cities_pandas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled7.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMlW1TO41nFeNb/1Cw0A0l8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wingated/cs180_labs/blob/main/cities_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ej_UhCBbma-",
        "outputId": "901c9868-b8b0-4822-a495-ce32053dd235"
      },
      "source": [
        "# run wget to download data\n",
        "!wget http://liftothers.org/byu/worldcities.csv"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-01-27 15:20:51--  http://liftothers.org/byu/worldcities.csv\n",
            "Resolving liftothers.org (liftothers.org)... 50.62.229.1\n",
            "Connecting to liftothers.org (liftothers.org)|50.62.229.1|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2859311 (2.7M) [text/csv]\n",
            "Saving to: ‘worldcities.csv’\n",
            "\n",
            "\rworldcities.csv       0%[                    ]       0  --.-KB/s               \rworldcities.csv      94%[=================>  ]   2.58M  12.9MB/s               \rworldcities.csv     100%[===================>]   2.73M  13.3MB/s    in 0.2s    \n",
            "\n",
            "2021-01-27 15:20:51 (13.3 MB/s) - ‘worldcities.csv’ saved [2859311/2859311]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqGqAyL-bslv"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM1B69dubvB2"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "plt.rcParams['figure.figsize'] = [15, 10]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65uknWWebzWV"
      },
      "source": [
        "df = pd.read_csv( \"./worldcities.csv\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgNTp5k2b3lf",
        "outputId": "bf3ad75f-0e2a-44ce-c221-4351074f16a3"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26569"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iHu1VVDcBc-",
        "outputId": "60f0e48d-24ec-4103-e07c-88e58b6ce001"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['city', 'city_ascii', 'lat', 'lng', 'country', 'iso2', 'iso3',\n",
              "       'admin_name', 'capital', 'population', 'id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LGYqcv-cCIl",
        "outputId": "e2a83170-7a8f-48b9-eb15-507f9b925652"
      },
      "source": [
        "df['population']"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        37977000.0\n",
              "1        34540000.0\n",
              "2        29617000.0\n",
              "3        23355000.0\n",
