module-1-programming/SOLUTIONS_Day1_1_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f351b15-fdc1-45e4-adaf-bf82b5c4e655",
   "metadata": {},
   "source": [
    "# Jupyter Notebook\n",
    "---\n",
    "Jupyter notebooks let you interactively run code and inspect your work in \"blocks (cells).\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a27436-83ab-4c3a-b8ec-da8d2844a759",
   "metadata": {},
   "source": [
    "## Interface\n",
    "\n",
    "#### Kernel\n",
    "- Restart the Kernel: Use this option if your notebook is stuck or you want to clear all variables and start fresh.\n",
    "- Shut Down the Kernel: Shutting down a kernel stops the execution of the notebook but retains its content.\n",
    "- Select a Kernel: You can switch between different kernels (e.g., Python, R) if you have multiple environments installed.\n",
    "\n",
    "#### Cell\n",
    "- add / delete\n",
    "- copy / paste\n",
    "- move\n",
    "- undo / redo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dff2662-db6f-4883-9282-da62687051ad",
   "metadata": {},
   "source": [
    "## Operations\n",
    "\n",
    "#### Switching Modes:\n",
    "- Press `Enter` to enter Edit Mode (where you can type inside a cell).\n",
    "- Press `Esc` to exit Edit Mode and return to Command Mode (where you can use shortcuts to manipulate cells).\n",
    "\n",
    "#### Running a Cell:\n",
    "- Press `Shift-Enter` to execute the code or render text in the current cell and automatically move to the next cell.\n",
    "\n",
    "#### Changing Cell Type:\n",
    "- Press `y` to change the current cell to a Code cell (used for writing code).\n",
    "- Press `m` to change the current cell to a Markdown cell (used for writing formatted text, headings, lists, etc.).\n",
    "\n",
    "#### Adding New Cells:\n",
    "- Press `a` to create a new cell above the current one.\n",
    "- Press `b` to create a new cell below the current one.\n",
    "\n",
    "#### Deleting a Cell:\n",
    "- Press d twice (`d-d`) while in Command Mode to delete the current cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dbe2206b-1bc8-4e15-8af3-9f9cc0f22daa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "print('Hello world')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48bfefe6-909c-423e-a6cd-da849a71ad4b",
   "metadata": {},
   "source": [
    "# Fundamentals\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3225e4d5-0be0-46e1-a2c8-9e399b21d727",
   "metadata": {},
   "source": [
    "## Variables\n",
    "- integer\n",
    "- float\n",
    "- boolean\n",
    "- string"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c70611a-6041-421c-a309-9ff9965c5aa9",
   "metadata": {},
   "source": [
    "### integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59022a75-9918-4d55-b834-c789274495ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "print(type(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "372c3e81-e4f8-4c01-bad8-5d6cd756c760",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "a = a + 3\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d54fc8ac-a3b7-46ab-b8cd-3e20da45c9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "a = a**2\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9977f687-e5bc-4c16-aeae-96e645af7a7e",
   "metadata": {},
   "source": [
    "### float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7303973c-4585-473b-9c25-d58ae6944c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n",
      "1.5\n"
     ]
    }
   ],
   "source": [
    "a = 1.5\n",
    "print(type(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf729a04-2e07-4d1e-b534-43232eded455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "a = a / 3\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b25131-4f5e-4295-b6f7-c7b81568f575",
   "metadata": {},
   "source": [
    "### boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "faed78af-261e-4437-83aa-b1cecb603d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = True\n",
      "b = False\n"
     ]
    }
   ],
   "source": [
    "a = True\n",
    "b = False\n",
    "print('a =', a)\n",
    "print('b =', b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a7866d6-9af1-428e-a92b-7db4bc6eb84f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(a & b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0402c8d-be39-450f-ab89-067fe5bda114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(a | b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a75785-0506-4b11-9cb2-60e8109bdd69",
   "metadata": {},
   "source": [
    "### string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bfa69db3-ab94-4cec-989d-69c7855b6d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "This is a string\n"
     ]
    }
   ],
   "source": [
    "a = 'This is a string'\n",
    "print(type(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b92910d-4352-4845-b7c9-e1886543b086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "print(len(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f1ed38be-8b31-4241-a0ab-e2240570ed2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a string, please enjoy it\n"
     ]
    }
   ],
   "source": [
    "b = ', please enjoy it'\n",
