world-population-analysis.ipynb

{
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/world-population/World Population.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Population Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18c5e44a",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4186bc15",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "population=pd.read_csv('../input/world-population/World Population.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a717c3e",
   "metadata": {
    "execution": {
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   },
   "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>Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1411778724</td>\n",
       "      <td>17.80%</td>\n",
       "      <td>20-Nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1386141732</td>\n",
       "      <td>17.50%</td>\n",
       "      <td>21-Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>United States</td>\n",
       "      <td>Americas</td>\n",
       "      <td>332960297</td>\n",
       "      <td>4.20%</td>\n",
       "      <td>21-Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>271350000</td>\n",
       "      <td>3.43%</td>\n",
       "      <td>20-Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>225200000</td>\n",
       "      <td>2.84%</td>\n",
       "      <td>21-Jul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank        Country    Region  Population Percentage    Date\n",
       "0     1          China      Asia  1411778724     17.80%  20-Nov\n",
       "1     2          India      Asia  1386141732     17.50%  21-Dec\n",
       "2     3  United States  Americas   332960297      4.20%  21-Dec\n",
       "3     4      Indonesia      Asia   271350000      3.43%  20-Dec\n",
       "4     5       Pakistan      Asia   225200000      2.84%  21-Jul"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.head()"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### What is the population of each region ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdb35dc4",
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    "execution": {
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   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 455.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gr=sns.displot(data=population,y='Region',hue='Region', kde=True)\n",
    "plt.title(\"Population per Region\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ef81a7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-11T07:14:58.877113Z",
     "iopub.status.busy": "2022-08-11T07:14:58.876430Z",
     "iopub.status.idle": "2022-08-11T07:14:58.885374Z",
     "shell.execute_reply": "2022-08-11T07:14:58.886014Z",
     "shell.execute_reply.started": "2022-08-11T07:05:14.113294Z"
    },
    "papermill": {
     "duration": 0.025635,
     "end_time": "2022-08-11T07:14:58.886192",
     "exception": false,
     "start_time": "2022-08-11T07:14:58.860557",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Africa         1345290019\n",
       "Americas       1024609723\n",
       "Asia           4545166611\n",
       "Asia,Europe      83614362\n",
       "Europe          600873091\n",
       "Europe,Asia     146171015\n",
       "Oceania          43609090\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.groupby('Region')['Population'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecb798c",
   "metadata": {
    "papermill": {
     "duration": 0.012794,
     "end_time": "2022-08-11T07:14:58.911657",
     "exception": false,
     "start_time": "2022-08-11T07:14:58.898863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Which country has the most population in each region ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e0cbd26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-11T07:14:58.940226Z",
     "iopub.status.busy": "2022-08-11T07:14:58.939514Z",
     "iopub.status.idle": "2022-08-11T07:14:58.948887Z",
     "shell.execute_reply": "2022-08-11T07:14:58.949515Z",
     "shell.execute_reply.started": "2022-08-11T07:05:35.864391Z"
    },
    "papermill": {
     "duration": 0.025325,
     "end_time": "2022-08-11T07:14:58.949730",
     "exception": false,
     "start_time": "2022-08-11T07:14:58.924405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Country       Region  Population\n",
      "0           China         Asia  1411778724\n",
      "2   United States     Americas   332960297\n",
