Introduction to Python A Tutorial in Jupyter Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7626848",
   "metadata": {},
   "source": [
    "1. VAJA: Introduction to Python: A Tutorial in Jupyter Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34cbf7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c9177d",
   "metadata": {},
   "source": [
    "Q1) Define a list containing the following floats: 1, 4.2, 3, 5.1, 8, 2, 0.7. Write a for-loop to iterate through each item in the list. For each item, if the value is greater than 5, add 1, and otherwise add 2. Append the sum for each item to a new list. Print the new list to the console output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d000481c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4.2, 3, 5.1, 8, 2, 0.7]\n"
     ]
    }
   ],
   "source": [
    "list = [1, 4.2, 3, 5.1, 8, 2, 0.7]\n",
    "print(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8cf12a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 6.2, 5, 6.1, 9, 4, 2.7]\n"
     ]
    }
   ],
   "source": [
    "new_list = []\n",
    "for i in list:\n",
    "    if i > 5:\n",
    "        i = i + 1\n",
    "        new_list.append(i)\n",
    "    else:\n",
    "        i = i + 2\n",
    "        new_list.append(i)\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1e7905",
   "metadata": {},
   "source": [
    "Q2) Define a dictionary with the following key-value pairs: (\"one-squared\", 1), (\"two\"squared\", 2), (\"three-squared\", 3), (\"four-squared\", 4), (\"five-squared\", 5). Write a for loop or list comprehension to iterate over each key-value pair and update the values to be equal to their square (i.e. to the power of two). Print the resulting dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "337b3b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one-squared': 1, 'two-squared': 2, 'three-squared': 3, 'four-squared': 4, 'five-squared': 5}\n"
     ]
    }
   ],
   "source": [
    "dictionary = {\n",
    "    \"one-squared\": 1,\n",
    "    \"two-squared\": 2,\n",
    "    \"three-squared\": 3,\n",
    "    \"four-squared\": 4,\n",
    "    \"five-squared\": 5,\n",
    "}\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2cb95ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([1, 2, 3, 4, 5])\n",
      "<class 'dict_values'>\n",
      "{'one-squared': 1, 'two-squared': 2, 'three-squared': 3, 'four-squared': 4, 'five-squared': 5}\n",
      "dict_values([1, 2, 3, 4, 5])\n",
      "dict_keys(['one-squared', 'two-squared', 'three-squared', 'four-squared', 'five-squared'])\n",
      "dict_items([('one-squared', 1), ('two-squared', 2), ('three-squared', 3), ('four-squared', 4), ('five-squared', 5)])\n"
     ]
    }
   ],
   "source": [
    "print(dictionary.values())\n",
    "print(type(dictionary.values()))\n",
    "print(dictionary)\n",
    "print(dictionary.values())\n",
    "print(dictionary.keys())\n",
    "print(dictionary.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6996bfcb",
   "metadata": {},
   "source": [
    "for zanka dela s seznamom, dictionary ni seznam, zato ga s končnico .item pretovoriš v seznam parov. Prvi element v paru je key (k), drugi element je value (v) - z uporabo for k, v ločiš par na dva dela in lahko spreminjaš vsak del posebej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1331881e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one-squared': 1, 'two-squared': 4, 'three-squared': 9, 'four-squared': 16, 'five-squared': 25}\n"
     ]
    }
   ],
   "source": [
    "new_dictionary = {}\n",
    "for k, v in dictionary.items():\n",
    "    new_dictionary[k] = v ** 2\n",
    "print(new_dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bcb6ac",
   "metadata": {},
   "source": [
    "Q3) Define a numpy array consisting of the numbers 1 to 20 (hint: use np.arange to assign the values). Write a function reverse() that takes a numpy array and returns an array containing the items in a reverse order (hint: you might need to use a for-loop). Bonus: is there a way to reverse an array in one line of code?\n",
    "\n",
    "NumPy provides:\n",
    "extension package to Python for multi-dimensional arrays\n",
    "closer to hardware (efficiency)\n",
    "designed for scientific computation (convenience)\n",
    "Also known as array oriented computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2401ca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.array(range(1, 21))\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90373139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3  2  1]\n"
     ]
    }
   ],
   "source": [
    "def reverse(arr):\n",
    "    reverse_array = arr[::-1]\n",
    "    print(reverse_array)\n",
    "\n",
    "reverse (array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a482e96f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "print(len(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06505a88",
   "metadata": {},
   "source": [
    "Q4) Below, we define a 4x4 numpy array. Using this array, compute the mean for each column. Plot a histogram of the column means using matplotlib (code provided).\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "150fcbf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.88 12.54 11.1  12.24 12.56 10.94 10.26 10.9  10.86  8.44 10.7   9.66\n",
      "  8.66 11.62 10.66  8.38 11.48 10.4   9.84  9.78 12.24 12.14  9.46 10.66\n",
      " 10.64 10.64 10.78 11.9  11.66 12.32 10.72 10.36 12.54  9.14 11.84 10.92\n",
      " 13.48 11.06 10.32 12.4  11.14 12.1   8.58 11.92  9.8   9.9  10.28 10.02\n",
      " 10.3  10.86 11.22 10.24 11.44 10.26  9.94  9.    9.58 12.38  9.94 10.42\n",
      " 12.86 11.62  9.22 10.28  9.92 10.84 11.62 10.72 10.   11.96 10.2  11.92\n",
      " 10.88 11.12 11.64 11.7   9.3   9.92 10.44 10.54 11.1   9.32 12.36 11.26\n",
      " 10.   12.08  9.3   9.94 11.06  8.64 10.64 11.46 11.22 11.72 13.08 12.84\n",
      " 10.38 10.14 12.2   9.98]\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "my_arr = np.array([[1.0, 22.0, 12.0, 22.0, 27.0, 14.0, 15.0, 14.0, 16.0, 5.0, 10.0, 4.0, 0.0, 24.0, 15.0, 14.0, 27.0, 2.0, 14.0, 24.0, 13.0, 8.0, 0.0, 12.0, 35.0, 3.0, 19.0, 11.0, 11.0, 9.0, 15.0, 11.0, 7.0, 7.0, 22.0, 13.0, 7.0, 18.0, 17.0, 18.0, 0.0, 3.0, 10.0, 8.0, 19.0, 9.0, 11.0, 21.0, 5.0, 10.0, 7.0, 15.0, 28.0, 29.0, 1.0, 8.0, 16.0, 2.0, 12.0, 5.0, 16.0, 14.0, 0.0, 27.0, 1.0, 28.0, 10.0, 21.0, 4.0, 8.0, 4.0, 10.0, 13.0, 26.0, 26.0, 8.0, 1.0, 4.0, 15.0, 11.0, 10.0, 21.0, 13.0, 26.0, 21.0, 16.0, 7.0, 13.0, 8.0, 1.0, 3.0, 10.0, 6.0, 4.0, 4.0, 25.0, 18.0, 18.0, 6.0, 0.0],\n",
    "[21.0, 13.0, 27.0, 8.0, 25.0, 18.0, 0.0, 5.0, 4.0, 24.0, 9.0, 18.0, 14.0, 29.0, 1.0, 2.0, 17.0, 20.0, 0.0, 15.0, 17.0, 11.0, 17.0, 12.0, 17.0, 7.0, 18.0, 5.0, 26.0, 30.0, 17.0, 12.0, 22.0, 6.0, 26.0, 1.0, 26.0, 24.0, 0.0, 19.0, 1.0, 10.0, 4.0, 11.0, 2.0, 8.0, 32.0, 6.0, 12.0, 12.0, 21.0, 18.0, 12.0, 12.0, 9.0, 4.0, 14.0, 0.0, 12.0, 1.0, 29.0, 10.0, 15.0, 10.0, 23.0, 16.0, 7.0, 30.0, 29.0, 27.0, 0.0, 17.0, 11.0, 2.0, 11.0, 7.0, 27.0, 14.0, 13.0, 16.0, 9.0, 7.0, 33.0, 0.0, 15.0, 1.0, 13.0, 8.0, 23.0, 11.0, 11.0, 27.0, 3.0, 7.0, 20.0, 0.0, 7.0, 19.0, 20.0, 16.0],\n",
    "[12.0, 10.0, 9.0, 11.0, 16.0, 1.0, 13.0, 15.0, 16.0, 21.0, 0.0, 24.0, 0.0, 9.0, 18.0, 15.0, 20.0, 16.0, 0.0, 0.0, 19.0, 28.0, 23.0, 16.0, 24.0, 13.0, 34.0, 18.0, 10.0, 15.0, 7.0, 14.0, 22.0, 1.0, 16.0, 7.0, 30.0, 1.0, 29.0, 17.0, 0.0, 15.0, 2.0, 8.0, 8.0, 0.0, 0.0, 9.0, 14.0, 18.0, 11.0, 11.0, 17.0, 2.0, 25.0, 7.0, 22.0, 21.0, 5.0, 7.0, 21.0, 22.0, 10.0, 19.0, 7.0, 0.0, 8.0, 12.0, 0.0, 0.0, 16.0, 25.0, 20.0, 9.0, 15.0, 5.0, 14.0, 0.0, 11.0, 1.0, 27.0, 0.0, 11.0, 3.0, 17.0, 8.0, 22.0, 28.0, 13.0, 2.0, 1.0, 18.0, 3.0, 16.0, 16.0, 7.0, 1.0, 4.0, 16.0, 2.0],\n",
    "[11.0, 12.0, 2.0, 7.0, 0.0, 0.0, 5.0, 8.0, 0.0, 0.0, 11.0, 5.0, 0.0, 0.0, 11.0, 25.0, 19.0, 16.0, 0.0, 0.0, 20.0, 0.0, 10.0, 14.0, 7.0, 1.0, 12.0, 12.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 14.0, 6.0, 0.0, 17.0, 7.0, 0.0, 1.0, 7.0, 4.0, 14.0, 2.0, 0.0, 7.0, 0.0, 0.0, 13.0, 0.0, 8.0, 23.0, 11.0, 30.0, 6.0, 0.0, 0.0, 17.0, 0.0, 0.0, 0.0, 5.0, 0.0, 0.0, 3.0, 16.0, 6.0, 3.0, 0.0, 5.0, 8.0, 22.0, 22.0, 17.0, 0.0, 3.0, 0.0, 11.0, 11.0, 21.0, 15.0, 6.0, 1.0, 6.0, 0.0, 0.0, 11.0, 6.0, 15.0, 5.0, 8.0, 2.0, 5.0, 10.0, 8.0, 7.0, 0.0],\n",