    "print(a + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03bd9a98-53c2-4ba7-928d-c42a13af32ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c98e788-eace-4f07-81d9-8a282f31453e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:4] # a[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dc3ba-cbc1-4fa7-8731-7508ded53871",
   "metadata": {},
   "source": [
    "## Data Types and Structures\n",
    "- List: Ordered collection of items\n",
    "- Set: Unordered collection of unique items\n",
    "- Tuple: Ordered, immutable collection of items\n",
    "- Dictionary: Unordered collection of key-value pairs\n",
    "\n",
    "| Feature | List | Set | Tuple | Dict |\n",
    "| --- | --- | --- | --- | --- |\n",
    "| Ordered | Yes | No | Yes | No |\n",
    "| Indexing | Yes | No | Yes | Keys act as indexes |\n",
    "| Mutable | Yes | Yes | No | Yes |\n",
    "| Allows Duplicates | Yes | No | Yes | Keys: No, Values: Yes |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738e50fb-e27f-49e9-8244-fc6d4330f00c",
   "metadata": {},
   "source": [
    "### list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "802d5728-c564-459b-b5a6-903ad55d8bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4]\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "53302498-16ba-4f2c-8c9c-1ff0e21a51fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# indexing\n",
    "\n",
    "print(l[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07245943-1369-447e-8f71-29de211e285d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 4]\n"
     ]
    }
   ],
   "source": [
    "# mutable\n",
    "\n",
    "l[2] = 5\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27fba1dd-2448-4ab1-bcb6-776b7265f3dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "# duplicates\n",
    "\n",
    "l.append(6)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0691e848-a1cd-4453-b3f1-70500a2137ad",
   "metadata": {},
   "source": [
    "### set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f772374-8658-4c47-b329-25f76d1570b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4}\n"
     ]
    }
   ],
   "source": [
    "s = set({1, 2, 3, 4})\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc8c0a6f-faf9-45f7-bda2-3933c55148b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5}\n"
     ]
    }
   ],
   "source": [
    "# mutable\n",
    "\n",
    "s.add(5)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc159759-3236-40af-8511-ecf99b4a1714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 5}\n"
     ]
    }
   ],
   "source": [
    "s.remove(4)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bebc993-eb52-446a-8ac5-0dbcac17a184",
   "metadata": {},
   "source": [
    "### tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "81f75f07-40e2-4539-81ab-deee47e8e21e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4)\n"
     ]
    }
   ],
   "source": [
    "t = tuple([1, 2, 3, 4])\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c0693ddc-3661-4b7d-9a02-cf40e3ec09a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# indexing\n",
    "\n",
    "print(t[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71cf7700-9183-408e-8196-680ee488dc4a",
   "metadata": {},
   "source": [
    "### dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ee03289-2d22-4c73-8c96-7e96f6d0676b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 1, 'b': 2, 'c': 3}\n"
     ]
    }
   ],
   "source": [
    "d = dict({'a':1, 'b':2, 'c':3})\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2bde9030-f51d-4929-81b3-9f861cf65b9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# indexing\n",
    "\n",
    "print(d['c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "029c1462-bf7c-4bf8-859e-01c9c05bcc27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 1, 'b': 4, 'c': 3}\n"
     ]
    }
   ],
   "source": [
    "# mutable\n",
    "\n",
    "d['b'] = 4\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ddf330d-d98d-44ca-9ccd-b24206e8de6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 1, 'b': 4, 'c': 3, 'd': 1}\n"
     ]
    }
   ],
   "source": [
    "# duplicates\n",
    "\n",
    "d['d'] = 1\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e560670b-c97f-44cc-9bd0-8a7dfc6b6e43",
   "metadata": {},
   "source": [
    "## Conditional Statements\n",
    "\n",
    "Conditional statements allow your program to make decisions based on certain conditions.\n",
    "\n",
    "- `if`: Executes a block of code if the condition is `True`.\n",
    "- `elif`: (short for \"else if\") Checks another condition if the previous `if` or `elif` was `False`.\n",
    "- `else`: Executes a block of code if all previous conditions were `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4bd40ee7-a953-4eab-9b4a-c0fbddda4c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thank goodness it's Friday!\n"
     ]
    }
   ],
   "source": [
    "today = \"friday\"\n",
    "\n",
    "if today == \"friday\":\n",
    "    print(\"Thank goodness it's Friday!\")\n",
    "elif today == \"thursday\":\n",
    "    print(\"One more day to Friday!\")\n",
    "else:\n",
    "    print(\"Can't wait until it's Friday!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64687efb-fe8e-4409-a6f2-8dd6c7edab0a",
