Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "devoted-width",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "careful-scenario",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "y=[4,5,6]\n",
    "z=[12,5,4]\n",
    "\n",
    "plt.title(\"test plots\")\n",
    "plt.plot(x,y)\n",
    "plt.plot(x,z)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y and z\")\n",
    "plt.legend([\"this is y\",\"this is z\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "asian-notification",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = pd.read_csv(\"sample_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adult-rubber",
   "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>column_a</th>\n",
       "      <th>column_b</th>\n",
       "      <th>column_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column_a  column_b  column_c\n",
       "0         1         1        10\n",
       "1         2         4         8\n",
       "2         3         9         6\n",
       "3         4        16         4\n",
       "4         5        25         2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "forced-timer",
   "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>column_a</th>\n",
       "      <th>column_b</th>\n",
       "      <th>column_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column_a  column_b  column_c\n",
       "0         1         1        10\n",
       "1         2         4         8\n",
       "2         3         9         6\n",
       "3         4        16         4\n",
       "4         5        25         2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "broadband-provincial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "annual-bailey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     8\n",
       "2     6\n",
       "3     4\n",
       "4     2\n",
       "Name: column_c, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.column_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "enclosed-machine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sample_data.column_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "lasting-prisoner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.column_c.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "educated-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sample_data.column_a,sample_data.column_b,'o')\n",
    "plt.plot(sample_data.column_a,sample_data.column_c)\n",
    "plt.xlabel(\"a\")\n",
    "plt.ylabel(\"b and c\")\n",
    "plt.legend([\"this is b\",\"this is c\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "therapeutic-volleyball",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"countries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "extended-object",
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population\n",
       "0     Afghanistan  1952     8425333\n",
       "1     Afghanistan  1957     9240934\n",
       "2     Afghanistan  1962    10267083\n",
       "3     Afghanistan  1967    11537966\n",
       "4     Afghanistan  1972    13079460\n",
       "...           ...   ...         ...\n",
       "1699     Zimbabwe  1987     9216418\n",
       "1700     Zimbabwe  1992    10704340\n",
       "1701     Zimbabwe  1997    11404948\n",
       "1702     Zimbabwe  2002    11926563\n",
       "1703     Zimbabwe  2007    12311143\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "paperback-equilibrium",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>Afghanistan</td>\n",
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      "text/plain": [
       "       country  year  population\n",
       "0  Afghanistan  1952     8425333\n",
       "1  Afghanistan  1957     9240934\n",
       "2  Afghanistan  1962    10267083\n",
       "3  Afghanistan  1967    11537966\n",
       "4  Afghanistan  1972    13079460"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "unique-nothing",
   "metadata": {},
   "outputs": [],
   "source": [
    "us = data[data.country=='United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "solar-bosnia",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1610</th>\n",
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       "      <th>1611</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>United States</td>\n",
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       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>United States</td>\n",
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       "      <td>United States</td>\n",
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       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>United States</td>\n",
       "      <td>1997</td>\n",
       "      <td>272911760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>United States</td>\n",
       "      <td>2002</td>\n",
       "      <td>287675526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>United States</td>\n",
       "      <td>2007</td>\n",
       "      <td>301139947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  year  population\n",
       "1608  United States  1952   157553000\n",
       "1609  United States  1957   171984000\n",
       "1610  United States  1962   186538000\n",
       "1611  United States  1967   198712000\n",
       "1612  United States  1972   209896000\n",
       "1613  United States  1977   220239000\n",
       "1614  United States  1982   232187835\n",
       "1615  United States  1987   242803533\n",
       "1616  United States  1992   256894189\n",
       "1617  United States  1997   272911760\n",
       "1618  United States  2002   287675526\n",
       "1619  United States  2007   301139947"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "differential-expense",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1699    False\n",
       "1700    False\n",
       "1701    False\n",
       "1702    False\n",
       "1703    False\n",
       "Name: country, Length: 1704, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.country == 'United States'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "basic-shepherd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1616</th>\n",
       "      <td>United States</td>\n",
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       "      <th>1617</th>\n",
       "      <td>United States</td>\n",
       "      <td>1997</td>\n",
       "      <td>272911760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>United States</td>\n",
       "      <td>2002</td>\n",
       "      <td>287675526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>United States</td>\n",
       "      <td>2007</td>\n",
       "      <td>301139947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  year  population\n",
       "1608  United States  1952   157553000\n",
       "1609  United States  1957   171984000\n",
       "1610  United States  1962   186538000\n",
       "1611  United States  1967   198712000\n",
       "1612  United States  1972   209896000\n",
       "1613  United States  1977   220239000\n",
       "1614  United States  1982   232187835\n",
       "1615  United States  1987   242803533\n",
       "1616  United States  1992   256894189\n",
       "1617  United States  1997   272911760\n",
       "1618  United States  2002   287675526\n",
       "1619  United States  2007   301139947"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.country=='United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "second-tobago",
   "metadata": {},
   "outputs": [],
   "source": [
    "Egypt = data[data.country=='Egypt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "corresponding-matthew",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "      <td>1957</td>\n",
       "      <td>25009741</td>\n",
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       "    <tr>\n",
       "      <th>458</th>\n",
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       "      <td>28173309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Egypt</td>\n",
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       "      <td>31681188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Egypt</td>\n",
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       "      <td>34807417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>Egypt</td>\n",
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       "      <td>38783863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1982</td>\n",
       "      <td>45681811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1987</td>\n",
       "      <td>52799062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1992</td>\n",
       "      <td>59402198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1997</td>\n",
       "      <td>66134291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2002</td>\n",
       "      <td>73312559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2007</td>\n",
       "      <td>80264543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year  population\n",
       "456   Egypt  1952    22223309\n",
       "457   Egypt  1957    25009741\n",
       "458   Egypt  1962    28173309\n",
       "459   Egypt  1967    31681188\n",
       "460   Egypt  1972    34807417\n",
       "461   Egypt  1977    38783863\n",
       "462   Egypt  1982    45681811\n",
       "463   Egypt  1987    52799062\n",
       "464   Egypt  1992    59402198\n",
       "465   Egypt  1997    66134291\n",
       "466   Egypt  2002    73312559\n",
       "467   Egypt  2007    80264543"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Egypt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "periodic-meditation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(us.year,us.population/1000000)\n",
    "plt.plot(Egypt.year,Egypt.population/10**6)\n",
    "plt.legend(['usa','egypt'])\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"population\")\n",
    "plt.title(\"comparing usa and egy growth rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "seven-debate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(us.year,us.population/us.population.iloc[0] * 100)\n",
    "plt.plot(Egypt.year,Egypt.population/Egypt.population.iloc[0] * 100)\n",
    "plt.legend(['usa','egypt'])\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"population growth (1st yr = 100%)\")\n",
    "plt.title(\"comparing usa and egy growth rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alien-communication",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}