    "[23.0, 12.0, 12.0, 24.0, 1.0, 7.0, 7.0, 16.0, 0.0, 39.0, 13.0, 1.0, 24.0, 0.0, 0.0, 10.0, 8.0, 0.0, 15.0, 12.0, 0.0, 27.0, 0.0, 17.0, 9.0, 24.0, 2.0, 7.0, 8.0, 14.0, 20.0, 15.0, 12.0, 9.0, 7.0, 19.0, 4.0, 8.0, 0.0, 5.0, 3.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 12.0, 24.0, 15.0, 0.0, 1.0, 10.0, 0.0, 8.0, 8.0, 19.0, 13.0, 20.0, 24.0, 0.0, 20.0, 4.0, 0.0, 7.0, 12.0, 17.0, 16.0, 21.0, 0.0, 5.0, 21.0, 0.0, 5.0, 1.0, 20.0, 0.0, 0.0, 3.0, 19.0, 10.0, 23.0, 0.0, 19.0, 25.0, 6.0, 1.0, 26.0, 8.0, 0.0, 9.0, 4.0, 0.0, 0.0, 13.0, 17.0, 0.0, 0.0, 9.0, 10.0],\n",
    "[6.0, 10.0, 12.0, 5.0, 32.0, 0.0, 20.0, 18.0, 37.0, 10.0, 18.0, 0.0, 0.0, 0.0, 10.0, 20.0, 27.0, 0.0, 22.0, 5.0, 3.0, 4.0, 24.0, 0.0, 10.0, 0.0, 20.0, 13.0, 23.0, 2.0, 30.0, 25.0, 15.0, 0.0, 25.0, 14.0, 10.0, 15.0, 6.0, 27.0, 11.0, 14.0, 18.0, 1.0, 0.0, 4.0, 2.0, 10.0, 12.0, 0.0, 0.0, 12.0, 8.0, 0.0, 8.0, 4.0, 5.0, 19.0, 13.0, 9.0, 23.0, 9.0, 15.0, 20.0, 20.0, 0.0, 22.0, 20.0, 18.0, 6.0, 15.0, 25.0, 17.0, 12.0, 20.0, 4.0, 7.0, 9.0, 0.0, 0.0, 17.0, 9.0, 6.0, 21.0, 0.0, 0.0, 0.0, 0.0, 24.0, 6.0, 9.0, 7.0, 14.0, 10.0, 9.0, 8.0, 15.0, 7.0, 4.0, 0.0],\n",
    "[17.0, 17.0, 5.0, 0.0, 0.0, 1.0, 7.0, 0.0, 0.0, 0.0, 14.0, 0.0, 4.0, 6.0, 0.0, 0.0, 0.0, 0.0, 15.0, 15.0, 12.0, 2.0, 2.0, 0.0, 0.0, 0.0, 14.0, 0.0, 1.0, 9.0, 7.0, 10.0, 16.0, 13.0, 0.0, 13.0, 11.0, 3.0, 0.0, 36.0, 7.0, 8.0, 14.0, 23.0, 4.0, 0.0, 1.0, 9.0, 6.0, 13.0, 3.0, 7.0, 0.0, 7.0, 1.0, 0.0, 3.0, 8.0, 6.0, 16.0, 22.0, 0.0, 0.0, 17.0, 5.0, 0.0, 9.0, 16.0, 0.0, 8.0, 8.0, 4.0, 2.0, 0.0, 5.0, 7.0, 4.0, 8.0, 2.0, 13.0, 0.0, 9.0, 2.0, 0.0, 16.0, 35.0, 0.0, 6.0, 0.0, 0.0, 8.0, 1.0, 5.0, 10.0, 0.0, 12.0, 13.0, 1.0, 7.0, 4.0],\n",
    "[11.0, 0.0, 0.0, 13.0, 23.0, 8.0, 20.0, 16.0, 10.0, 18.0, 8.0, 25.0, 20.0, 20.0, 5.0, 24.0, 0.0, 5.0, 6.0, 21.0, 0.0, 15.0, 14.0, 0.0, 16.0, 32.0, 26.0, 24.0, 16.0, 15.0, 13.0, 2.0, 24.0, 12.0, 20.0, 2.0, 20.0, 12.0, 5.0, 17.0, 10.0, 27.0, 27.0, 4.0, 11.0, 20.0, 4.0, 3.0, 10.0, 21.0, 18.0, 35.0, 17.0, 24.0, 13.0, 8.0, 15.0, 0.0, 14.0, 1.0, 13.0, 13.0, 6.0, 9.0, 24.0, 20.0, 16.0, 11.0, 16.0, 15.0, 16.0, 30.0, 0.0, 0.0, 10.0, 11.0, 6.0, 17.0, 8.0, 3.0, 25.0, 6.0, 0.0, 17.0, 22.0, 25.0, 15.0, 8.0, 24.0, 7.0, 14.0, 3.0, 24.0, 28.0, 7.0, 1.0, 11.0, 20.0, 36.0, 33.0],\n",
    "[0.0, 22.0, 29.0, 1.0, 11.0, 17.0, 6.0, 5.0, 5.0, 14.0, 9.0, 22.0, 6.0, 0.0, 0.0, 0.0, 6.0, 17.0, 12.0, 0.0, 20.0, 0.0, 15.0, 8.0, 0.0, 5.0, 13.0, 13.0, 3.0, 0.0, 2.0, 8.0, 7.0, 15.0, 6.0, 17.0, 21.0, 0.0, 7.0, 10.0, 11.0, 20.0, 8.0, 13.0, 35.0, 27.0, 16.0, 20.0, 7.0, 1.0, 3.0, 12.0, 23.0, 11.0, 2.0, 2.0, 3.0, 16.0, 7.0, 16.0, 8.0, 0.0, 20.0, 0.0, 3.0, 0.0, 8.0, 5.0, 11.0, 2.0, 24.0, 1.0, 3.0, 3.0, 12.0, 12.0, 10.0, 22.0, 5.0, 25.0, 11.0, 3.0, 0.0, 4.0, 0.0, 0.0, 13.0, 1.0, 0.0, 0.0, 8.0, 0.0, 5.0, 18.0, 13.0, 0.0, 7.0, 0.0, 24.0, 13.0],\n",
    "[7.0, 22.0, 16.0, 2.0, 8.0, 10.0, 0.0, 9.0, 9.0, 17.0, 15.0, 7.0, 7.0, 15.0, 6.0, 8.0, 0.0, 13.0, 15.0, 3.0, 19.0, 11.0, 0.0, 5.0, 8.0, 20.0, 1.0, 25.0, 0.0, 5.0, 6.0, 5.0, 25.0, 0.0, 10.0, 9.0, 4.0, 15.0, 4.0, 3.0, 15.0, 2.0, 24.0, 0.0, 16.0, 10.0, 10.0, 6.0, 4.0, 8.0, 23.0, 4.0, 34.0, 10.0, 12.0, 0.0, 11.0, 5.0, 0.0, 14.0, 15.0, 5.0, 9.0, 9.0, 15.0, 13.0, 24.0, 6.0, 0.0, 0.0, 25.0, 26.0, 22.0, 24.0, 24.0, 3.0, 21.0, 2.0, 9.0, 25.0, 2.0, 27.0, 23.0, 40.0, 5.0, 12.0, 7.0, 0.0, 0.0, 11.0, 3.0, 5.0, 15.0, 0.0, 21.0, 9.0, 16.0, 3.0, 9.0, 9.0],\n",
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    "[0.0, 15.0, 7.0, 16.0, 15.0, 15.0, 5.0, 4.0, 0.0, 5.0, 5.0, 1.0, 12.0, 8.0, 25.0, 0.0, 0.0, 3.0, 0.0, 7.0, 12.0, 19.0, 3.0, 8.0, 1.0, 18.0, 0.0, 0.0, 0.0, 10.0, 21.0, 5.0, 7.0, 1.0, 13.0, 0.0, 6.0, 0.0, 13.0, 13.0, 9.0, 25.0, 0.0, 13.0, 3.0, 0.0, 6.0, 8.0, 7.0, 0.0, 0.0, 3.0, 14.0, 4.0, 0.0, 18.0, 0.0, 16.0, 15.0, 0.0, 0.0, 7.0, 3.0, 0.0, 0.0, 0.0, 5.0, 0.0, 3.0, 19.0, 0.0, 5.0, 0.0, 2.0, 2.0, 0.0, 10.0, 0.0, 21.0, 1.0, 20.0, 3.0, 8.0, 22.0, 0.0, 2.0, 0.0, 0.0, 22.0, 2.0, 0.0, 7.0, 0.0, 1.0, 5.0, 2.0, 16.0, 12.0, 18.0, 7.0],\n",
    "[5.0, 17.0, 25.0, 0.0, 5.0, 8.0, 6.0, 8.0, 15.0, 0.0, 8.0, 15.0, 10.0, 0.0, 8.0, 1.0, 3.0, 5.0, 13.0, 11.0, 10.0, 0.0, 6.0, 8.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.0, 15.0, 0.0, 5.0, 5.0, 17.0, 11.0, 30.0, 17.0, 12.0, 15.0, 26.0, 9.0, 0.0, 1.0, 0.0, 15.0, 13.0, 0.0, 1.0, 6.0, 8.0, 20.0, 1.0, 0.0, 0.0, 0.0, 21.0, 4.0, 5.0, 0.0, 11.0, 7.0, 7.0, 0.0, 6.0, 15.0, 19.0, 9.0, 8.0, 4.0, 16.0, 7.0, 0.0, 9.0, 19.0, 0.0, 14.0, 12.0, 0.0, 13.0, 17.0, 0.0, 0.0, 0.0, 20.0, 11.0, 4.0, 8.0, 0.0, 5.0, 2.0, 9.0, 0.0, 13.0, 0.0, 0.0, 0.0, 11.0, 20.0],\n",
    "[24.0, 22.0, 0.0, 4.0, 3.0, 0.0, 9.0, 0.0, 16.0, 16.0, 0.0, 22.0, 7.0, 22.0, 24.0, 2.0, 19.0, 22.0, 17.0, 8.0, 9.0, 33.0, 3.0, 11.0, 17.0, 22.0, 11.0, 11.0, 8.0, 9.0, 15.0, 11.0, 8.0, 17.0, 3.0, 6.0, 0.0, 11.0, 18.0, 2.0, 22.0, 9.0, 5.0, 15.0, 4.0, 26.0, 3.0, 14.0, 16.0, 7.0, 30.0, 10.0, 9.0, 17.0, 5.0, 0.0, 0.0, 7.0, 2.0, 10.0, 10.0, 0.0, 6.0, 25.0, 0.0, 22.0, 12.0, 0.0, 14.0, 11.0, 0.0, 14.0, 14.0, 9.0, 11.0, 11.0, 15.0, 23.0, 20.0, 3.0, 0.0, 1.0, 0.0, 8.0, 13.0, 14.0, 18.0, 15.0, 25.0, 8.0, 6.0, 2.0, 21.0, 12.0, 32.0, 0.0, 22.0, 0.0, 10.0, 15.0],\n",
    "[10.0, 19.0, 10.0, 8.0, 31.0, 9.0, 9.0, 25.0, 0.0, 7.0, 0.0, 10.0, 0.0, 12.0, 14.0, 10.0, 30.0, 13.0, 6.0, 3.0, 13.0, 19.0, 11.0, 27.0, 23.0, 10.0, 3.0, 19.0, 5.0, 21.0, 7.0, 8.0, 19.0, 0.0, 10.0, 7.0, 8.0, 20.0, 7.0, 31.0, 23.0, 13.0, 8.0, 0.0, 5.0, 16.0, 18.0, 13.0, 20.0, 15.0, 17.0, 3.0, 12.0, 1.0, 25.0, 16.0, 7.0, 21.0, 12.0, 16.0, 17.0, 6.0, 0.0, 9.0, 10.0, 1.0, 14.0, 22.0, 1.0, 5.0, 14.0, 6.0, 25.0, 3.0, 26.0, 12.0, 1.0, 0.0, 9.0, 16.0, 12.0, 14.0, 14.0, 5.0, 21.0, 11.0, 16.0, 22.0, 22.0, 24.0, 6.0, 1.0, 11.0, 0.0, 20.0, 20.0, 18.0, 25.0, 6.0, 0.0]])\n",
    "\n",
    "column_means = my_arr.mean(axis=0)\n",
    "print(column_means)\n",
    "print(len(column_means))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "039a4c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(column_means, bins=20) # you must define column_means as a np.array of column means for my_arr\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b72cd6f",
   "metadata": {},
   "source": [
    "Q5) Use my_arr from the previous question. Define a variable count equal to zero. Write a for-loop to iterate through the values of my_arr. For each item, if it is greater than the column mean for its respective column, add 1 to count. What is the value of count? This should provide a total count of the number of values in my_arr that are greater than their column mean. How can we represent this as a percentage instead of a count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6ede177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2309\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "\n",
    "#print(my_arr.shape) # vrne (50, 100), kar pomeni, da array vsebuje 50 stolpcev (stolpci so to kar je znotraj []) in v vsakem stolpcu je po 100 vrednosti iz katerih se računa povprečje\n",
    "#print(my_arr.shape[0]) # to je 50 (50 je tudi stolpcev, za vsak stolpec se računa povprečje - imaš 50 povprečji)\n",
    "#print(my_arr.shape[1]) # to je 100, pomeni, da je v vsakem stolpcu 100 vrednosti\n",
    "\n",
    "for r in np.arange(my_arr.shape[0]): # za vsak stolpec v arrayju; r je indeks in ne vrednost!\n",
    "    for c in np.arange(my_arr.shape[1]): # za vsako vrednost v stolpcu; c je indeks in ne vrednost!\n",
    "        if my_arr[r, c] > column_means[c]: #greš notri v en stolpec (označuje ga trenutna vrednost r) in vse vrednosti v stolpcu (vsako vrednost označuje trenutna vrednost c) primerjaš s povprečjem stolpca\n",
    "            count+=1 # če je trenutna vrednost večja od povprečja, prišteješ vrednosti count 1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c360cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2309"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum((my_arr>column_means).flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b1682a",
   "metadata": {},
   "source": [
    "How can we represent this as a percentage instead of a count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "240b24f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2309\n",