   "metadata": {},
   "source": [
    "## Looping and Iteration\n",
    "\n",
    "Looping allows you to execute a block of code repeatedly, either a specific number of times or until a condition is met.\n",
    "\n",
    "- `for` Loop: Used for iterating over a sequence (e.g., list, tuple, string, range, dictionary). Executes a block of code for each element in the sequence.\n",
    "\n",
    "- `while` Loop: Repeats as long as a specified condition evaluates to `True`. Useful when the number of iterations is not predetermined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ce9d720-333a-4e01-a177-41839c14ab43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# for loop\n",
    "\n",
    "s = [1, 2, 3, 4]\n",
    "for i in s:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "99060a5f-c40a-4a6b-b6a2-7985b7f0ffeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# while loop\n",
    "\n",
    "k = 0\n",
    "while k < 10:\n",
    "    print(k)\n",
    "    k += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a9bd9e-3cea-44dc-a6f9-433d683fac11",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "A function is a block of reusable code that performs a specific task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6bc9fd7b-ed73-4c81-a29c-7a411e7532a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "def F(x):\n",
    "    return x + 2\n",
    "\n",
    "x = 1\n",
    "print(F(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1e0dcdc3-e1d4-4eb7-94ab-7c1d51fb1c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is even\n"
     ]
    }
   ],
   "source": [
    "def F(x):\n",
    "    if x%2 == 0:\n",
    "        print(\"x is even\")\n",
    "    else:\n",
    "        print(\"x is odd\")\n",
    "\n",
    "x = 4\n",
    "F(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92eb90f4-575b-44be-8221-44431641af94",
   "metadata": {},
   "source": [
    "**NOW YOU TRY: Write a Function to List All Even Numbers**\n",
    "\n",
    "Task:\n",
    "Create a function that takes a list of numbers as input and return a list of all even numbers from the input.\n",
    "\n",
    "The function should:\n",
    "- Accept a single list of numbers as input.\n",
    "- Use the modulo operator `%` to check if a number is even.\n",
    "- Return a new list containing only the even numbers from the input list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e5961e2-0078-472c-90fa-774a4ca00093",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "def ListEven(li):\n",
    "    even_li = list()\n",
    "    for i in li:\n",
    "        if i%2 == 0:\n",
    "            even_li.append(i)\n",
    "    return even_li\n",
    "\n",
    "li = [1,2,3,4,5,6]\n",
    "even_li = ListEven(li)\n",
    "print(even_li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d46d089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 6]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list comprehension\n",
    "[i for i in li if i%2==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbfe2f7-aab5-4eae-8da1-5c610069a778",
   "metadata": {},
   "source": [
    "# Packages\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8d6fa2b1-1755-45bd-a2e9-6492c7309c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f114acb2-474d-401f-ba72-2f2d703c951d",
   "metadata": {},
   "source": [
    "## Numpy\n",
    "A library for high-performance operations on multi-dimensional arrays and matrices, along with mathematical functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a31376f5-2915-43f7-82b2-c0cf6d50d472",
   "metadata": {},
   "outputs": [],
   "source": [
    "li = [1,2,3,4]\n",
    "arr = np.array(li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e661e1e3-6598-40dc-80b3-0d6be6d03733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(10)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum\n",
    "\n",
    "np.sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2548c63-4fec-4cb5-9f40-1df824402451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean\n",
    "\n",
    "np.mean(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b63e3c6-8e43-436c-a46b-b2b251654dfa",
   "metadata": {},
   "source": [
    "**NOW YOU TRY: Write a Function to Calculate Standard Deviation**\n",
    "\n",
    "Task:\n",
    "Create a function that takes a list of numbers as input and return the standard deviation (STD).\n",
    "\n",
    "$$\n",
    "\\sigma = \\sqrt{\\frac{1}{N} \\sum_{i=1}^{N} (x_i - \\mu)^2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fbb76a75-6341-4150-bbc3-1215eb78612f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.118033988749895)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def STD(li):\n",
    "    # calculate the mean\n",
    "    mu = sum(li)/len(li)\n",
    "    \n",
    "    # calculate the squared difference\n",
    "    diff = [(i-mu)**2 for i in li]\n",
    "    \n",
    "    # compute the average of squared difference\n",
    "    avg_diff = sum(diff) / len(diff)\n",
    "    \n",
    "    # take the square root\n",
    "    std = avg_diff**0.5\n",
    "    \n",
    "    return std\n",
    "\n",
    "\n",
