reading_files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76672f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b13a8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>ASIA (EX. NEAR EAST)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>EASTERN EUROPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>OCEANIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>WESTERN EUROPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>222</th>\n",
       "      <td>West Bank</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country                               Region\n",
       "0       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "1           Albania   EASTERN EUROPE                     \n",
       "2           Algeria   NORTHERN AFRICA                    \n",
       "3    American Samoa   OCEANIA                            \n",
       "4           Andorra   WESTERN EUROPE                     \n",
       "..               ...                                  ...\n",
       "222       West Bank   NEAR EAST                          \n",
       "223  Western Sahara   NORTHERN AFRICA                    \n",
       "224           Yemen   NEAR EAST                          \n",
       "225          Zambia   SUB-SAHARAN AFRICA                 \n",
       "226        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[227 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eac5ad0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_table(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fe8b8ba5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>American Samoa</td>\n",
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       "      <td>NEAR EAST</td>\n",
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       "      <th>223</th>\n",
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       "      <td>NORTHERN AFRICA</td>\n",
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       "      <td>SUB-SAHARAN AFRICA</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
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       "<p>227 rows × 2 columns</p>\n",
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      "text/plain": [
       "             Country                               Region\n",
       "0       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "1           Albania   EASTERN EUROPE                     \n",
       "2           Algeria   NORTHERN AFRICA                    \n",
       "3    American Samoa   OCEANIA                            \n",
       "4           Andorra   WESTERN EUROPE                     \n",
       "..               ...                                  ...\n",
       "222       West Bank   NEAR EAST                          \n",
       "223  Western Sahara   NORTHERN AFRICA                    \n",
       "224           Yemen   NEAR EAST                          \n",
       "225          Zambia   SUB-SAHARAN AFRICA                 \n",
       "226        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[227 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e4af0a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e8e0b557",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NEAR EAST</td>\n",
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       "      <td>Western Sahara</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
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       "      <td>Yemen</td>\n",
       "      <td>NEAR EAST</td>\n",
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      "text/plain": [
       "                   0                                    1\n",
       "0            Country                               Region\n",
       "1       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "2           Albania   EASTERN EUROPE                     \n",
       "3           Algeria   NORTHERN AFRICA                    \n",
       "4    American Samoa   OCEANIA                            \n",
       "..               ...                                  ...\n",
       "223       West Bank   NEAR EAST                          \n",
       "224  Western Sahara   NORTHERN AFRICA                    \n",
       "225           Yemen   NEAR EAST                          \n",
       "226          Zambia   SUB-SAHARAN AFRICA                 \n",
       "227        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[228 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e8203f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.csv\", names=['Land', 'Bezirk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b1f7933",
   "metadata": {},
   "outputs": [
    {
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       "      <th>224</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>228 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "                Land                               Bezirk\n",
       "0            Country                               Region\n",
       "1       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "2           Albania   EASTERN EUROPE                     \n",
       "3           Algeria   NORTHERN AFRICA                    \n",
       "4    American Samoa   OCEANIA                            \n",
       "..               ...                                  ...\n",
       "223       West Bank   NEAR EAST                          \n",
       "224  Western Sahara   NORTHERN AFRICA                    \n",
       "225           Yemen   NEAR EAST                          \n",
       "226          Zambia   SUB-SAHARAN AFRICA                 \n",
       "227        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[228 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7dd865a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_table(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54667726",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country                               Region\n",
       "0       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "1           Albania   EASTERN EUROPE                     \n",
       "2           Algeria   NORTHERN AFRICA                    \n",
       "3    American Samoa   OCEANIA                            \n",
       "4           Andorra   WESTERN EUROPE                     \n",
       "..               ...                                  ...\n",
       "222       West Bank   NEAR EAST                          \n",
       "223  Western Sahara   NORTHERN AFRICA                    \n",
       "224           Yemen   NEAR EAST                          \n",
       "225          Zambia   SUB-SAHARAN AFRICA                 \n",