      "5000\n",
      "Delez visjih vrednosti:  46.18  %\n"
     ]
    }
   ],
   "source": [
    "visje_vrednosti = 0\n",
    "vse_vrednosti = 0\n",
    "\n",
    "for r in np.arange(my_arr.shape[0]):\n",
    "    for c in np.arange(my_arr.shape[1]): \n",
    "        if my_arr[r, c] > column_means[c]: \n",
    "            visje_vrednosti+=1\n",
    "            vse_vrednosti+=1\n",
    "        else:\n",
    "            vse_vrednosti+=1\n",
    "print(visje_vrednosti)\n",
    "print(vse_vrednosti)\n",
    "razmerje = (visje_vrednosti/vse_vrednosti)*100\n",
    "\n",
    "print(\"Delez visjih vrednosti: \", razmerje , \" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f66d5a",
   "metadata": {},
   "source": [
    "We will now introduce pandas data frames. Use the new link below, to the pandas documentation, to answer the remaining questions. We have converted my_arr into a pandas data frame and defined names for the columns and rows. The ability to define the row and column names is one of the advantages for pandas data frames compared to numpy arrays.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html\n",
    "\n",
    "https://pandas.pydata.org/docs/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf45126b",
   "metadata": {},
   "source": [
    "pd.DataFrame ustvari tabelo iz arraya ali dictionaryja\n",
    ".T spremeni stolpce v vrstice in obratno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c3fdfaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2     3     4     5     6     7     8     9   ...    40  \\\n",
      "0    1.0  21.0  12.0  11.0  23.0   6.0  17.0  11.0   0.0   7.0  ...  15.0   \n",
      "1   22.0  13.0  10.0  12.0  12.0  10.0  17.0   0.0  22.0  22.0  ...  19.0   \n",
      "2   12.0  27.0   9.0   2.0  12.0  12.0   5.0   0.0  29.0  16.0  ...   5.0   \n",
      "3   22.0   8.0  11.0   7.0  24.0   5.0   0.0  13.0   1.0   2.0  ...  15.0   \n",
      "4   27.0  25.0  16.0   0.0   1.0  32.0   0.0  23.0  11.0   8.0  ...  12.0   \n",
      "..   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
      "95  25.0   0.0   7.0   5.0  17.0   8.0  12.0   1.0   0.0   9.0  ...  20.0   \n",
      "96  18.0   7.0   1.0  10.0   0.0  15.0  13.0  11.0   7.0  16.0  ...  13.0   \n",
      "97  18.0  19.0   4.0   8.0   0.0   7.0   1.0  20.0   0.0   3.0  ...  26.0   \n",
      "98   6.0  20.0  16.0   7.0   9.0   4.0   7.0  36.0  24.0   9.0  ...   3.0   \n",
      "99   0.0  16.0   2.0   0.0  10.0   0.0   4.0  33.0  13.0   9.0  ...   8.0   \n",
      "\n",
      "      41    42    43    44    45    46    47    48    49  \n",
      "0   13.0   0.0   0.0  17.0  15.0   0.0   5.0  24.0  10.0  \n",
      "1   15.0  21.0   0.0  11.0   0.0  15.0  17.0  22.0  19.0  \n",
      "2   16.0   0.0   0.0   0.0  21.0   7.0  25.0   0.0  10.0  \n",
      "3   22.0  14.0   0.0   6.0  23.0  16.0   0.0   4.0   8.0  \n",
      "4   19.0   4.0   0.0  17.0  31.0  15.0   5.0   3.0  31.0  \n",
      "..   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
      "95  27.0  15.0  26.0  31.0   0.0   2.0   0.0   0.0  20.0  \n",
      "96  23.0   3.0   2.0  20.0   5.0  16.0   0.0  22.0  18.0  \n",
      "97   0.0   9.0  27.0   2.0   0.0  12.0   0.0   0.0  25.0  \n",
      "98  12.0  16.0  13.0   6.0  15.0  18.0  11.0  10.0   6.0  \n",
      "99   8.0   6.0   5.0   0.0  22.0   7.0  20.0  15.0   0.0  \n",
      "\n",
      "[100 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(my_arr).T # T transforms the data frame, i.e. flips the rows and columns\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "830c9ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geni:  100\n",
      "Celice:  50\n"
     ]
    }
   ],
   "source": [
    "print(\"Geni: \", df.shape[0]) # df.shape[0] določi vrednost prve dimenzije, to so vrstice (v tem primeru geni) -> 50\n",
    "print(\"Celice: \", df.shape[1]) # df.shape[0] določi vrednost druge dimenzije, to so stolpci (v tem primeru celice) -> 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d150f5e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Cell0', 'Cell1', 'Cell2', 'Cell3', 'Cell4', 'Cell5', 'Cell6', 'Cell7',\n",
      "       'Cell8', 'Cell9', 'Cell10', 'Cell11', 'Cell12', 'Cell13', 'Cell14',\n",
      "       'Cell15', 'Cell16', 'Cell17', 'Cell18', 'Cell19', 'Cell20', 'Cell21',\n",
      "       'Cell22', 'Cell23', 'Cell24', 'Cell25', 'Cell26', 'Cell27', 'Cell28',\n",
      "       'Cell29', 'Cell30', 'Cell31', 'Cell32', 'Cell33', 'Cell34', 'Cell35',\n",
      "       'Cell36', 'Cell37', 'Cell38', 'Cell39', 'Cell40', 'Cell41', 'Cell42',\n",
      "       'Cell43', 'Cell44', 'Cell45', 'Cell46', 'Cell47', 'Cell48', 'Cell49',\n",
      "       'Cell50', 'Cell51', 'Cell52', 'Cell53', 'Cell54', 'Cell55', 'Cell56',\n",
      "       'Cell57', 'Cell58', 'Cell59', 'Cell60', 'Cell61', 'Cell62', 'Cell63',\n",
      "       'Cell64', 'Cell65', 'Cell66', 'Cell67', 'Cell68', 'Cell69', 'Cell70',\n",
      "       'Cell71', 'Cell72', 'Cell73', 'Cell74', 'Cell75', 'Cell76', 'Cell77',\n",
      "       'Cell78', 'Cell79', 'Cell80', 'Cell81', 'Cell82', 'Cell83', 'Cell84',\n",
      "       'Cell85', 'Cell86', 'Cell87', 'Cell88', 'Cell89', 'Cell90', 'Cell91',\n",
      "       'Cell92', 'Cell93', 'Cell94', 'Cell95', 'Cell96', 'Cell97', 'Cell98',\n",
      "       'Cell99'],\n",
      "      dtype='object')\n",
      "Index(['Gene0', 'Gene1', 'Gene2', 'Gene3', 'Gene4', 'Gene5', 'Gene6', 'Gene7',\n",
      "       'Gene8', 'Gene9', 'Gene10', 'Gene11', 'Gene12', 'Gene13', 'Gene14',\n",
      "       'Gene15', 'Gene16', 'Gene17', 'Gene18', 'Gene19', 'Gene20', 'Gene21',\n",
      "       'Gene22', 'Gene23', 'Gene24', 'Gene25', 'Gene26', 'Gene27', 'Gene28',\n",
      "       'Gene29', 'Gene30', 'Gene31', 'Gene32', 'Gene33', 'Gene34', 'Gene35',\n",
      "       'Gene36', 'Gene37', 'Gene38', 'Gene39', 'Gene40', 'Gene41', 'Gene42',\n",
      "       'Gene43', 'Gene44', 'Gene45', 'Gene46', 'Gene47', 'Gene48', 'Gene49'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df.index = [\"Cell\"+str(i) for i in range(0, df.shape[0])]\n",
    "df.columns = [\"Gene\"+str(i) for i in range(0, df.shape[1])]\n",
    "print(df.index)\n",
    "print(df.columns)\n",
    "# v 50 celicah gledaš po 100 genov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d812f4",
   "metadata": {},
   "source": [
    "Ukaza\n",
    "df.index = [\"Cell\"+str(i) for i in range(0, df.shape[0])]\n",
    "df.columns = [\"Gene\"+str(i) for i in range(0, df.shape[1])]\n",
    "lahko zapišeš kot svojo zanko"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fefe1a01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "celcie: \n",
      " ['Cell0', 'Cell1', 'Cell2', 'Cell3', 'Cell4', 'Cell5', 'Cell6', 'Cell7', 'Cell8', 'Cell9', 'Cell10', 'Cell11', 'Cell12', 'Cell13', 'Cell14', 'Cell15', 'Cell16', 'Cell17', 'Cell18', 'Cell19', 'Cell20', 'Cell21', 'Cell22', 'Cell23', 'Cell24', 'Cell25', 'Cell26', 'Cell27', 'Cell28', 'Cell29', 'Cell30', 'Cell31', 'Cell32', 'Cell33', 'Cell34', 'Cell35', 'Cell36', 'Cell37', 'Cell38', 'Cell39', 'Cell40', 'Cell41', 'Cell42', 'Cell43', 'Cell44', 'Cell45', 'Cell46', 'Cell47', 'Cell48', 'Cell49', 'Cell50', 'Cell51', 'Cell52', 'Cell53', 'Cell54', 'Cell55', 'Cell56', 'Cell57', 'Cell58', 'Cell59', 'Cell60', 'Cell61', 'Cell62', 'Cell63', 'Cell64', 'Cell65', 'Cell66', 'Cell67', 'Cell68', 'Cell69', 'Cell70', 'Cell71', 'Cell72', 'Cell73', 'Cell74', 'Cell75', 'Cell76', 'Cell77', 'Cell78', 'Cell79', 'Cell80', 'Cell81', 'Cell82', 'Cell83', 'Cell84', 'Cell85', 'Cell86', 'Cell87', 'Cell88', 'Cell89', 'Cell90', 'Cell91', 'Cell92', 'Cell93', 'Cell94', 'Cell95', 'Cell96', 'Cell97', 'Cell98', 'Cell99']\n",
      "Geni:  \n",
      " ['Gene0', 'Gene1', 'Gene2', 'Gene3', 'Gene4', 'Gene5', 'Gene6', 'Gene7', 'Gene8', 'Gene9', 'Gene10', 'Gene11', 'Gene12', 'Gene13', 'Gene14', 'Gene15', 'Gene16', 'Gene17', 'Gene18', 'Gene19', 'Gene20', 'Gene21', 'Gene22', 'Gene23', 'Gene24', 'Gene25', 'Gene26', 'Gene27', 'Gene28', 'Gene29', 'Gene30', 'Gene31', 'Gene32', 'Gene33', 'Gene34', 'Gene35', 'Gene36', 'Gene37', 'Gene38', 'Gene39', 'Gene40', 'Gene41', 'Gene42', 'Gene43', 'Gene44', 'Gene45', 'Gene46', 'Gene47', 'Gene48', 'Gene49']\n"