    "STD(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e37f098f-ae55-42a5-b9db-cc1cc8dde9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.118033988749895)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using numpy\n",
    "\n",
    "np.std(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e1e1ab-ca08-4948-b0fb-ca3f384dbdd6",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "A library for data manipulation and analysis, particularly for tabular data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "37718a55-43ce-4d94-925c-44404a952bbc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# read a tsv file\n",
    "file = 'data/gene_chrom.tsv'\n",
    "gene_chrom_table = pd.read_csv(file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded6e832-6058-46e1-a8fb-13292ea0f723",
   "metadata": {},
   "source": [
    "### check out the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0726aae8-b66b-4bc7-b897-18b354f65d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30619, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3a1d8a6d-0aa9-4e38-88c8-9f4ef3d870ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geneSymbol', 'chromosome'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5608ea25-7025-4177-bab4-f23fb58140c5",
   "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>geneSymbol</th>\n",
       "      <th>chromosome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OR4F5</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DQ597235</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DQ599768</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOC100132062</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     geneSymbol chromosome\n",
       "0       DDX11L1       chr1\n",
       "1         OR4F5       chr1\n",
       "2      DQ597235       chr1\n",
       "3      DQ599768       chr1\n",
       "4  LOC100132062       chr1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2074e61d-51af-4dd3-84fb-723987dfadbe",
   "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>geneSymbol</th>\n",
       "      <th>chromosome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30614</th>\n",
       "      <td>U3</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30615</th>\n",
       "      <td>SNORD112</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30616</th>\n",
       "      <td>U7</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30617</th>\n",
       "      <td>Mir_105</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30618</th>\n",
       "      <td>U6</td>\n",
       "      <td>chrY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      geneSymbol chromosome\n",
       "30614         U3       chrX\n",
       "30615   SNORD112       chrX\n",
       "30616         U7       chrX\n",
       "30617    Mir_105       chrX\n",
       "30618         U6       chrY"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156154f3-aae1-434d-83cf-d6dbfce9c155",
   "metadata": {},
   "source": [
    "### indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1464f373-5c4a-4e07-9c66-e8498e62f2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geneSymbol    DQ599768\n",
       "chromosome        chr1\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d5049069-4fbb-45a0-ae58-4255ac68bb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DQ599768'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.loc[3, 'geneSymbol']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc49329-1658-4062-beb7-3a72626de91c",
   "metadata": {},
   "source": [
    "### filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "897112d6-2aea-4678-a98c-76f91f25b8f7",
   "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",
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       "    }\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>geneSymbol</th>\n",
       "      <th>chromosome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>TP53</td>\n",
       "      <td>chr17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      geneSymbol chromosome\n",
       "11939       TP53      chr17"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table[gene_chrom_table.geneSymbol == 'TP53']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "18286285-8db1-4949-bd38-8f4c37634745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>geneSymbol</th>\n",
       "      <th>chromosome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>TP53</td>\n",
       "      <td>chr17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12301</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>chr17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      geneSymbol chromosome\n",
       "11939       TP53      chr17\n",
       "12301      BRCA1      chr17"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table[(gene_chrom_table.geneSymbol == 'TP53') | (gene_chrom_table.geneSymbol == 'BRCA1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "14c104cb-c63a-4c83-bdf1-d12b6978cfb1",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geneSymbol</th>\n",
       "      <th>chromosome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>TP53</td>\n",
