pandas_read_sheet_merge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>China</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Canada</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank      Country  Maths\n",
       "0     1    Singapore    564\n",
       "1     2    Hong Kong    548\n",
       "2     3        Macau    544\n",
       "3     4       Taiwan    542\n",
       "4     5        Japan    532\n",
       "5     6        China    531\n",
       "6     7  South Korea    524\n",
       "7     8  Switzerland    521\n",
       "8     9      Estonia    520\n",
       "9    10       Canada    516"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"https://github.com/prasertcbs/basic-dataset/raw/master/PisaScore2015.xlsx\"\n",
    "math=pd.read_excel(url,sheet_name=\"math\")\n",
    "math.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sci_math=pd.read_excel(url,sheet_name=[\"math\",\"sci\"])\n",
    "# sci_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank    Country  Maths\n",
       "0     1  Singapore    564\n",
       "1     2  Hong Kong    548\n",
       "2     3      Macau    544\n",
       "3     4     Taiwan    542\n",
       "4     5      Japan    532"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_math[\"math\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel(url,sheet_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Canada</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Finland</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank    Country  Reading\n",
       "0     1  Singapore      535\n",
       "1     2  Hong Kong      527\n",
       "2     3     Canada      527\n",
       "3     4    Finland      526\n",
       "4     5    Ireland      521"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"reading\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank_x</th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Rank_y</th>\n",
       "      <th>Science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "      <td>1</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "      <td>9</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "      <td>6</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "      <td>4</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "      <td>2</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Rank_x    Country  Maths  Rank_y  Science\n",
       "0       1  Singapore    564       1      556\n",
       "1       2  Hong Kong    548       9      523\n",
       "2       3      Macau    544       6      529\n",
       "3       4     Taiwan    542       4      532\n",
       "4       5      Japan    532       2      538"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pisa=pd.merge(df[\"math\"],df[\"sci\"],on=\"Country\")\n",
    "pisa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "      <td>538</td>\n",
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      "text/plain": [
       "     Country  Maths  Science\n",
       "0  Singapore    564      556\n",
       "1  Hong Kong    548      523\n",
       "2      Macau    544      529\n",
       "3     Taiwan    542      532\n",
       "4      Japan    532      538"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pisa=pd.merge(df[\"math\"][[\"Country\",\"Maths\"]],df[\"sci\"][[\"Country\",\"Science\"]],on=\"Country\")\n",
    "pisa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>Reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "      <td>556</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "      <td>523</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "      <td>529</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "      <td>532</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "      <td>538</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     Country  Maths  Science  Reading\n",
       "0  Singapore    564      556      535\n",
       "1  Hong Kong    548      523      527\n",
       "2      Macau    544      529      509\n",
       "3     Taiwan    542      532      497\n",
       "4      Japan    532      538      516"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pisa=pd.merge(pisa,df[\"reading\"][[\"Reading\",\"Country\"]])\n",
    "pisa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>Reading</th>\n",
       "      <th>Total</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "      <td>556</td>\n",
       "      <td>535</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "      <td>523</td>\n",
       "      <td>527</td>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "      <td>529</td>\n",
       "      <td>509</td>\n",
       "      <td>1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "      <td>532</td>\n",
       "      <td>497</td>\n",
       "      <td>1571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "      <td>538</td>\n",
       "      <td>516</td>\n",
       "      <td>1586</td>\n",
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      ],
      "text/plain": [
       "     Country  Maths  Science  Reading  Total\n",
       "0  Singapore    564      556      535   1655\n",
       "1  Hong Kong    548      523      527   1598\n",
       "2      Macau    544      529      509   1582\n",
       "3     Taiwan    542      532      497   1571\n",
       "4      Japan    532      538      516   1586"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pisa[\"Total\"]=pisa.Maths+pisa.Science+pisa.Reading\n",
    "pisa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "\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>Country</th>\n",
       "      <th>Maths</th>\n",
       "      <th>Science</th>\n",
       "      <th>Reading</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>564</td>\n",
       "      <td>556</td>\n",
       "      <td>535</td>\n",
       "      <td>1655</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>548</td>\n",
       "      <td>523</td>\n",
       "      <td>527</td>\n",
       "      <td>1598</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>532</td>\n",
       "      <td>538</td>\n",
       "      <td>516</td>\n",
       "      <td>1586</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Macau</td>\n",
       "      <td>544</td>\n",
       "      <td>529</td>\n",
       "      <td>509</td>\n",
       "      <td>1582</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>520</td>\n",
       "      <td>534</td>\n",
       "      <td>519</td>\n",
       "      <td>1573</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>542</td>\n",
       "      <td>532</td>\n",
       "      <td>497</td>\n",
       "      <td>1571</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Canada</td>\n",
       "      <td>516</td>\n",
       "      <td>528</td>\n",
       "      <td>527</td>\n",
       "      <td>1571</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Finland</td>\n",
       "      <td>511</td>\n",
       "      <td>531</td>\n",
       "      <td>526</td>\n",
       "      <td>1568</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>524</td>\n",
       "      <td>516</td>\n",
       "      <td>517</td>\n",
       "      <td>1557</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>China</td>\n",
       "      <td>531</td>\n",
       "      <td>518</td>\n",
       "      <td>494</td>\n",
       "      <td>1543</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Maths  Science  Reading  Total  Rank\n",
       "0     Singapore    564      556      535   1655     1\n",
       "1     Hong Kong    548      523      527   1598     2\n",
       "4         Japan    532      538      516   1586     3\n",
       "2         Macau    544      529      509   1582     4\n",
       "8       Estonia    520      534      519   1573     5\n",
       "3        Taiwan    542      532      497   1571     6\n",
       "9        Canada    516      528      527   1571     6\n",
       "12      Finland    511      531      526   1568     8\n",
       "6   South Korea    524      516      517   1557     9\n",
       "5         China    531      518      494   1543    10"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pisa[\"Rank\"] = pisa[\"Total\"].rank(ascending=False,method=\"min\").astype(int)\n",
    "pisa.sort_values(\"Rank\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pisa.to_csv(\"Top score.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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