     ]
    }
   ],
   "source": [
    "a = []\n",
    "for i in range(0, df.shape[0]):\n",
    "    a.append(\"Cell\"+str(i))\n",
    "\n",
    "print(\"celcie: \\n\", a)\n",
    "\n",
    "b = []\n",
    "for k in range(0, df.shape[1]):\n",
    "    b.append(\"Gene\"+str(k))\n",
    "\n",
    "print(\"Geni:  \\n\", b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d42d358",
   "metadata": {
    "scrolled": true
   },
   "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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell95</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell96</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell97</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell98</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell99</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Cell0     1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Cell1    22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Cell2    12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Cell3    22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Cell4    27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "Cell95   25.0    0.0    7.0    5.0   17.0    8.0   12.0    1.0    0.0    9.0   \n",
       "Cell96   18.0    7.0    1.0   10.0    0.0   15.0   13.0   11.0    7.0   16.0   \n",
       "Cell97   18.0   19.0    4.0    8.0    0.0    7.0    1.0   20.0    0.0    3.0   \n",
       "Cell98    6.0   20.0   16.0    7.0    9.0    4.0    7.0   36.0   24.0    9.0   \n",
       "Cell99    0.0   16.0    2.0    0.0   10.0    0.0    4.0   33.0   13.0    9.0   \n",
       "\n",
       "        ...  Gene40  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  \\\n",
       "Cell0   ...    15.0    13.0     0.0     0.0    17.0    15.0     0.0     5.0   \n",
       "Cell1   ...    19.0    15.0    21.0     0.0    11.0     0.0    15.0    17.0   \n",
       "Cell2   ...     5.0    16.0     0.0     0.0     0.0    21.0     7.0    25.0   \n",
       "Cell3   ...    15.0    22.0    14.0     0.0     6.0    23.0    16.0     0.0   \n",
       "Cell4   ...    12.0    19.0     4.0     0.0    17.0    31.0    15.0     5.0   \n",
       "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "Cell95  ...    20.0    27.0    15.0    26.0    31.0     0.0     2.0     0.0   \n",
       "Cell96  ...    13.0    23.0     3.0     2.0    20.0     5.0    16.0     0.0   \n",
       "Cell97  ...    26.0     0.0     9.0    27.0     2.0     0.0    12.0     0.0   \n",
       "Cell98  ...     3.0    12.0    16.0    13.0     6.0    15.0    18.0    11.0   \n",
       "Cell99  ...     8.0     8.0     6.0     5.0     0.0    22.0     7.0    20.0   \n",
       "\n",
       "        Gene48  Gene49  \n",
       "Cell0     24.0    10.0  \n",
       "Cell1     22.0    19.0  \n",
       "Cell2      0.0    10.0  \n",
       "Cell3      4.0     8.0  \n",
       "Cell4      3.0    31.0  \n",
       "...        ...     ...  \n",
       "Cell95     0.0    20.0  \n",
       "Cell96    22.0    18.0  \n",
       "Cell97     0.0    25.0  \n",
       "Cell98    10.0     6.0  \n",
       "Cell99    15.0     0.0  \n",
       "\n",
       "[100 rows x 50 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4050c5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 100)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arr.shape # prva dimenzija je 50, druga dimenzija je 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79df8114",
   "metadata": {},
   "source": [
    "Q6) Print the sum of Gene15 and Gene24 for Cell2. Is this greater than the sum of Gene15 and Gene24 for Cell3?\n",
    "\n",
    "Pogledaš rešitev naslednje naloge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82ce3e7a",
   "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>Gene15</th>\n",
       "      <th>Gene24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gene15  Gene24\n",
       "Cell2    10.0    12.0\n",
       "Cell3    26.0    37.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3) # izpiše prve tri vrstice tabele celic s podatki o geni\n",
    "df.iloc[ [2,3], [15,24]] # izpište izbrane vrstice (prvi oglati oklepaj) in izbrane stolpce (drugi oglati oklepaj)\n",
    "# Purely integer-location based indexing for selection by position."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e625a2ca",
   "metadata": {},
   "source": [
    "Rešitev:\n",
    "sum of gene15 and gene24 for cell2 = 22\n",
    "sum of gene15 and gene24 for cell3 = 63\n",
    "Yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4e91295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vsota za Cell2:  22.0\n"
     ]
    }
   ],
   "source": [
    "Cell2_Gene15 = df.loc['Cell2']['Gene15']\n",
    "Cell2_Gene24 = df.loc['Cell2']['Gene24']\n",
    "#print(Cell2_Gene15 , Cell2_Gene24)\n",
    "sum_cell2 = Cell2_Gene15 + Cell2_Gene24\n",
    "print(\"Vsota za Cell2: \",sum_cell2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6fe1b871",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vsota za Cell3:  63.0\n"
     ]
    }
   ],
   "source": [
    "Cell3_Gene15 = df.loc['Cell3']['Gene15']\n",
    "Cell3_Gene24 = df.loc['Cell3']['Gene24']\n",
    "sum_cell3 = Cell3_Gene15 + Cell3_Gene24\n",
    "print(\"Vsota za Cell3: \",sum_cell3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dca8c030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vsota genov 15 in 24 je večja pri Cell3. Odgovor je No.\n"
     ]
    }
   ],
   "source": [
    "if sum_cell2 > sum_cell3:\n",
    "    print(\"Vsota genov 15 in 24 je večja pri Cell2. Odgovor je Yes.\")\n",
    "else:\n",
    "    print(\"Vsota genov 15 in 24 je večja pri Cell3. Odgovor je No.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2005a492",
   "metadata": {},
   "source": [
    "VSTAVLJANJE NOVEGA STOLPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe0ff44",
   "metadata": {},
   "source": [
    "Create a new \"Gene50\" column. Assign each cell a random value from a normal distribution with mean 20 and standard deviation 10 for \"Gene50\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "69b81815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nKo si kopirala iz googla je bilo size = None, takrat se je enkrat naredila random vrednost in je šla ta ista vrednost\\nk vsem celicam. Če daš size = df.shape[0], se random vrednost generira na novo v vsaki vrstici.\\n'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import random\n",
    "random_value = np.random.normal(loc=20, scale=10,size=df.shape[0])\n",
    "#df.drop(df.columns[50], axis=1, inplace=True)\n",
    "df.insert(50, \"Gene50\", random_value )\n",
    "\"\"\"\n",
    "Ko si kopirala iz googla je bilo size = None, takrat se je enkrat naredila random vrednost in je šla ta ista vrednost\n",
    "k vsem celicam. Če daš size = df.shape[0], se random vrednost generira na novo v vsaki vrstici.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "085722cf",
   "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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "      <th>Gene50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.358474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.156664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.361359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.907494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24.693809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.582272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.772492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.835299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.702924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.996064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Retina    1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Brain    22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Heart    12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Heart    22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Kidney   27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "Retina   25.0    0.0    7.0    5.0   17.0    8.0   12.0    1.0    0.0    9.0   \n",