       "226        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[227 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "890ad665",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/countries.txt\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d5ed877f",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>ASIA (EX. NEAR EAST)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>EASTERN EUROPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>OCEANIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>WESTERN EUROPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>West Bank</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>NEAR EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>SUB-SAHARAN AFRICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country                               Region\n",
       "0       Afghanistan         ASIA (EX. NEAR EAST)         \n",
       "1           Albania   EASTERN EUROPE                     \n",
       "2           Algeria   NORTHERN AFRICA                    \n",
       "3    American Samoa   OCEANIA                            \n",
       "4           Andorra   WESTERN EUROPE                     \n",
       "..               ...                                  ...\n",
       "222       West Bank   NEAR EAST                          \n",
       "223  Western Sahara   NORTHERN AFRICA                    \n",
       "224           Yemen   NEAR EAST                          \n",
       "225          Zambia   SUB-SAHARAN AFRICA                 \n",
       "226        Zimbabwe   SUB-SAHARAN AFRICA                 \n",
       "\n",
       "[227 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ad74620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Afghanistan \n",
       "1             Albania \n",
       "2             Algeria \n",
       "3      American Samoa \n",
       "4             Andorra \n",
       "            ...       \n",
       "222         West Bank \n",
       "223    Western Sahara \n",
       "224             Yemen \n",
       "225            Zambia \n",
       "226          Zimbabwe \n",
       "Name: Country, Length: 227, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "23d98a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_json(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/songs.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa8679f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Californication</th>\n",
       "      <th>What's wrong with me</th>\n",
       "      <th>I want you</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>{'Released': 1999, 'Artist': 'Red Hot Chili Pe...</td>\n",
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       "      <td>{'Released': 1966, 'Artist': 'Bob Dylan', 'Gen...</td>\n",
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      "text/plain": [
       "                                     Californication  \\\n",
       "0  {'Released': 1999, 'Artist': 'Red Hot Chili Pe...   \n",
       "\n",
       "                                What's wrong with me  \\\n",
       "0  {'Released': 2012, 'Artist': 'Hurray for the R...   \n",
       "\n",
       "                                          I want you  \n",
       "0  {'Released': 1966, 'Artist': 'Bob Dylan', 'Gen...  "
      ]
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     "execution_count": 42,
     "metadata": {},
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5a5587c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/population.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "16e1c41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
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       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41128771</td>\n",
       "      <td>652230</td>\n",
       "      <td>63.0587</td>\n",
       "      <td>1.0257</td>\n",
       "      <td>0.52</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Europe</td>\n",
       "      <td>2842321</td>\n",
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       "      <td>98.8702</td>\n",
       "      <td>0.9957</td>\n",
       "      <td>0.04</td>\n",
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       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>DZA</td>\n",
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       "      <td>Algiers</td>\n",
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       "      <td>44903225</td>\n",
       "      <td>2381741</td>\n",
       "      <td>18.8531</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>0.56</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>44273</td>\n",
       "      <td>199</td>\n",
       "      <td>222.4774</td>\n",
       "      <td>0.9831</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>AND</td>\n",
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       "      <td>Andorra la Vella</td>\n",
       "      <td>Europe</td>\n",
       "      <td>79824</td>\n",
       "      <td>468</td>\n",
       "      <td>170.5641</td>\n",
       "      <td>1.0100</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>226</td>\n",
       "      <td>WLF</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>Mata-Utu</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>11572</td>\n",
       "      <td>142</td>\n",
       "      <td>81.4930</td>\n",
       "      <td>0.9953</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>172</td>\n",
       "      <td>ESH</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>El Aaiún</td>\n",
       "      <td>Africa</td>\n",
       "      <td>575986</td>\n",
       "      <td>266000</td>\n",
       "      <td>2.1654</td>\n",
       "      <td>1.0184</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>46</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Sanaa</td>\n",
       "      <td>Asia</td>\n",
       "      <td>33696614</td>\n",
       "      <td>527968</td>\n",
       "      <td>63.8232</td>\n",
       "      <td>1.0217</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>63</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Lusaka</td>\n",
       "      <td>Africa</td>\n",
       "      <td>20017675</td>\n",
       "      <td>752612</td>\n",
       "      <td>26.5976</td>\n",
       "      <td>1.0280</td>\n",
       "      <td>0.25</td>\n",
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       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>74</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare</td>\n",
       "      <td>Africa</td>\n",
       "      <td>16320537</td>\n",
       "      <td>390757</td>\n",
       "      <td>41.7665</td>\n",
       "      <td>1.0204</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3            Country           Capital Continent  \\\n",
       "0      36  AFG        Afghanistan             Kabul      Asia   \n",
       "1     138  ALB            Albania            Tirana    Europe   \n",
       "2      34  DZA            Algeria           Algiers    Africa   \n",
       "3     213  ASM     American Samoa         Pago Pago   Oceania   \n",
       "4     203  AND            Andorra  Andorra la Vella    Europe   \n",
       "..    ...  ...                ...               ...       ...   \n",
       "229   226  WLF  Wallis and Futuna          Mata-Utu   Oceania   \n",
       "230   172  ESH     Western Sahara          El Aaiún    Africa   \n",