              "4        23088000.0\n",
              "            ...    \n",
              "26564          10.0\n",
              "26565          10.0\n",
              "26566           0.0\n",
              "26567           0.0\n",
              "26568           0.0\n",
              "Name: population, Length: 26569, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "intz2vNecEyH",
        "outputId": "9c9ca3e7-b2aa-4d52-eaa0-adb88efd843f"
      },
      "source": [
        "plt.plot( df['population'])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f66bde15198>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aa29vrW1vrW3funXrqbzEaXX00ke9RAAAgC4tKahV1ZoMQ9pvt9b+cMohdye5fOzxU0fbzijHmokIagAAQIeW0vWxkrwjyadba798nMOuTfL6UffHFyfZ3Vq7ZxnrXBF1tD1/x3UAAACr21K6Pn5dku9O8qmqumG07S1JnpYkrbXfSHJdklcnuS3JviTfu/ylnn5Vix8DAABwui0a1Fprf5uxzvXHOaYl+ZHlKqprFrwGAAC6dFJdH1cLMQ0AAOiSoDZGMxEAAKAPBLUx9ViD/k7rAAAAVjdBbYwRNQAAoA8EtTG6PgIAAH0gqE1hQA0AAOiSoDbm2ILXkhoAANAhQW3MsTlqxtQAAIAOCWpjjvV8lNMAAIAOCWpjHhtRAwAA6I6g9jjaPgIAAN0T1KZorn0EAAA6JKiNsY4aAADQB4LaGM1EAACAPhDUxtRoSE17fgAAoEuC2hgjagAAQB8IagAAAD0jqI05to6aETUAAKBDgtqYytE5agAAAN0R1MY8NqImqgEAAN0R1KYQ0wAAgC4JamMseA0AAPSBoDaNITUAAKBDgtoYC14DAAB9IKiNseA1AADQB4LamGNdH7stAwAAWOUEtTHH1lGT1AAAgA4JamN0fQQAAPpAUJtCMxEAAKBLgtoYzUQAAIA+ENTGaSYCAAD0gKA2po4lNVENAADojqA2Rnt+AACgDwS1MZo+AgAAfSCoTeHKRwAAoEuC2piqowteS2oAAEB3BLUxx9rzd1oFAACw2glqY0rTRwAAoAcEtTFH2/PLaQAAQJcENQAAgJ4R1MYdu/TRmBoAANAdQW1MWUgNAADoAUFtzLGujwbUAACADglqY46to6adCAAA0CFBbcxAe34AAKAHBLUxg9GI2oKgBgAAdEhQG3O0mciCITUAAKBDgtqYoyNq2vMDAABdEtTGuPQRAADoA0FtzMCljwAAQA8IamPKiBoAANADgtqEQZmjBgAAdEtQmzCocukjAADQKUFtwjCodV0FAACwmglqk0ozEQAAoFuC2oThHLWuqwAAAFazRYNaVb2zqu6vqpuOs/+lVbW7qm4Y3d66/GWunEGVZiIAAECnZpdwzLuSvC3Je05wzN+01r5tWSrqmDlqAABA1xYdUWut/XWSh1agll4oc9QAAICOLdcctZdU1Y1V9f6q+orjHVRV11TVjqrasXPnzmV66+U1vPSx6yoAAIDVbDmC2ieSXNFae16SX03y3453YGvt7a217a217Vu3bl2Gt15+AyNqAABAx550UGutPdJa2zu6f12SNVW15UlX1hELXgMAAF170kGtqi6pqhrdf9HoNR98sq/bldJMBAAA6NiiXR+r6neSvDTJlqq6K8lPJ1mTJK2130jy2iQ/VFVHkuxPcnU7g/vbD9dRO2PLBwAAzgKLBrXW2nctsv9tGbbvPysMqrKw0HUVAADAarZcXR/PGpqJAAAAXRPUJpijBgAAdE1QmzAYmKMGAAB0S1CboD0/AADQNUFtwsCljwAAQMcEtQmlmQgAANAxQW3CoCpyGgAA0CVBbYL2/AAAQNcEtQmaiQAAAF0T1CZYRw0AAOiaoDZhUNZRAwAAuiWoTdCeHwAA6JqgNkEzEQAAoGuC2gRz1AAAgK4JahPMUQMAALomqE0YVGXekBoAANAhQW3CoCoG1AAAgC4JahNKMxEAAKBjgtoEI2oAAEDXBLUJg4ERNQAAoFuC2oRKCWoAAECnBLUJVYmYBgAAdElQmzCw4DUAANAxQW2CBa8BAICuCWoThiNqghoAANAdQW1CVWVhoesqAACA1UxQmzCw4DUAANAxQW2CBa8BAICuCWoTLHgNAAB0TVCbUJqJAAAAHRPUJrj0EQAA6JqgNkEzEQAAoGuC2oThOmpdVwEAAKxmgtqEMqIGAAB0TFCbYI4aAADQNUFtgjlqAABA1wS1CQPt+QEAgI4JahOGc9S6rgIAAFjNBLUJVZVmRA0AAOiQoDZhUNFMBAAA6JSgNsEcNQAAoGuC2gQLXgMAAF0T1CZY8BoAAOiaoDbBgtcAAEDXBLUJFrwGAAC6JqhN0EwEAADomqA2YTCozOsmAgAAdEhQmzArqAEAAB0T1CbMDIbt+ReENQAAoCOC2oTZQSVJ5s1TAwAAOiKoTZgZDL8lLn8EAAC6IqhNODqidnh+oeNKAACA1UpQmzA7M7r00YgaAADQEUFtwtERtSOCGgAA0BFBbYI5agAAQNcEtQlG1AAAgK4tGtSq6p1VdX9V3XSc/VVVv1JVt1XVJ6vqhctf5sqZORrUNBMBAAA6spQRtXcleeUJ9r8qyVWj2zVJfv3Jl9Wdo81EjKgBAABdWTSotdb+OslDJzjkNUne04Y+kuS8qrp0uQpcabPmqAEAAB1bjjlqlyW5c+zxXaNtT1BV11TVjqrasXPnzmV46+X32KWPghoAANCNFW0m0lp7e2tte2tt+9atW1fyrZfsaDMRI2oAAEBXliOo3Z3k8rHHTx1tOyPNHJujppkIAADQjeUIatcmef2o++OLk+xurd2zDK/bCe35AQCArs0udkBV/U6SlybZUlV3JfnpJGuSpLX2G0muS/LqJLcl2Zfke09XsSvBHDUAAKBriwa11tp3LbK/JfmRZauoY2tmdH0EAAC6taLNRM4Ex0bUzFEDAAA6IqhN0PURAADomqA2YUYzEQAAoGOC2oTZwfBbopkIAADQFUFtwqx11AAAgI4JahPMUQMAALomqE0wRw0AAOiaoDbh6Bw1I2oAAEBXBLUJx0bU5s1RAwAAuiGoTZh16SMAANAxQW3C0a6PLn0EAAC6IqhNOLaOmqAGAAB0RFCbMKM9PwAA0DFBbcKxOWrzghoAANANQW3CYFCpSo4s6PoIAAB0Q1CbYnZQ5qgBAACdEdSmmB0MzFEDAAA6I6hNMTsoc9QAAIDOCGpTzMxU5s1RAwAAOiKoTWGOGgAA0CVBbYoZlz4CAAAdEtSmmB0MjKgBAACdEdSmmDVHDQAA6JCgNsXsoHLYpY8AAEBHBLUpNszN5tFDR7ouAwAAWKUEtSnWr53JvoPzXZcBAACsUoLaFBvnZrP3oBE1AACgG4LaFOvXzmafSx8BAICOCGpTbJibzV6XPgIAAB0R1KbYsHbGiBoAANAZQW2KjefMZt+h+RyZt5YaAACw8gS1KbZsnEuSPPTooY4rAQAAViNBbYqtm4ZB7d5HDnRcCQAAsBoJalNcsGFtEiNqAABANwS1KTbOzSZJ9h/S+REAAFh5gtoU69bMJEn2HxbUAACAlSeoTbF+7TCo7TOiBgAAdEBQm2LdKKi59BEAAOiCoDbF+rXDOWqPWvQaAADogKA2xcygsm7NjEsfAQCATghqx7FhbiZ7DxpRAwAAVp6gdhwb5mbzqKAGAAB0QFA7jvVrZ/PoQZc+AgAAK09QO46NczNG1AAAgE4IasexYW5W10cAAKATgtpxbFg7q5kIAADQCUHtODbMzWSfOWoAAEAHBLXj0PURAADoiqB2HBvWzmbPwSNprXVdCgAAsMoIasdx3vo1SZKH9x3uuBIAAGC1EdSO49LN65Ik9z1yoONKAACA1UZQO45LNs8lSe4V1AAAgBUmqB3HRZvOSZLcL6gBAAArTFA7jovOHY2o7T7YcSUAAMBqI6gdx9zsTLZsnMvdu/Z1XQoAALDKCGoncOWW9bnjAUENAABYWUsKalX1yqr6bFXdVlVvmrL/DVW1s6puGN1+YPlLXXnbLtyQOx58tOsyAACAVWZ2sQOqaibJryV5RZK7knysqq5trd0yceh7W2tvPA01duay89dl596DOXRkIWtnDT4CAAArYynp40VJbmutfaG1dijJ7yZ5zektqx+esnldWrOWGgAAsLKWEtQuS3Ln2OO7Rtsm/cuq+mRVva+qLp/2QlV1TVXtqKodO3fuPIVyV9Ylm4ct+r+0a3/HlQAAAKvJcl3P98dJtrXWnpvkg0nePe2g1trbW2vbW2vbt27dukxvffo85bxhULPoNQAAsJKWEtTuTjI+QvbU0bZjWmsPttaOLjj2m0m+annK69Ylm9clSb60S1ADAABWzlKC2seSXFVVV1bV2iRXJ7l2/ICqunTs4bcn+fTyldidjXOz2XTObO7Z7dJHAABg5Sza9bG1dqSq3pjkz5LMJHlna+3mqvrZJDtaa9cm+TdV9e1JjiR5KMkbTmPNK+opm9flnt1G1AAAgJWzaFBLktbadUmum9j21rH7b07y5uUtrR8uPe8cI2oAAMCKsjjYIi7dfE7uMUcNAABYQYLaIi7dvC4PPnooBw7Pd10KAACwSghqi7h0tJaaeWoAAMBKEdQWcdXFm