       "Brain    18.0    7.0    1.0   10.0    0.0   15.0   13.0   11.0    7.0   16.0   \n",
       "Retina   18.0   19.0    4.0    8.0    0.0    7.0    1.0   20.0    0.0    3.0   \n",
       "Lung      6.0   20.0   16.0    7.0    9.0    4.0    7.0   36.0   24.0    9.0   \n",
       "Heart     0.0   16.0    2.0    0.0   10.0    0.0    4.0   33.0   13.0    9.0   \n",
       "\n",
       "        ...  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  Gene48  \\\n",
       "Retina  ...    13.0     0.0     0.0    17.0    15.0     0.0     5.0    24.0   \n",
       "Brain   ...    15.0    21.0     0.0    11.0     0.0    15.0    17.0    22.0   \n",
       "Heart   ...    16.0     0.0     0.0     0.0    21.0     7.0    25.0     0.0   \n",
       "Heart   ...    22.0    14.0     0.0     6.0    23.0    16.0     0.0     4.0   \n",
       "Kidney  ...    19.0     4.0     0.0    17.0    31.0    15.0     5.0     3.0   \n",
       "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "Retina  ...    27.0    15.0    26.0    31.0     0.0     2.0     0.0     0.0   \n",
       "Brain   ...    23.0     3.0     2.0    20.0     5.0    16.0     0.0    22.0   \n",
       "Retina  ...     0.0     9.0    27.0     2.0     0.0    12.0     0.0     0.0   \n",
       "Lung    ...    12.0    16.0    13.0     6.0    15.0    18.0    11.0    10.0   \n",
       "Heart   ...     8.0     6.0     5.0     0.0    22.0     7.0    20.0    15.0   \n",
       "\n",
       "        Gene49     Gene50  \n",
       "Retina    10.0  24.358474  \n",
       "Brain     19.0  19.156664  \n",
       "Heart     10.0  28.361359  \n",
       "Heart      8.0  27.907494  \n",
       "Kidney    31.0  24.693809  \n",
       "...        ...        ...  \n",
       "Retina    20.0  21.582272  \n",
       "Brain     18.0  18.772492  \n",
       "Retina    25.0  11.835299  \n",
       "Lung       6.0   4.702924  \n",
       "Heart      0.0  27.996064  \n",
       "\n",
       "[100 rows x 51 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33668883",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.shape[0]) # poda število stolpcev - velikost prve dimenzije\n",
    "print(df.shape[1]) # poda število vrstic - velikost druge dimenzije\n",
    "print(df[\"Gene50\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c855300",
   "metadata": {},
   "source": [
    "Rešitev od profesorja:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "49c5aa59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retina    17.974879\n",
      "Brain     11.863647\n",
      "Heart     23.214494\n",
      "Heart     22.811877\n",
      "Kidney    20.177484\n",
      "            ...    \n",
      "Retina    11.776561\n",
      "Brain     16.016889\n",
      "Retina    16.217619\n",
      "Lung      34.387036\n",
      "Heart     32.458683\n",
      "Name: Gene50, Length: 100, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Gene50 = df[['Gene50']]\n",
    "df[\"Gene50\"] = np.random.normal(20, 10, size=df.shape[0])\n",
    "print(df[\"Gene50\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3b3f09",
   "metadata": {},
   "source": [
    "NOV PROBLEM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d23902",
   "metadata": {},
   "source": [
    "Q8) Now we want to know the mean gene expression for each cell type. Compute this by using pandas groupby to aggregate all columns by their cell type, and take the mean for each gene. Hint: this can be done in a single line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ca541ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "število organov:  100\n",
      "         0\n",
      "0   Retina\n",
      "1    Brain\n",
      "2    Heart\n",
      "3    Heart\n",
      "4   Kidney\n",
      "..     ...\n",
      "95  Retina\n",
      "96   Brain\n",
      "97  Retina\n",
      "98    Lung\n",
      "99   Heart\n",
      "\n",
      "[100 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# 50 genov in 100 celic - 100 primerov in 50 atributov\n",
    "# 50 je tudi organov - naredi dodaten stolpec in odstrani stolpec s celicami\n",
    "organi = np.array(['Retina', 'Brain', 'Heart', 'Heart', 'Kidney', 'Brain', 'Brain',\n",
    "       'Heart', 'Heart', 'Kidney', 'Brain', 'Kidney', 'Heart', 'Kidney',\n",
    "       'Heart', 'Brain', 'Brain', 'Retina', 'Brain', 'Heart', 'Retina',\n",
    "       'Retina', 'Kidney', 'Heart', 'Lung', 'Kidney', 'Heart', 'Kidney',\n",
    "       'Lung', 'Kidney', 'Lung', 'Kidney', 'Retina', 'Kidney', 'Heart',\n",
    "       'Retina', 'Brain', 'Lung', 'Heart', 'Heart', 'Heart', 'Retina',\n",
    "       'Lung', 'Lung', 'Retina', 'Brain', 'Kidney', 'Retina', 'Kidney',\n",
    "       'Lung', 'Heart', 'Brain', 'Retina', 'Lung', 'Retina', 'Brain',\n",
    "       'Kidney', 'Heart', 'Kidney', 'Heart', 'Kidney', 'Brain', 'Kidney',\n",
    "       'Kidney', 'Heart', 'Lung', 'Kidney', 'Kidney', 'Retina', 'Lung',\n",
    "       'Retina', 'Heart', 'Brain', 'Lung', 'Retina', 'Retina', 'Brain',\n",
    "       'Lung', 'Lung', 'Brain', 'Brain', 'Brain', 'Brain', 'Kidney',\n",
    "       'Retina', 'Retina', 'Retina', 'Brain', 'Retina', 'Heart', 'Lung',\n",
    "       'Retina', 'Heart', 'Kidney', 'Brain', 'Retina', 'Brain', 'Retina',\n",
    "       'Lung', 'Heart'])\n",
    "#print(organi)\n",
    "print(\"število organov: \", len(organi))\n",
    "stolpec_organov  = pd.DataFrame(data=organi)\n",
    "print(stolpec_organov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "93cb06ba",
   "metadata": {
    "scrolled": true
   },
   "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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Cell0    1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Cell1   22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Cell2   12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Cell3   22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Cell4   27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "\n",
       "       ...  Gene40  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  \\\n",
       "Cell0  ...    15.0    13.0     0.0     0.0    17.0    15.0     0.0     5.0   \n",
       "Cell1  ...    19.0    15.0    21.0     0.0    11.0     0.0    15.0    17.0   \n",
       "Cell2  ...     5.0    16.0     0.0     0.0     0.0    21.0     7.0    25.0   \n",
       "Cell3  ...    15.0    22.0    14.0     0.0     6.0    23.0    16.0     0.0   \n",
       "Cell4  ...    12.0    19.0     4.0     0.0    17.0    31.0    15.0     5.0   \n",
       "\n",
       "       Gene48  Gene49  \n",
       "Cell0    24.0    10.0  \n",
       "Cell1    22.0    19.0  \n",
       "Cell2     0.0    10.0  \n",
       "Cell3     4.0     8.0  \n",
       "Cell4     3.0    31.0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "524d7630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Brain',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Brain',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Heart',),\n",
      "            ('Retina',),\n",
      "            ('Retina',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            (  'Lung',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            (  'Lung',),\n",
      "            ('Kidney',),\n",
      "            (  'Lung',),\n",
      "            ('Kidney',),\n",
      "            ('Retina',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            (  'Lung',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Heart',),\n",
      "            ('Retina',),\n",
      "            (  'Lung',),\n",
      "            (  'Lung',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ('Kidney',),\n",
      "            ('Retina',),\n",
      "            ('Kidney',),\n",
      "            (  'Lung',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Brain',),\n",
      "            ('Retina',),\n",
      "            (  'Lung',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Brain',),\n",
      "            ('Kidney',),\n",
      "            ('Kidney',),\n",
      "            ( 'Heart',),\n",
      "            (  'Lung',),\n",
      "            ('Kidney',),\n",