       "      <td>chr17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12301</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>chr17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      geneSymbol chromosome\n",
       "11939       TP53      chr17\n",
       "12301      BRCA1      chr17"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table[gene_chrom_table.geneSymbol.isin(['TP53', 'BRCA1'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13157eca-2773-4574-aa3f-2f98ce15045e",
   "metadata": {},
   "source": [
    "### grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6881e947-1029-4efc-85ca-398c786a7f56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geneSymbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chromosome</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>2735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr10</th>\n",
       "      <td>1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr11</th>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr12</th>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr13</th>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr14</th>\n",
       "      <td>1048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr15</th>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr16</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr17</th>\n",
       "      <td>1647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr17_ctg5_hap1</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr17_gl000204_random</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr17_gl000205_random</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr18</th>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr19</th>\n",
       "      <td>1862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr19_gl000209_random</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1_gl000191_random</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr1_gl000192_random</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr2</th>\n",
       "      <td>1763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr20</th>\n",
       "      <td>754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr21</th>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr22</th>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3</th>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr4</th>\n",
       "      <td>1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr4_ctg9_hap1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr4_gl000193_random</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr4_gl000194_random</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr5</th>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6</th>\n",
       "      <td>1574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_apd_hap1</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_cox_hap2</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_dbb_hap3</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_mann_hap4</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_mcf_hap5</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_qbl_hap6</th>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr6_ssto_hap7</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr7</th>\n",
       "      <td>1469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr7_gl000195_random</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr8</th>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr9</th>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr9_gl000201_random</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrM</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000211</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000212</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000213</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000214</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000218</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000219</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000220</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000221</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000222</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000223</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000227</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000228</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000229</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000237</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000241</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000243</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrUn_gl000247</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrY</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       geneSymbol\n",
       "chromosome                       \n",
       "chr1                         2735\n",
       "chr10                        1162\n",
       "chr11                        1817\n",
       "chr12                        1382\n",
       "chr13                         566\n",
       "chr14                        1048\n",
       "chr15                        1441\n",
       "chr16                        1148\n",
       "chr17                        1647\n",