       "231    46  YEM              Yemen             Sanaa      Asia   \n",
       "232    63  ZMB             Zambia            Lusaka    Africa   \n",
       "233    74  ZWE           Zimbabwe            Harare    Africa   \n",
       "\n",
       "     2022 Population  Area (km²)  Density (per km²)  Growth Rate  \\\n",
       "0           41128771      652230            63.0587       1.0257   \n",
       "1            2842321       28748            98.8702       0.9957   \n",
       "2           44903225     2381741            18.8531       1.0164   \n",
       "3              44273         199           222.4774       0.9831   \n",
       "4              79824         468           170.5641       1.0100   \n",
       "..               ...         ...                ...          ...   \n",
       "229            11572         142            81.4930       0.9953   \n",
       "230           575986      266000             2.1654       1.0184   \n",
       "231         33696614      527968            63.8232       1.0217   \n",
       "232         20017675      752612            26.5976       1.0280   \n",
       "233         16320537      390757            41.7665       1.0204   \n",
       "\n",
       "     World Population Percentage  \n",
       "0                           0.52  \n",
       "1                           0.04  \n",
       "2                           0.56  \n",
       "3                           0.00  \n",
       "4                           0.00  \n",
       "..                           ...  \n",
       "229                         0.00  \n",
       "230                         0.01  \n",
       "231                         0.42  \n",
       "232                         0.25  \n",
       "233                         0.20  \n",
       "\n",
       "[234 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "27f95479",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel(\"https://github.com/berislav-vidakovic/PythonBlog/raw/main/population.xlsx\", sheet_name=\"Sheet2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5f2f8200",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>AFG</td>\n",
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       "      <td>Kabul</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Tirana</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>DZA</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
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       "      <td>Pago Pago</td>\n",
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       "      <th>4</th>\n",
       "      <td>203</td>\n",
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       "      <th>...</th>\n",
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       "      <th>229</th>\n",
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       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>172</td>\n",
       "      <td>ESH</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>El Aaiún</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>46</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Sanaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>63</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>74</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3            Country           Capital\n",
       "0      36  AFG        Afghanistan             Kabul\n",
       "1     138  ALB            Albania            Tirana\n",
       "2      34  DZA            Algeria           Algiers\n",
       "3     213  ASM     American Samoa         Pago Pago\n",
       "4     203  AND            Andorra  Andorra la Vella\n",
       "..    ...  ...                ...               ...\n",
       "229   226  WLF  Wallis and Futuna          Mata-Utu\n",
       "230   172  ESH     Western Sahara          El Aaiún\n",
       "231    46  YEM              Yemen             Sanaa\n",
       "232    63  ZMB             Zambia            Lusaka\n",
       "233    74  ZWE           Zimbabwe            Harare\n",
       "\n",
       "[234 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62ccb459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 234 entries, 0 to 233\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Rank     234 non-null    int64 \n",
      " 1   CCA3     234 non-null    object\n",
      " 2   Country  234 non-null    object\n",
      " 3   Capital  234 non-null    object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 7.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acbdb969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(234, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f0eea9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Kabul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Tirana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algiers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Pago Pago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank CCA3         Country           Capital\n",
       "0    36  AFG     Afghanistan             Kabul\n",
       "1   138  ALB         Albania            Tirana\n",
       "2    34  DZA         Algeria           Algiers\n",
       "3   213  ASM  American Samoa         Pago Pago\n",
       "4   203  AND         Andorra  Andorra la Vella"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be5b5114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country</th>\n",
       "      <th>Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>226</td>\n",
       "      <td>WLF</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>Mata-Utu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>172</td>\n",
       "      <td>ESH</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>El Aaiún</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>46</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>Sanaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>63</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>Lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>74</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3            Country   Capital\n",
       "229   226  WLF  Wallis and Futuna  Mata-Utu\n",
       "230   172  ESH     Western Sahara  El Aaiún\n",
       "231    46  YEM              Yemen     Sanaa\n",
       "232    63  ZMB             Zambia    Lusaka\n",
       "233    74  ZWE           Zimbabwe    Harare"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b184ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max.rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "039943be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max.rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "334513e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max.columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "03d2ce7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max.columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe2f4625",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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