12 Working with Files/12.9 Pandas 101 - Exporting Data to Files.ipynb

{
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   "cell_type": "markdown",
   "id": "6f1080cd",
   "metadata": {},
   "source": [
    "# Pandas 101 - Exporting Data to Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e011a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b07022e",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>CCA3</th>\n",
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       "      <th>Capital</th>\n",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Kabul</td>\n",
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       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
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       "      <td>1.0164</td>\n",
       "      <td>0.56</td>\n",
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       "      <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>44273.0</td>\n",
       "      <td>46189.0</td>\n",
       "      <td>51368.0</td>\n",
       "      <td>54849.0</td>\n",
       "      <td>58230.0</td>\n",
       "      <td>47818.0</td>\n",
       "      <td>32886.0</td>\n",
       "      <td>27075.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>222.4774</td>\n",
       "      <td>0.9831</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>4</th>\n",
       "      <td>203</td>\n",
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       "      <td>79824.0</td>\n",
       "      <td>77700.0</td>\n",
       "      <td>71746.0</td>\n",
       "      <td>71519.0</td>\n",
       "      <td>66097.0</td>\n",
       "      <td>53569.0</td>\n",
       "      <td>35611.0</td>\n",
       "      <td>19860.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>170.5641</td>\n",
       "      <td>1.0100</td>\n",
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       "      <td>226</td>\n",
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       "      <td>Wallis and Futuna</td>\n",
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       "      <td>11572.0</td>\n",
       "      <td>11655.0</td>\n",
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       "      <td>14723.0</td>\n",
       "      <td>13454.0</td>\n",
       "      <td>11315.0</td>\n",
       "      <td>9377.0</td>\n",
       "      <td>142.0</td>\n",
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       "      <td>575986.0</td>\n",
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       "      <td>413296.0</td>\n",
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       "      <td>178529.0</td>\n",
       "      <td>116775.0</td>\n",
       "      <td>76371.0</td>\n",
       "      <td>266000.0</td>\n",
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       "      <td>63</td>\n",
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       "      <td>Lusaka</td>\n",
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       "      <td>20017675.0</td>\n",
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       "      <th>233</th>\n",
       "      <td>74</td>\n",
       "      <td>ZWE</td>\n",
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       "      <td>Harare</td>\n",
       "      <td>Africa</td>\n",
       "      <td>16320537.0</td>\n",
       "      <td>15669666.0</td>\n",
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       "      <td>41.7665</td>\n",
       "      <td>1.0204</td>\n",
       "      <td>0.20</td>\n",
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      "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  2020 Population  2015 Population  2010 Population  \\\n",
       "0         41128771.0       38972230.0       33753499.0       28189672.0   \n",
       "1          2842321.0        2866849.0        2882481.0        2913399.0   \n",
       "2         44903225.0       43451666.0       39543154.0       35856344.0   \n",
       "3            44273.0          46189.0          51368.0          54849.0   \n",
       "4            79824.0          77700.0          71746.0          71519.0   \n",
       "..               ...              ...              ...              ...   \n",
       "229          11572.0          11655.0          12182.0          13142.0   \n",
       "230         575986.0         556048.0         491824.0         413296.0   \n",
       "231       33696614.0       32284046.0       28516545.0       24743946.0   \n",
       "232       20017675.0       18927715.0              NaN       13792086.0   \n",
       "233       16320537.0       15669666.0       14154937.0       12839771.0   \n",
       "\n",
       "     2000 Population  1990 Population  1980 Population  1970 Population  \\\n",
       "0         19542982.0       10694796.0       12486631.0       10752971.0   \n",
       "1          3182021.0        3295066.0        2941651.0        2324731.0   \n",
       "2         30774621.0       25518074.0       18739378.0       13795915.0   \n",
       "3            58230.0          47818.0          32886.0          27075.0   \n",
       "4            66097.0          53569.0          35611.0          19860.0   \n",
       "..               ...              ...              ...              ...   \n",
       "229          14723.0          13454.0          11315.0           9377.0   \n",
       "230         270375.0         178529.0         116775.0          76371.0   \n",
       "231       18628700.0       13375121.0        9204938.0        6843607.0   \n",
       "232        9891136.0        7686401.0        5720438.0        4281671.0   \n",
       "233       11834676.0       10113893.0        7049926.0        5202918.0   \n",
       "\n",
       "     Area (km²)  Density (per km²)  Growth Rate  World Population Percentage  \n",
       "0      652230.0            63.0587       1.0257                         0.52  \n",
       "1       28748.0            98.8702       0.9957                         0.04  \n",
       "2     2381741.0            18.8531       1.0164                         0.56  \n",
       "3         199.0           222.4774       0.9831                         0.00  \n",
       "4         468.0           170.5641       1.0100                         0.00  \n",
       "..          ...                ...          ...                          ...  \n",
       "229       142.0            81.4930       0.9953                         0.00  \n",
       "230    266000.0             2.1654       1.0184                         0.01  \n",
       "231    527968.0            63.8232       1.0217                         0.42  \n",
       "232    752612.0            26.5976       1.0280                         0.25  \n",
       "233    390757.0            41.7665       1.0204                         0.20  \n",
       "\n",
       "[234 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\alexf\\OneDrive\\Documents\\Python Tutorials\\Pandas_Folder\\world_population.csv')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97730a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexf\\AppData\\Local\\Temp\\ipykernel_1462408\\3068146767.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df.groupby('Continent')['2022 Population', '2020 Population'].count()\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
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       "      <th>Africa</th>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
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       "      <th>Asia</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
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       "      <th>Europe</th>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
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       "      <th>North America</th>\n",
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       "      <th>South America</th>\n",
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      "text/plain": [
       "               2022 Population  2020 Population\n",
       "Continent                                      \n",
       "Africa                      56               57\n",
       "Asia                        49               49\n",
       "Europe                      49               50\n",
       "North America               40               40\n",
       "Oceania                     22               23\n",
       "South America               14               14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Continent')['2022 Population', '2020 Population'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3254ed8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexf\\AppData\\Local\\Temp\\ipykernel_1462408\\2787865554.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df2 = df.groupby('Continent')['2022 Population', '2020 Population'].count()\n"
     ]
    }
   ],
   "source": [
    "df2 = df.groupby('Continent')['2022 Population', '2020 Population'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14a30fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv(r'C:\\Users\\alexf\\OneDrive\\Documents\\Python Tutorials\\Pandas_Folder\\world_population_grouped.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e9320fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_excel(r'C:\\Users\\alexf\\OneDrive\\Documents\\Python Tutorials\\Pandas_Folder\\world_population_grouped_excel.xlsx')"
   ]
  },
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