       "chr17_ctg5_hap1                26\n",
       "chr17_gl000204_random           1\n",
       "chr17_gl000205_random           1\n",
       "chr18                         458\n",
       "chr19                        1862\n",
       "chr19_gl000209_random          10\n",
       "chr1_gl000191_random            3\n",
       "chr1_gl000192_random            7\n",
       "chr2                         1763\n",
       "chr20                         754\n",
       "chr21                         362\n",
       "chr22                         729\n",
       "chr3                         1484\n",
       "chr4                         1068\n",
       "chr4_ctg9_hap1                  5\n",
       "chr4_gl000193_random            1\n",
       "chr4_gl000194_random            3\n",
       "chr5                         1233\n",
       "chr6                         1574\n",
       "chr6_apd_hap1                 109\n",
       "chr6_cox_hap2                 217\n",
       "chr6_dbb_hap3                 194\n",
       "chr6_mann_hap4                171\n",
       "chr6_mcf_hap5                 189\n",
       "chr6_qbl_hap6                 203\n",
       "chr6_ssto_hap7                182\n",
       "chr7                         1469\n",
       "chr7_gl000195_random            3\n",
       "chr8                         1004\n",
       "chr9                         1237\n",
       "chr9_gl000201_random            1\n",
       "chrM                           20\n",
       "chrUn_gl000211                  2\n",
       "chrUn_gl000212                  2\n",
       "chrUn_gl000213                  4\n",
       "chrUn_gl000214                  1\n",
       "chrUn_gl000218                  2\n",
       "chrUn_gl000219                  3\n",
       "chrUn_gl000220                  7\n",
       "chrUn_gl000221                  3\n",
       "chrUn_gl000222                  4\n",
       "chrUn_gl000223                  7\n",
       "chrUn_gl000227                  1\n",
       "chrUn_gl000228                  7\n",
       "chrUn_gl000229                  1\n",
       "chrUn_gl000237                  1\n",
       "chrUn_gl000241                  1\n",
       "chrUn_gl000243                  2\n",
       "chrUn_gl000247                  1\n",
       "chrX                         1148\n",
       "chrY                          133"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_chrom_table.groupby('chromosome').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d42ae7-c852-4129-bcc0-86bf946f89c5",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab83c8a-995f-497a-83d5-edf7e8aee3b2",
   "metadata": {},
   "source": [
    "### read an intypical tsv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b2b529da-f342-4507-b4dc-a8d6d208916c",
   "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>chrom</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>249,698,942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>242,508,799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>198,450,956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>190,424,264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>181,630,948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom       length\n",
       "0     1  249,698,942\n",
       "1     2  242,508,799\n",
       "2     3  198,450,956\n",
       "3     4  190,424,264\n",
       "4     5  181,630,948"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'data/chrom_lengths.tsv'\n",
    "chrom_length_table = pd.read_csv(file, sep=' ', names=['chrom', 'length'])\n",
    "chrom_length_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3427a314-0946-477f-9b90-2ec983c53bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chrom_length_table['length'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c153cb9b-2d77-4772-87c9-9bb6fcf95dc6",
   "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>chrom</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>249698942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>242508799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>198450956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>190424264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>181630948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom     length\n",
       "0     1  249698942\n",
       "1     2  242508799\n",
       "2     3  198450956\n",
       "3     4  190424264\n",
       "4     5  181630948"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# string conversion of numbers\n",
    "chrom_length_table = pd.read_csv(file, sep=' ', names=['chrom', 'length'], thousands=',')\n",
    "chrom_length_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c5e9d7b7-e4c3-4eda-9a2a-4a91932d1b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chrom_length_table['length'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19b4768-634c-42a3-8b9f-e9d637b604d3",
   "metadata": {},
   "source": [
    "### modify data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3ecfe17f-dee6-40d7-ae41-c7a405912fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      chr1\n",
       "1      chr2\n",
       "2      chr3\n",
       "3      chr4\n",
       "4      chr5\n",
