{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2abf4bed",
   "metadata": {},
   "source": [
    "# Instructor Solutions Notebook\n",
    "This notebook contains solution functions and tests for 20 questions covering Python data structures and pandas manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fddf698",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'student name'\n",
    "roll_number = 'student roll number'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c9d32a",
   "metadata": {},
   "source": [
    "## Sum of Unique Elements\n",
    "\n",
    "Return the sum of unique elements in a list (each element counted once)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b87465",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_one(nums: list[int]) -> int:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e097504c",
   "metadata": {},
   "source": [
    "## Reverse List\n",
    "\n",
    "Return a new list that is the reverse of input list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c326cfdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_two(xs: list) -> list:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d98e97",
   "metadata": {},
   "source": [
    "## Count Occurrences\n",
    "\n",
    "Return a dict mapping element -> count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "599ff8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_three(xs: list) -> dict:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23d1e8b",
   "metadata": {},
   "source": [
    "## Flatten Nested List (one level)\n",
    "\n",
    "Flatten a list containing lists by one level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65899fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_four(nested: list[list]) -> list:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24847107",
   "metadata": {},
   "source": [
    "## Invert Dictionary (values -> list of keys)\n",
    "\n",
    "Invert a dict mapping k->v into v->list_of_k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c88d1112",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_five(d: dict) -> dict:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d66b889",
   "metadata": {},
   "source": [
    "## Merge Dicts Summing Values\n",
    "\n",
    "Given two dicts with numeric values, return merged dict summing values for same keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17b1bfa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_six(a: dict, b: dict) -> dict:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4dff695",
   "metadata": {},
   "source": [
    "## Top-K Frequent Elements (small k)\n",
    "\n",
    "Return top k frequent elements (order not important)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72e1b78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_seven(nums: list[int], k: int) -> list[int]:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70553f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = question_seven([1, 1, 2, 2, 2, 3], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2325d7",
   "metadata": {},
   "source": [
    "## Split Words from Sentence\n",
    "\n",
    "Split a sentence into words (split on whitespace)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508e4ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_eight(s: str) -> list[str]:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb38de3",
   "metadata": {},
   "source": [
    "## Join Words with Separator\n",
    "\n",
    "Join a list of words with given separator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45eece31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_nine(words: list[str], sep: str=' ') -> str:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995be44a",
   "metadata": {},
   "source": [
    "## Longest Word Length\n",
    "\n",
    "Return length of the longest word in sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f4c025",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_ten(sentence: str) -> int:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee342af1",
   "metadata": {},
   "source": [
    "## Set Intersection\n",
    "\n",
    "Return intersection of two lists as a set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "387d19e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_eleven(a: list, b: list) -> set:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7648ab",
   "metadata": {},
   "source": [
    "## Deduplicate Preserve Order\n",
    "\n",
    "Remove duplicates preserving the first occurrence order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13af69b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_twelve(xs: list) -> list:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ddf28a6",
   "metadata": {},
   "source": [
    "## Square Each Element (map)\n",
    "\n",
    "Return list of squares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f96fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_thirteen(nums: list[int]) -> list[int]:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e127c7a",
   "metadata": {},
   "source": [
    "## Filter Even Numbers\n",
    "\n",
    "Return only even numbers in original order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d4ff569",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_fourteen(nums: list[int]) -> list[int]:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350fb552",
   "metadata": {},
   "source": [
    "## Pandas Series value_counts\n",
    "\n",
    "Given a list, return a pandas Series of value_counts sorted by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd8b1042",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_fifteen(values: list) -> 'pd.Series':\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5ccc6c",
   "metadata": {},
   "source": [
    "## Pandas: Split 'name' column into first/last\n",
    "\n",
    "Given DataFrame with column 'name' = 'First Last', split into 'first' and 'last' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45a8096a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_sixteen(df: 'pd.DataFrame') -> 'pd.DataFrame':\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891b95ab",
   "metadata": {},
   "source": [
    "## Pandas: Merge two DataFrames on key 'id'\n",
    "\n",
    "Perform inner merge on 'id' and return merged DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "346f9620",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_seventeen(a: 'pd.DataFrame', b: 'pd.DataFrame') -> 'pd.DataFrame':\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcc6e0f",
   "metadata": {},
   "source": [
    "## Pandas: Concatenate DataFrames vertically\n",
    "\n",
    "Return concatenated DataFrame with reset index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9031770f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_eighteen(dfs: list) -> 'pd.DataFrame':\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a2ada1",
   "metadata": {},
   "source": [
    "## Pandas: GroupBy Sum\n",
    "\n",
    "Group by 'key' and sum numeric column 'val'. Return DataFrame sorted by key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "061403a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_nineteen(df: 'pd.DataFrame') -> 'pd.DataFrame':\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b1800e",
   "metadata": {},
   "source": [
    "## Pandas: Fill missing values\n",
    "\n",
    "Fill NA in numeric column 'num' with given fill_value and return DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30ed60d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def question_twenty(df: 'pd.DataFrame', fill_value=0) -> 'pd.DataFrame':\n",
    "    pass"
   ]
  }
 ],
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