{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "unique-intellectual",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "shared-fitness",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"1D struktura podataka\"\"\"\n",
    "\n",
    "s1 = pd.Series([1,2,3,5]) #pravljenje serije pomocu liste i automatsko dodeljivanje oznaka redovima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "mighty-fifteen",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "signed-pencil",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s1[2] 3\n",
      "s1[1] 2\n"
     ]
    }
   ],
   "source": [
    "#pristup elementima  serija[oznaka]\n",
    "print('s1[2]', s1[2])\n",
    "print('s1[1]', s1.get(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "genetic-hurricane",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s1[8] nan\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  # modul zbog nan vrednosti\n",
    "print('s1[8]', s1.get(8, np.nan))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "handmade-montgomery",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pravljenje serije pomocu liste sa zadatim oznakama za redove\n",
    "s2 = pd.Series([1,2,3,5], index=['a', 'b', 'c', 'd'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "exterior-plain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2['d'] 5\n"
     ]
    }
   ],
   "source": [
    "print(\"s2['d']\" ,s2['d'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ahead-application",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e    2\n",
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    5\n",
      "dtype: int64\n",
      "s3['a'] 1\n"
     ]
    }
   ],
   "source": [
    "s3 = pd.Series({'e':2, 'a':1,'b':2,'c':3,'d':5, }) # pravljenje serije pomocu recnika\n",
    "print(s3)\n",
    "print(\"s3['a']\", s3['a'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unlikely-blond",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['e', 'a', 'b', 'c', 'd']\n",
      "Index(['e', 'a', 'b', 'c', 'd'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#izdvajanje oznaka\n",
    "print(s3.index.tolist())\n",
    "#ili\n",
    "print(s3.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "structured-multimedia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 2 3 5]\n"
     ]
    }
   ],
   "source": [
    "#izdvajanje vrednosti\n",
    "print(s3.values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "advisory-latitude",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "#tip elemenata\n",
    "print(s3.dtype)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "excellent-logic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    2\n",
      "1    1\n",
      "3    1\n",
      "5    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#broj pojavljivanja svake vrednosti\n",
    "print(s3.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "average-thailand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "younger-algeria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    2\n",
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fallen-painting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     2.0\n",
       "b     4.0\n",
       "c     6.0\n",
       "d    10.0\n",
       "e     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2+s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "contained-regular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     2\n",
       "b     4\n",
       "c     6\n",
       "d    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "framed-assistant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df1\n",
      "   prva druga\n",
      "a     1     x\n",
      "b     2     y\n",
      "c     3     z\n"
     ]
    }
   ],
   "source": [
    "\"\"\"2D struktura podataka DataFrame\"\"\"\n",
    "\n",
    "d1= {\n",
    "    'prva': pd.Series([1,2,3], index=['a', 'b', 'c']),\n",
    "    'druga': pd.Series(['x', 'y', 'z'], index=['a', 'b', 'c'])\n",
    "}\n",
    "df1 = pd.DataFrame(d1)\n",
    "\n",
    "print(\"df1\")\n",
    "print(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "solid-guinea",
   "metadata": {},
   "outputs": [],
   "source": [
    "d2= {\n",
    "    'prva': pd.Series([3,2,1], index=['a', 'b', 'c']),\n",
    "    'druga': pd.Series(['x', 'z', 'y'], index=['a', 'b', 'd']),\n",
    "    'treca': pd.Series(['m', 'n', 'p'], index=['a', 'b', 'e'])\n",
    "   \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "radio-chocolate",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(d2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "objective-rating",
   "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>prva</th>\n",
       "      <th>druga</th>\n",
       "      <th>treca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.0</td>\n",
       "      <td>x</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>z</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prva druga treca\n",
       "a   3.0     x     m\n",
       "b   2.0     z     n\n",
       "c   1.0   NaN   NaN\n",
       "d   NaN     y   NaN\n",
       "e   NaN   NaN     p"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "printable-breast",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3 = pd.DataFrame( [(1,2,'X'), (2,3,'C')], columns=['A', 'B','C'])\n",
    "df3 = pd.DataFrame( [(1,2,'X'), (2,3,'C')])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "robust-politics",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2\n",
       "0  1  2  X\n",
       "1  2  3  C"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "careful-resort",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sortiranje po indeksima (oznakama)\n",
    "#df2.sort_index(ascending=False, inplace=True)  # sortiranje redova\n",
    "df2.sort_index(ascending=True, inplace=True)  # sortiranje redova\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "enormous-protest",
   "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>prva</th>\n",
       "      <th>druga</th>\n",
       "      <th>treca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.0</td>\n",
       "      <td>x</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>z</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prva druga treca\n",
       "a   3.0     x     m\n",
       "b   2.0     z     n\n",
       "c   1.0   NaN   NaN\n",
       "d   NaN     y   NaN\n",
       "e   NaN   NaN     p"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "artificial-roots",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.sort_index(ascending=True, axis=1, inplace=True) #sortiranje kolona\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "patient-wayne",
   "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>druga</th>\n",
       "      <th>prva</th>\n",
       "      <th>treca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>x</td>\n",
       "      <td>3.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  druga  prva treca\n",
       "a     x   3.0     m\n",
       "b     z   2.0     n\n",
       "c   NaN   1.0   NaN\n",