       "5      chr6\n",
       "6      chr7\n",
       "7      chr8\n",
       "8      chr9\n",
       "9     chr10\n",
       "10    chr11\n",
       "11    chr12\n",
       "12    chr13\n",
       "13    chr14\n",
       "14    chr15\n",
       "15    chr16\n",
       "16    chr17\n",
       "17    chr18\n",
       "18    chr19\n",
       "19    chr20\n",
       "20    chr21\n",
       "21    chr22\n",
       "22     chrX\n",
       "23     chrY\n",
       "Name: chrom, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# string\n",
    "'chr' + chrom_length_table['chrom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4e99c8c2-8393-4128-af20-6315a26d98d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     249.698942\n",
       "1     242.508799\n",
       "2     198.450956\n",
       "3     190.424264\n",
       "4     181.630948\n",
       "5     170.805979\n",
       "6     159.345973\n",
       "7     145.138636\n",
       "8     138.688728\n",
       "9     133.797422\n",
       "10    135.186938\n",
       "11    133.275309\n",
       "12    114.364328\n",
       "13    108.136338\n",
       "14    102.439437\n",
       "15     92.211104\n",
       "16     83.836422\n",
       "17     80.373285\n",
       "18     58.617616\n",
       "19     64.444167\n",
       "20     46.709983\n",
       "21     51.857516\n",
       "22    156.040895\n",
       "23     57.264655\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# float\n",
    "chrom_length_table['length']/(10**6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "acbc8d83-053b-41c6-b5ed-fe5a82bc558d",
   "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>chrom</th>\n",
       "      <th>length</th>\n",
       "      <th>length(Mb)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>249698942</td>\n",
       "      <td>249.698942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>242508799</td>\n",
       "      <td>242.508799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>198450956</td>\n",
       "      <td>198.450956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>190424264</td>\n",
       "      <td>190.424264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>181630948</td>\n",
       "      <td>181.630948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom     length  length(Mb)\n",
       "0     1  249698942  249.698942\n",
       "1     2  242508799  242.508799\n",
       "2     3  198450956  198.450956\n",
       "3     4  190424264  190.424264\n",
       "4     5  181630948  181.630948"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chrom_length_table['length(Mb)'] = chrom_length_table['length']/(10**6)\n",
    "chrom_length_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb553e3-cf3d-4c2d-82d6-f1f4208134f1",
   "metadata": {},
   "source": [
    "### reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3b99ea39-d605-4bca-9977-d9ff665f779e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>length(Mb)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrom</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>64444167</td>\n",
       "      <td>64.444167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>46709983</td>\n",
       "      <td>46.709983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51857516</td>\n",
       "      <td>51.857516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>156040895</td>\n",
       "      <td>156.040895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>57264655</td>\n",
       "      <td>57.264655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          length  length(Mb)\n",
       "chrom                       \n",
       "20      64444167   64.444167\n",
       "21      46709983   46.709983\n",
       "22      51857516   51.857516\n",
       "X      156040895  156.040895\n",
       "Y       57264655   57.264655"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chrom_length_table = chrom_length_table.set_index('chrom')\n",
    "chrom_length_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "054a087f-a8d3-489a-90ce-4940268d7348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>length</th>\n",
       "      <th>length(Mb)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>64444167</td>\n",
       "      <td>64.444167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>46709983</td>\n",
       "      <td>46.709983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>51857516</td>\n",
       "      <td>51.857516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>X</td>\n",
       "      <td>156040895</td>\n",
       "      <td>156.040895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Y</td>\n",
       "      <td>57264655</td>\n",
       "      <td>57.264655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chrom     length  length(Mb)\n",
       "19    20   64444167   64.444167\n",
       "20    21   46709983   46.709983\n",
       "21    22   51857516   51.857516\n",
       "22     X  156040895  156.040895\n",
       "23     Y   57264655   57.264655"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chrom_length_table = chrom_length_table.reset_index()\n",