      "            ('Kidney',),\n",
      "            ('Retina',),\n",
      "            (  'Lung',),\n",
      "            ('Retina',),\n",
      "            ( 'Heart',),\n",
      "            ( 'Brain',),\n",
      "            (  'Lung',),\n",
      "            ('Retina',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            (  'Lung',),\n",
      "            (  'Lung',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Brain',),\n",
      "            ( 'Brain',),\n",
      "            ('Kidney',),\n",
      "            ('Retina',),\n",
      "            ('Retina',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ('Retina',),\n",
      "            ( 'Heart',),\n",
      "            (  'Lung',),\n",
      "            ('Retina',),\n",
      "            ( 'Heart',),\n",
      "            ('Kidney',),\n",
      "            ( 'Brain',),\n",
      "            ('Retina',),\n",
      "            ( 'Brain',),\n",
      "            ('Retina',),\n",
      "            (  'Lung',),\n",
      "            ( 'Heart',)],\n",
      "           )\n",
      "Index(['Gene0', 'Gene1', 'Gene2', 'Gene3', 'Gene4', 'Gene5', 'Gene6', 'Gene7',\n",
      "       'Gene8', 'Gene9', 'Gene10', 'Gene11', 'Gene12', 'Gene13', 'Gene14',\n",
      "       'Gene15', 'Gene16', 'Gene17', 'Gene18', 'Gene19', 'Gene20', 'Gene21',\n",
      "       'Gene22', 'Gene23', 'Gene24', 'Gene25', 'Gene26', 'Gene27', 'Gene28',\n",
      "       'Gene29', 'Gene30', 'Gene31', 'Gene32', 'Gene33', 'Gene34', 'Gene35',\n",
      "       'Gene36', 'Gene37', 'Gene38', 'Gene39', 'Gene40', 'Gene41', 'Gene42',\n",
      "       'Gene43', 'Gene44', 'Gene45', 'Gene46', 'Gene47', 'Gene48', 'Gene49'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "# poimenovanje vrstic in stolpcev od prej:\n",
    "df.index = [\"Cell\"+str(i) for i in range(0, df.shape[0])]\n",
    "df.columns = [\"Gene\"+str(i) for i in range(0, df.shape[1])]\n",
    "print(df.index)\n",
    "print(df.columns)\n",
    "\"\"\"\n",
    "\n",
    "df.index = [organi]\n",
    "df.columns = [\"Gene\"+str(i) for i in range(0, df.shape[1])]\n",
    "print(df.index)\n",
    "print(df.columns)\n",
    "\n",
    "# df.columns = [\"Gene\"+str(i) for i in np.arange(0, df.shape[1])] # rešitev od prof\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd8e5c17",
   "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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Retina    1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Brain    22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Heart    12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Heart    22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Kidney   27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "Retina   25.0    0.0    7.0    5.0   17.0    8.0   12.0    1.0    0.0    9.0   \n",
       "Brain    18.0    7.0    1.0   10.0    0.0   15.0   13.0   11.0    7.0   16.0   \n",
       "Retina   18.0   19.0    4.0    8.0    0.0    7.0    1.0   20.0    0.0    3.0   \n",
       "Lung      6.0   20.0   16.0    7.0    9.0    4.0    7.0   36.0   24.0    9.0   \n",
       "Heart     0.0   16.0    2.0    0.0   10.0    0.0    4.0   33.0   13.0    9.0   \n",
       "\n",
       "        ...  Gene40  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  \\\n",
       "Retina  ...    15.0    13.0     0.0     0.0    17.0    15.0     0.0     5.0   \n",
       "Brain   ...    19.0    15.0    21.0     0.0    11.0     0.0    15.0    17.0   \n",
       "Heart   ...     5.0    16.0     0.0     0.0     0.0    21.0     7.0    25.0   \n",
       "Heart   ...    15.0    22.0    14.0     0.0     6.0    23.0    16.0     0.0   \n",
       "Kidney  ...    12.0    19.0     4.0     0.0    17.0    31.0    15.0     5.0   \n",
       "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "Retina  ...    20.0    27.0    15.0    26.0    31.0     0.0     2.0     0.0   \n",
       "Brain   ...    13.0    23.0     3.0     2.0    20.0     5.0    16.0     0.0   \n",
       "Retina  ...    26.0     0.0     9.0    27.0     2.0     0.0    12.0     0.0   \n",
       "Lung    ...     3.0    12.0    16.0    13.0     6.0    15.0    18.0    11.0   \n",
       "Heart   ...     8.0     8.0     6.0     5.0     0.0    22.0     7.0    20.0   \n",
       "\n",
       "        Gene48  Gene49  \n",
       "Retina    24.0    10.0  \n",
       "Brain     22.0    19.0  \n",
       "Heart      0.0    10.0  \n",
       "Heart      4.0     8.0  \n",
       "Kidney     3.0    31.0  \n",
       "...        ...     ...  \n",
       "Retina     0.0    20.0  \n",
       "Brain     22.0    18.0  \n",
       "Retina     0.0    25.0  \n",
       "Lung      10.0     6.0  \n",
       "Heart     15.0     0.0  \n",
       "\n",
       "[100 rows x 50 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faec37ea",
   "metadata": {},
   "source": [
    "Uporabiš funkcijo groupby:\n",
    "A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4d3012b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.523810</td>\n",
       "      <td>11.761905</td>\n",
       "      <td>9.571429</td>\n",
       "      <td>9.380952</td>\n",
       "      <td>11.238095</td>\n",
       "      <td>5.761905</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>9.857143</td>\n",
       "      <td>11.904762</td>\n",
       "      <td>...</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>13.619048</td>\n",
       "      <td>6.619048</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>8.380952</td>\n",
       "      <td>6.523810</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.857143</td>\n",
       "      <td>13.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>10.619048</td>\n",
       "      <td>11.523810</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.380952</td>\n",
       "      <td>12.809524</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>7.523810</td>\n",
       "      <td>9.904762</td>\n",
       "      <td>...</td>\n",
       "      <td>13.190476</td>\n",
       "      <td>11.952381</td>\n",
       "      <td>6.285714</td>\n",
       "      <td>7.380952</td>\n",
       "      <td>10.047619</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.476190</td>\n",
       "      <td>10.047619</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney</th>\n",
       "      <td>11.857143</td>\n",
       "      <td>16.238095</td>\n",
       "      <td>13.523810</td>\n",
       "      <td>5.238095</td>\n",
       "      <td>10.904762</td>\n",
       "      <td>12.809524</td>\n",
       "      <td>6.190476</td>\n",
       "      <td>15.619048</td>\n",
       "      <td>9.476190</td>\n",
       "      <td>10.952381</td>\n",
       "      <td>...</td>\n",
       "      <td>11.619048</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>7.047619</td>\n",
       "      <td>5.952381</td>\n",
       "      <td>14.190476</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>5.952381</td>\n",
       "      <td>5.380952</td>\n",
       "      <td>10.952381</td>\n",
       "      <td>12.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung</th>\n",
       "      <td>15.066667</td>\n",
       "      <td>15.066667</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>6.733333</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>8.733333</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.133333</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>9.733333</td>\n",
       "      <td>15.066667</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>13.066667</td>\n",
       "      <td>7.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>11.954545</td>\n",
       "      <td>12.545455</td>\n",
       "      <td>13.772727</td>\n",
       "      <td>8.227273</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.409091</td>\n",
       "      <td>7.590909</td>\n",
       "      <td>13.227273</td>\n",
       "      <td>10.590909</td>\n",
       "      <td>11.227273</td>\n",
       "      <td>...</td>\n",
       "      <td>15.181818</td>\n",
       "      <td>11.318182</td>\n",
       "      <td>8.863636</td>\n",
       "      <td>7.863636</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.590909</td>\n",
