R              74.3\n",
       "Matlab         78.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rounding values to 100 \n",
    "popularity_2022[popularity_2022 > 90] = [100 for mask in (popularity_2022 > 90).values if mask]\n",
    "popularity_2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*:)*"
   ]
  }
 ],
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