       "d     y   NaN   NaN\n",
       "e   NaN   NaN     p"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "expressed-array",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sortiranje po vrednosti kolone\n",
    "df2.sort_values(by='prva', inplace=True)   #by=[lista kolona]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "appointed-summer",
   "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>druga</th>\n",
       "      <th>prva</th>\n",
       "      <th>treca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>x</td>\n",
       "      <td>3.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  druga  prva treca\n",
       "c   NaN   1.0   NaN\n",
       "b     z   2.0     n\n",
       "a     x   3.0     m\n",
       "d     y   NaN   NaN\n",
       "e   NaN   NaN     p"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "narrow-barbados",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame({\n",
    "                    'number' : [1,2,2,3],\n",
    "                    'object' : ['c', 'd', 'e', 'c']\n",
    "                    })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "solved-dover",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number     int64\n",
      "object    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df4.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "specialized-reviewer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opis svih kolona\n",
      "         number\n",
      "count  4.000000\n",
      "mean   2.000000\n",
      "std    0.816497\n",
      "min    1.000000\n",
      "25%    1.750000\n",
      "50%    2.000000\n",
      "75%    2.250000\n",
      "max    3.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"Opis svih kolona\")\n",
    "#print(df4.describe(include='all'))\n",
    "print(df4.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "legendary-throw",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opis numerickih kolona\n",
      "         number\n",
      "count  4.000000\n",
      "mean   2.000000\n",
      "std    0.816497\n",
      "min    1.000000\n",
      "25%    1.750000\n",
      "50%    2.000000\n",
      "75%    2.250000\n",
      "max    3.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"Opis numerickih kolona\")\n",
    "print(df4.describe(include=['number']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fabulous-label",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opis kolona object\n",
      "       object\n",
      "count       4\n",
      "unique      3\n",
      "top         c\n",
      "freq        2\n"
     ]
    }
   ],
   "source": [
    "print(\"Opis kolona object\")\n",
    "print(df4.describe(include=['object']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "nonprofit-fabric",
   "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>druga</th>\n",
       "      <th>prva</th>\n",
       "      <th>treca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>x</td>\n",
       "      <td>3.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  druga  prva treca\n",
       "c   NaN   1.0   NaN\n",
       "b     z   2.0     n\n",
       "a     x   3.0     m\n",
       "d     y   NaN   NaN\n",
       "e   NaN   NaN     p"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "sexual-museum",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Srednje vrednosti\n",
      "prva    2.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#neke deskriptivne statistike\n",
    "print(\"Srednje vrednosti\")\n",
    "print(df2.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "preceding-friendly",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Srednje vrednosti uzimajuci u obzir NaN\n",
      "prva   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Srednje vrednosti uzimajuci u obzir NaN\")\n",
    "print(df2.mean(skipna=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "monthly-munich",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a   b\n",
      "0  1   2\n",
      "1  2   8\n",
      "2  3  10\n",
      "Srednja vrednost za svaki red\n",
      "0    1.5\n",
      "1    5.0\n",
      "2    6.5\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df5=pd.DataFrame({'a':[1,2,3], 'b':[2,8,10]})\n",
    "print(df5)\n",
    "print(\"Srednja vrednost za svaki red\")\n",
    "print(df5.mean(axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fourth-corruption",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zbir po kolonama\n",
      "a     6\n",
      "b    20\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Zbir po kolonama\")\n",
    "print(df5.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "vocal-activity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zbir po redovima\n",
      "0     3\n",
      "1    10\n",
      "2    13\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Zbir po redovima\")\n",
    "print(df5.sum(axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "minus-visibility",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentili\n",
      "a    2.0\n",
      "b    8.0\n",
      "Name: 0.5, dtype: float64\n",
      "a    1.5\n",
      "b    5.0\n",
      "Name: 0.25, dtype: float64\n",
      "        a    b\n",
      "0.25  1.5  5.0\n",
      "0.50  2.0  8.0\n",
      "0.75  2.5  9.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentili\")\n",
    "print(df5.quantile())\n",
    "print(df5.quantile(0.25))\n",
    "print(df5.quantile([0.25, 0.5,0.75]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "humanitarian-router",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najzastupljenije vrednosti\n",
      "  druga  prva treca\n",
      "0     x   1.0     m\n",
      "1     y   2.0     n\n",
      "2     z   3.0     p\n"
     ]
    }
   ],
   "source": [
    "print(\"Najzastupljenije vrednosti\")\n",
    "print(df2.mode())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "available-agreement",
   "metadata": {},
   "outputs": [],
   "source": [
    "d2= {\n",
    "    'prva': pd.Series([3,2,1], index=['a', 'b', 'c']),\n",
    "    'druga': pd.Series(['x', 'x', 'y'], index=['a', 'b', 'd']),\n",
    "    'treca': pd.Series(['m', 'n', 'p'], index=['a', 'b', 'e'])\n",
    "   \n",
    "}\n",
    "df2=pd.DataFrame(d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "measured-scotland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najzastupljenije vrednosti\n",
      "   prva druga treca\n",
      "0   1.0     x     m\n",
      "1   2.0   NaN     n\n",
      "2   3.0   NaN     p\n"
     ]
    }
   ],
   "source": [
    "print(\"Najzastupljenije vrednosti\")\n",
    "print(df2.mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "steady-insertion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Razlicite vrednosti\n",
      "prva     3\n",
      "druga    2\n",
      "treca    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Razlicite vrednosti\")\n",
    "print(df2.nunique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "brief-devon",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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