       "      <td>6.409091</td>\n",
       "      <td>8.136364</td>\n",
       "      <td>11.409091</td>\n",
       "      <td>14.454545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gene0      Gene1      Gene2     Gene3      Gene4      Gene5  \\\n",
       "Brain   13.000000  12.523810  11.761905  9.571429   9.380952  11.238095   \n",
       "Heart   10.619048  11.523810  12.333333  5.000000   9.380952  12.809524   \n",
       "Kidney  11.857143  16.238095  13.523810  5.238095  10.904762  12.809524   \n",
       "Lung    15.066667  15.066667   7.266667  4.333333   6.666667   9.133333   \n",
       "Retina  11.954545  12.545455  13.772727  8.227273  10.000000   8.409091   \n",
       "\n",
       "           Gene6      Gene7      Gene8      Gene9  ...     Gene40     Gene41  \\\n",
       "Brain   5.761905  10.857143   9.857143  11.904762  ...  14.428571  13.619048   \n",
       "Heart   6.000000  14.285714   7.523810   9.904762  ...  13.190476  11.952381   \n",
       "Kidney  6.190476  15.619048   9.476190  10.952381  ...  11.619048   7.285714   \n",
       "Lung    6.733333  16.200000   6.800000   8.733333  ...  12.000000  10.133333   \n",
       "Retina  7.590909  13.227273  10.590909  11.227273  ...  15.181818  11.318182   \n",
       "\n",
       "           Gene42    Gene43     Gene44     Gene45    Gene46     Gene47  \\\n",
       "Brain    6.619048  5.142857  11.714286   8.380952  6.523810   6.666667   \n",
       "Heart    6.285714  7.380952  10.047619  14.000000  7.476190  10.047619   \n",
       "Kidney   7.047619  5.952381  14.190476   8.714286  5.952381   5.380952   \n",
       "Lung    10.800000  9.733333  15.066667   7.266667  6.600000   7.333333   \n",
       "Retina   8.863636  7.863636  13.000000  12.590909  6.409091   8.136364   \n",
       "\n",
       "           Gene48     Gene49  \n",
       "Brain    9.857143  13.047619  \n",
       "Heart   11.000000  12.666667  \n",
       "Kidney  10.952381  12.047619  \n",
       "Lung    13.066667   7.266667  \n",
       "Retina  11.409091  14.454545  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zdruzeno = df.groupby(axis=0, level=0).mean()\n",
    "\"\"\"\n",
    "Dokumentacija: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html\n",
    "axis = 0 pomeni, da razdeliš dataframe glede na prvo dimenzijo, prva dimenzija so vrstice\n",
    "axis = 1 pomeni, da bi dataframe razdelila glede na drugo dimenzijo, druga dimenzija so stolpci\n",
    "\"\"\"\n",
    "zdruzeno.head(zdruzeno.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7cd5a7",
   "metadata": {},
   "source": [
    "We now want to find out which cell types have the maximum and minimum expression of each gene. Use the idxmax and idxmin functions to determine this for each gene.\n",
    "Dokumentacija:\n",
    "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4e91c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Gene0       Lung\n",
       "Gene1     Kidney\n",
       "Gene2     Retina\n",
       "Gene3      Brain\n",
       "Gene4     Kidney\n",
       "Gene5      Heart\n",
       "Gene6     Retina\n",
       "Gene7       Lung\n",
       "Gene8     Retina\n",
       "Gene9      Brain\n",
       "Gene10      Lung\n",
       "Gene11      Lung\n",
       "Gene12     Brain\n",
       "Gene13    Kidney\n",
       "Gene14      Lung\n",
       "Gene15    Kidney\n",
       "Gene16     Heart\n",
       "Gene17     Heart\n",
       "Gene18    Kidney\n",
       "Gene19     Brain\n",
       "Gene20    Kidney\n",
       "Gene21     Heart\n",
       "Gene22     Brain\n",
       "Gene23     Heart\n",
       "Gene24      Lung\n",
       "Gene25     Brain\n",
       "Gene26      Lung\n",
       "Gene27      Lung\n",
       "Gene28     Heart\n",
       "Gene29    Kidney\n",
       "Gene30    Retina\n",
       "Gene31     Heart\n",
       "Gene32    Retina\n",
       "Gene33     Brain\n",
       "Gene34    Retina\n",
       "Gene35    Kidney\n",
       "Gene36    Kidney\n",
       "Gene37    Kidney\n",
       "Gene38      Lung\n",
       "Gene39    Retina\n",
       "Gene40    Retina\n",
       "Gene41     Brain\n",
       "Gene42      Lung\n",
       "Gene43      Lung\n",
       "Gene44      Lung\n",
       "Gene45     Heart\n",
       "Gene46     Heart\n",
       "Gene47     Heart\n",
       "Gene48      Lung\n",
       "Gene49    Retina\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Max:\")\n",
    "zdruzeno.idxmax(axis=0, skipna=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0c4220b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Gene0      Heart\n",
       "Gene1      Heart\n",
       "Gene2       Lung\n",
       "Gene3       Lung\n",
       "Gene4       Lung\n",
       "Gene5     Retina\n",
       "Gene6      Brain\n",
       "Gene7      Brain\n",
       "Gene8       Lung\n",
       "Gene9       Lung\n",
       "Gene10    Kidney\n",
       "Gene11    Kidney\n",
       "Gene12     Heart\n",
       "Gene13     Heart\n",
       "Gene14     Brain\n",
       "Gene15      Lung\n",
       "Gene16     Brain\n",
       "Gene17    Kidney\n",
       "Gene18    Retina\n",
       "Gene19     Heart\n",
       "Gene20    Retina\n",
       "Gene21     Brain\n",
       "Gene22      Lung\n",
       "Gene23      Lung\n",
       "Gene24    Kidney\n",
       "Gene25     Heart\n",
       "Gene26     Brain\n",
       "Gene27    Kidney\n",
       "Gene28    Retina\n",
       "Gene29    Retina\n",
       "Gene30    Kidney\n",
       "Gene31      Lung\n",
       "Gene32    Kidney\n",
       "Gene33    Kidney\n",
       "Gene34     Heart\n",
       "Gene35     Heart\n",
       "Gene36    Retina\n",
       "Gene37     Brain\n",
       "Gene38     Heart\n",
       "Gene39     Heart\n",
       "Gene40    Kidney\n",
       "Gene41    Kidney\n",
       "Gene42     Heart\n",
       "Gene43     Brain\n",
       "Gene44     Heart\n",
       "Gene45      Lung\n",
       "Gene46    Kidney\n",
       "Gene47    Kidney\n",
       "Gene48     Brain\n",
       "Gene49      Lung\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Min\")\n",
    "zdruzeno.idxmin(axis=0, skipna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f343cc",
   "metadata": {},
   "source": [
    "Q10) Often, it is useful to compare the average expression of each gene between two groups of cells, in order to determine which genes are more highly or more lowly expressed in a particular cell type. Using your answer from Q8, in which you computed the mean for each gene by cell type, make a scatter plot using the matplotlib library (i.e. matplotlib.pyplot.scatter) in which each point is a gene, the x-axis corresponds to the average expression in the \"Brain\", and the y-axis corresponds to the average expression in the \"Lung\". Don't forget to label your axes with plt.xlabel and plt.ylabel!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d65cb59",
   "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>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.523810</td>\n",
       "      <td>11.761905</td>\n",
       "      <td>9.571429</td>\n",
       "      <td>9.380952</td>\n",
       "      <td>11.238095</td>\n",
       "      <td>5.761905</td>\n",
       "      <td>10.857143</td>\n",
       "      <td>9.857143</td>\n",
       "      <td>11.904762</td>\n",
       "      <td>...</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>13.619048</td>\n",
       "      <td>6.619048</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>8.380952</td>\n",
       "      <td>6.523810</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.857143</td>\n",
       "      <td>13.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>10.619048</td>\n",
       "      <td>11.523810</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.380952</td>\n",
       "      <td>12.809524</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>7.523810</td>\n",
       "      <td>9.904762</td>\n",
       "      <td>...</td>\n",
       "      <td>13.190476</td>\n",
       "      <td>11.952381</td>\n",
       "      <td>6.285714</td>\n",
       "      <td>7.380952</td>\n",
       "      <td>10.047619</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.476190</td>\n",