    "chrom_length_table.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8856cd8-e5ef-45e6-a6ed-9be5bf59c0a6",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc0fed9-9311-45a0-98c1-8e0e3b9b66e8",
   "metadata": {},
   "source": [
    "### Gene density"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8160be6-95fc-4e98-b245-a137ff938f5c",
   "metadata": {},
   "source": [
    "**NOW YOU TRY: Compute the Gene Density**\n",
    "\n",
    "Task: Compute the gene density for each chromosome.\n",
    "$$\n",
    "\\text{gene density} = \\frac{\\text{gene count}}{\\text{length (Mb)}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cfe62ecb-1a45-4b47-8bdc-3e72d53e55ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename chromosome in chrom_length_table\n",
    "chrom_length_table['chrom'] = 'chr' + chrom_length_table['chrom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9bc26e82-eb21-4b6d-ace1-4b29020da796",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get number of genes of each chromosome using groupby\n",
    "num_genes = gene_chrom_table.groupby('chromosome').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dcea4e16-d04c-490a-9746-882190916d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge tables\n",
    "merge_table = chrom_length_table.set_index('chrom')\n",
    "merge_table['num_genes'] = num_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "032adc5d-14ce-42fe-b244-fb9f8ed70be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute gene density\n",
    "merge_table['gene_density'] = merge_table['num_genes'] / merge_table['length(Mb)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c88d4801",
   "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>length</th>\n",
       "      <th>length(Mb)</th>\n",
       "      <th>num_genes</th>\n",
       "      <th>gene_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrom</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr1</th>\n",
       "      <td>249698942</td>\n",
       "      <td>249.698942</td>\n",
       "      <td>2735</td>\n",
       "      <td>10.953190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr2</th>\n",
       "      <td>242508799</td>\n",
       "      <td>242.508799</td>\n",
       "      <td>1763</td>\n",
       "      <td>7.269839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3</th>\n",
       "      <td>198450956</td>\n",
       "      <td>198.450956</td>\n",
       "      <td>1484</td>\n",
       "      <td>7.477918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr4</th>\n",
       "      <td>190424264</td>\n",
       "      <td>190.424264</td>\n",
       "      <td>1068</td>\n",
       "      <td>5.608529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr5</th>\n",
       "      <td>181630948</td>\n",
       "      <td>181.630948</td>\n",
       "      <td>1233</td>\n",
       "      <td>6.788491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          length  length(Mb)  num_genes  gene_density\n",
       "chrom                                                \n",
       "chr1   249698942  249.698942       2735     10.953190\n",
       "chr2   242508799  242.508799       1763      7.269839\n",
       "chr3   198450956  198.450956       1484      7.477918\n",
       "chr4   190424264  190.424264       1068      5.608529\n",
       "chr5   181630948  181.630948       1233      6.788491"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d816d1f-73cd-46c8-8883-a48afc483e94",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "A library for the creation of statistical plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7bbe3b-3999-49c3-8d4a-fd1d1342024d",
   "metadata": {},
   "source": [
    "### Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6a8d00b1-6275-4dfe-98f1-32402ea1a9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='chrom', ylabel='gene_density'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = merge_table.reset_index()\n",
    "sns.barplot(data=plot_df, x='chrom', y='gene_density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ffab0a92-bb0c-40a8-a178-91a71579f3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rotate the x-axis labels\n",
    "sns.barplot(data=plot_df, x='chrom', y='gene_density')\n",
    "_ = plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5def851-d0ca-492d-953d-3c449cb64de0",
   "metadata": {},
   "source": [
    "### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1d9639fa-4523-4647-bb33-cf4b098b99f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=[1,2,3,4], y=[4,3,2,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be80215-fc11-4834-a78b-30cade339899",
   "metadata": {},
   "source": [
    "**NOW YOU TRY: Visualize the Distribution of Gene Counts and Chromosome Lengths**\n",
    "\n",
    "Task: Create a scatter plot to visualize the relationship between gene counts (x-axis) and chromosome lengths (y-axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6e2e17e3-ea44-4909-a292-75afce7065fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='num_genes', ylabel='length(Mb)'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=plot_df, x='num_genes', y='length(Mb)')"
   ]
  }
 ],
 "metadata": {
  "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}