      "6         Nigeria       Africa   211401000\n",
      "8          Russia  Europe,Asia   146171015\n",
      "17         Turkey  Asia,Europe    83614362\n",
      "18        Germany       Europe    83129285\n",
      "52      Australia      Oceania    25922849\n"
     ]
    }
   ],
   "source": [
    "pop=population.drop(columns=[\"Rank\",\"Date\",\"Percentage\"])\n",
    "pop.head(3)\n",
    "p=pop.groupby([\"Region\"], sort=\"True\").head(1)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88e44ba1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-11T07:14:58.978787Z",
     "iopub.status.busy": "2022-08-11T07:14:58.978116Z",
     "iopub.status.idle": "2022-08-11T07:14:59.199243Z",
     "shell.execute_reply": "2022-08-11T07:14:59.198466Z",
     "shell.execute_reply.started": "2022-08-11T07:05:43.242658Z"
    },
    "papermill": {
     "duration": 0.236537,
     "end_time": "2022-08-11T07:14:59.199400",
     "exception": false,
     "start_time": "2022-08-11T07:14:58.962863",
     "status": "completed"
    },
    "tags": []
   },
   "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.scatter(p[\"Country\"],p[\"Population\"])\n",
    "plt.title(\"Country with maximum population in each region\")\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70dc468a",
   "metadata": {
    "papermill": {
     "duration": 0.013608,
     "end_time": "2022-08-11T07:14:59.227381",
     "exception": false,
     "start_time": "2022-08-11T07:14:59.213773",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### What is the percentage of the first 10 countries ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52ce2dbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-11T07:14:59.261024Z",
     "iopub.status.busy": "2022-08-11T07:14:59.260327Z",
     "iopub.status.idle": "2022-08-11T07:14:59.267464Z",
     "shell.execute_reply": "2022-08-11T07:14:59.268035Z",
     "shell.execute_reply.started": "2022-08-11T07:11:35.674766Z"
    },
    "papermill": {
     "duration": 0.026912,
     "end_time": "2022-08-11T07:14:59.268236",
     "exception": false,
     "start_time": "2022-08-11T07:14:59.241324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Rank        Country       Region  Population Percentage    Date\n",
      "9    10         Mexico     Americas   126014024      1.59%  20-Mar\n",
      "8     9         Russia  Europe,Asia   146171015      1.85%  21-Jan\n",
      "7     8     Bangladesh         Asia   171950056      2.17%  21-Dec\n",
      "6     7        Nigeria       Africa   211401000      2.67%  21-Jul\n",
      "5     6         Brazil     Americas   214143381      2.70%  21-Dec\n",
      "4     5       Pakistan         Asia   225200000      2.84%  21-Jul\n",
      "3     4      Indonesia         Asia   271350000      3.43%  20-Dec\n",
      "2     3  United States     Americas   332960297      4.20%  21-Dec\n",
      "1     2          India         Asia  1386141732     17.50%  21-Dec\n",
      "0     1          China         Asia  1411778724     17.80%  20-Nov\n"
     ]
    }
   ],
   "source": [
    "pop=population.sort_values(by=['Population'], ascending=True)\n",
    "percent=pop.tail(10)\n",
    "print(percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b209378",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-11T07:14:59.304954Z",
     "iopub.status.busy": "2022-08-11T07:14:59.304256Z",
     "iopub.status.idle": "2022-08-11T07:14:59.307003Z",
     "shell.execute_reply": "2022-08-11T07:14:59.307484Z",
     "shell.execute_reply.started": "2022-08-11T07:13:47.213311Z"
    },
    "papermill": {
     "duration": 0.024816,
     "end_time": "2022-08-11T07:14:59.307675",
     "exception": false,
     "start_time": "2022-08-11T07:14:59.282859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 10 countries have a total of 56.75% population\n",
      "The bottom 10 countries have a total of 19.0% population\n"
     ]
    }
   ],
   "source": [
    "pt=pop.tail(10)[\"Percentage\"].tolist()\n",
    "sum=0\n",
    "for i in range(len(pt)):\n",
    "    sum+=float(pt[i][:-1])\n",
    "print(\"The top 10 countries have a total of {0}% population\".format(sum))\n",
    "\n",
    "pd=pop.tail(10)[\"Percentage\"].tolist()\n",
    "res=0\n",
    "for i in range(len(pd)):\n",
    "    res+=float(pt[i][:1])\n",
    "print(\"The bottom 10 countries have a total of {}% population\".format(res))"
   ]
  },
  {
   "cell_type": "code",
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