       "      <td>10.047619</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney</th>\n",
       "      <td>11.857143</td>\n",
       "      <td>16.238095</td>\n",
       "      <td>13.523810</td>\n",
       "      <td>5.238095</td>\n",
       "      <td>10.904762</td>\n",
       "      <td>12.809524</td>\n",
       "      <td>6.190476</td>\n",
       "      <td>15.619048</td>\n",
       "      <td>9.476190</td>\n",
       "      <td>10.952381</td>\n",
       "      <td>...</td>\n",
       "      <td>11.619048</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>7.047619</td>\n",
       "      <td>5.952381</td>\n",
       "      <td>14.190476</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>5.952381</td>\n",
       "      <td>5.380952</td>\n",
       "      <td>10.952381</td>\n",
       "      <td>12.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung</th>\n",
       "      <td>15.066667</td>\n",
       "      <td>15.066667</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>6.733333</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>8.733333</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.133333</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>9.733333</td>\n",
       "      <td>15.066667</td>\n",
       "      <td>7.266667</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>13.066667</td>\n",
       "      <td>7.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>11.954545</td>\n",
       "      <td>12.545455</td>\n",
       "      <td>13.772727</td>\n",
       "      <td>8.227273</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.409091</td>\n",
       "      <td>7.590909</td>\n",
       "      <td>13.227273</td>\n",
       "      <td>10.590909</td>\n",
       "      <td>11.227273</td>\n",
       "      <td>...</td>\n",
       "      <td>15.181818</td>\n",
       "      <td>11.318182</td>\n",
       "      <td>8.863636</td>\n",
       "      <td>7.863636</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.590909</td>\n",
       "      <td>6.409091</td>\n",
       "      <td>8.136364</td>\n",
       "      <td>11.409091</td>\n",
       "      <td>14.454545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gene0      Gene1      Gene2     Gene3      Gene4      Gene5  \\\n",
       "Brain   13.000000  12.523810  11.761905  9.571429   9.380952  11.238095   \n",
       "Heart   10.619048  11.523810  12.333333  5.000000   9.380952  12.809524   \n",
       "Kidney  11.857143  16.238095  13.523810  5.238095  10.904762  12.809524   \n",
       "Lung    15.066667  15.066667   7.266667  4.333333   6.666667   9.133333   \n",
       "Retina  11.954545  12.545455  13.772727  8.227273  10.000000   8.409091   \n",
       "\n",
       "           Gene6      Gene7      Gene8      Gene9  ...     Gene40     Gene41  \\\n",
       "Brain   5.761905  10.857143   9.857143  11.904762  ...  14.428571  13.619048   \n",
       "Heart   6.000000  14.285714   7.523810   9.904762  ...  13.190476  11.952381   \n",
       "Kidney  6.190476  15.619048   9.476190  10.952381  ...  11.619048   7.285714   \n",
       "Lung    6.733333  16.200000   6.800000   8.733333  ...  12.000000  10.133333   \n",
       "Retina  7.590909  13.227273  10.590909  11.227273  ...  15.181818  11.318182   \n",
       "\n",
       "           Gene42    Gene43     Gene44     Gene45    Gene46     Gene47  \\\n",
       "Brain    6.619048  5.142857  11.714286   8.380952  6.523810   6.666667   \n",
       "Heart    6.285714  7.380952  10.047619  14.000000  7.476190  10.047619   \n",
       "Kidney   7.047619  5.952381  14.190476   8.714286  5.952381   5.380952   \n",
       "Lung    10.800000  9.733333  15.066667   7.266667  6.600000   7.333333   \n",
       "Retina   8.863636  7.863636  13.000000  12.590909  6.409091   8.136364   \n",
       "\n",
       "           Gene48     Gene49  \n",
       "Brain    9.857143  13.047619  \n",
       "Heart   11.000000  12.666667  \n",
       "Kidney  10.952381  12.047619  \n",
       "Lung    13.066667   7.266667  \n",
       "Retina  11.409091  14.454545  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zdruzeno.head(zdruzeno.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bc57b8",
   "metadata": {},
   "source": [
    "Naredi razsevni diagram\n",
    "točke so geni\n",
    "x os je ekspresija v možganih\n",
    "y os je ekspresija v pljučih\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e987329f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene0     13.000000\n",
      "Gene1     12.523810\n",
      "Gene2     11.761905\n",
      "Gene3      9.571429\n",
      "Gene4      9.380952\n",
      "Gene5     11.238095\n",
      "Gene6      5.761905\n",
      "Gene7     10.857143\n",
      "Gene8      9.857143\n",
      "Gene9     11.904762\n",
      "Gene10    12.000000\n",
      "Gene11    10.761905\n",
      "Gene12    16.333333\n",
      "Gene13     7.714286\n",
      "Gene14     4.380952\n",
      "Gene15    14.190476\n",
      "Gene16     5.666667\n",
      "Gene17    14.333333\n",
      "Gene18    14.142857\n",
      "Gene19    17.666667\n",
      "Gene20     9.047619\n",
      "Gene21    10.904762\n",
      "Gene22    12.952381\n",
      "Gene23    18.904762\n",
      "Gene24    15.238095\n",
      "Gene25    14.904762\n",
      "Gene26    11.380952\n",
      "Gene27     5.809524\n",
      "Gene28    12.809524\n",
      "Gene29     6.190476\n",
      "Gene30     9.619048\n",
      "Gene31     9.666667\n",
      "Gene32    10.476190\n",
      "Gene33    12.380952\n",
      "Gene34     7.571429\n",
      "Gene35     5.428571\n",
      "Gene36    12.428571\n",
      "Gene37     6.714286\n",
      "Gene38    13.571429\n",
      "Gene39    11.333333\n",
      "Gene40    14.428571\n",
      "Gene41    13.619048\n",
      "Gene42     6.619048\n",
      "Gene43     5.142857\n",
      "Gene44    11.714286\n",
      "Gene45     8.380952\n",
      "Gene46     6.523810\n",
      "Gene47     6.666667\n",
      "Gene48     9.857143\n",
      "Gene49    13.047619\n",
      "Name: Brain, dtype: float64\n",
      "Gene0     15.066667\n",
      "Gene1     15.066667\n",
      "Gene2      7.266667\n",
      "Gene3      4.333333\n",
      "Gene4      6.666667\n",
      "Gene5      9.133333\n",
      "Gene6      6.733333\n",
      "Gene7     16.200000\n",
      "Gene8      6.800000\n",
      "Gene9      8.733333\n",
      "Gene10    14.400000\n",
      "Gene11    13.400000\n",
      "Gene12    13.333333\n",
      "Gene13     8.066667\n",
      "Gene14    12.066667\n",
      "Gene15     9.200000\n",
      "Gene16     7.666667\n",
      "Gene17    14.400000\n",
      "Gene18    12.333333\n",
      "Gene19    15.666667\n",
      "Gene20     8.666667\n",
      "Gene21    15.733333\n",
      "Gene22     6.800000\n",
      "Gene23    14.600000\n",
      "Gene24    18.933333\n",
      "Gene25    13.600000\n",
      "Gene26    16.933333\n",
      "Gene27    12.533333\n",
      "Gene28    10.933333\n",
      "Gene29     7.000000\n",
      "Gene30     8.333333\n",
      "Gene31     6.666667\n",
      "Gene32    13.266667\n",
      "Gene33     9.200000\n",
      "Gene34     8.333333\n",
      "Gene35     7.066667\n",
      "Gene36    11.666667\n",
      "Gene37     9.066667\n",
      "Gene38    16.266667\n",
      "Gene39    11.333333\n",
      "Gene40    12.000000\n",
      "Gene41    10.133333\n",
      "Gene42    10.800000\n",
      "Gene43     9.733333\n",
      "Gene44    15.066667\n",
      "Gene45     7.266667\n",
      "Gene46     6.600000\n",
      "Gene47     7.333333\n",
      "Gene48    13.066667\n",
      "Gene49     7.266667\n",
      "Name: Lung, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(zdruzeno.iloc[0,:])\n",
    "print(zdruzeno.iloc[3,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "06d54d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = zdruzeno.iloc[0,:]\n",
    "y = zdruzeno.iloc[3,:]\n",
    "#colors = np.random.rand(N)\n",
    "#area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y,alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7243f073",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}