5Ikn7tvT8eVAAAAq4WgtoijI2o79xxc5EgAAIDlIagt4oINazMzqNzxwKNdlwIAAKwSgtoi1swM8pTzzskDe42oAQAAK0NQW4LN69bkkQNHui4DAABYJQS1Jdi8bk127z/cdRkAAMAqIagtwZaNc7nvEe35AQCAlSGoLcHTLlife3YfyOH5ha5LAQAAVgFBbQmu3LIh8wtN50cAAGBFCGpL8IzRote33r+340oAAIDVQFBbgm1bNiRJvvjgvo4rAQAAVgNBbQk2zs1my8a1+eKDLn0EAABOP0Ftia64cEPuENQAAIAVIKgt0RUXrnfpIwAAsCIEtSXaduGG3LP7QA4cnu+6FAAA4CwnqC3RFReuT5L840NG1QAAgNNLUFuiK0edHz9z756OKwEAAM52gtoSfcVTNidJfuUvb+24EgAA4GwnqC3RzKBy6eZzctv9e81TAwAATitB7SS89duencTljwAAwOklqJ2E51w2vPzx93bc2XElAADA2UxQOwmXnbcuSfJfr//HjisBAADOZoLaSRgMavhndVwIAABwVhPUTtIPv/TpWWjJ7n2Huy4FAAA4SwlqJ+lpFwwXvn7LH32q40oAAICzlaB2kr7jBZclSe548NGOKwEAAM5WgtpJOmfNTL7j+U/JQ48e6roUAADgLCWonYIXXnF+7tl9IJ/fubfrUgAAgLOQoHYKXvHsizOo5H0fv6vrUgAAgLOQoHYKLt28Lt/wjK354xu/lNZa1+UAAABnGUHtFH3bc5+Sux7en4/e/lDXpQAAAGcZQe0UfdOXX5Qk+an/flPHlQAAAGcbQe0Unbd+bZ558aZ87r692bnnYNflAAAAZxFB7Un4ue98TpLkJyx+DQAALCNB7UnYfsX5SZI/v+W+7Dt0pONqAACAs4Wg9iRUVf7ja5+bJPnWX/nbjqsBAADOFoLak/S67ZcnSW5/4NF89t49HVcDAACcDQS1ZfDRn3h5tm6ayw/99sdz4PB81+UAAABnOEFtGVy06Zz8x9c+N1/Y+Wie9VMf6LocAADgDCeoLZNvfMbWY/d/5tqbO6wEAAA40wlqy6Sq8pE3vzxJ8q6/vyNv0bIfAAA4RYLaMrpk8zn5xE+9IhduWJv/ev0/Ztub/jSPHtS2HwAAODmC2jK7YMPa/MW/+8Zjj7/ip/8sN9y5K0nyc39yS655z44sLLSuygMAAM4AgtppcP6GtbnjF741WzfNJUl++YOfy/xCy4137cqf33Jf/slbrsu9uw90XCUAANBXgtpp9LGf+KZcfO5cNp0zm5lB5ff+t5dk+xXnJ0le/PN/ma/+P/8ih44sdFwlAADQN4LaafZlF23MPbv2Jxk2HPn9H3xJvuyijUmSnXsO5hk/+f78l7+6La25HBIAABgS1E6zqy7alJu/9MixkbOqyl/8u2/M7T//6mPH/IcPfDZXvvm6bHvTn+ae3fu7KhUAAOgJQe00277t/Bw8spBP3/PI47ZXVe74hW/NJ37qFXnZsy46tv0lP/+hbHvTn2bbm/40H/rMfUbaAABgFZrtuoCz3dHLHO96eH+ed/l5T9h/wYa1eecbvjrbf+4v8sDeg4/b933v2nHs/qu/8pJ879ddmRdcfl5mZ+RrAAA4mwlqp9n569cmSR7ed+iEx133o1+fLz64L39y45dy+4P78m+/6apc81sfz849w/B23afuzXWfuvcJz/uftj81b37Vl+f8DWuXv3gAAKAT1dWlddu3b287duxY/MAz3KEjC3nGT74/SXLHL3zrKb3GA3sP5sOfuX/Y3v/m+5G5ukMAAA87SURBVHL/noMnPP5rrrwg3/GCy/JlF23MMy7alM3r15zS+wIAAKdPVX28tbZ92j4jaqfZ2tknf5nilo1zed32y/O67Zfn577jK5Mkf/XZ+/OG/+djSZLnX37esUW1k+T62x/K9bc/NPW1LjtvXe7etT8Xblibb3zm1iwstLz+a7dl09xsLtl8Tjasnc1gUE+6ZgAA4NQZUVsBb/vQrfm//vxz+ehbXp6Lzj3ntLzHwkLLw/sO5dx1a/LQo4fy3o/dmT/4xF354oP7jh2zaW42MzOVXfsOP6n3etYlm3Lg8HzufeRArtyyMS942nl54dPOz5qZyhUXbsjF585lw9zsMPTVsHEKAADweCcaUVtSUKuqVyb5z0lmkvxma+0XJvbPJXlPkq9K8mCSf9Vau+NEr7magtoHb7kv/+t7duRH/tnT8398y7O6Lie79h3Ku/7+jlx10aY8sPdgLjtvXW7+0iN59z/ckdZa5hdaHjlw5LTX8axLNuW+Rw7k4X2H86rnXJL33/TYHLztV5yfHV98ON/87Ivz57fcd2z7j7/iGZlbM8jWTXPZf2ghN965K1ddvDG33rc3X9q9P8+//LzsPzSfLZvmsu3CDXnq+etyeH4ha2YGOXhkPhdsmMuBw/M5b/2arF8zm9mZYYicnanMDgapxIgiAAAr4kkFtaqaSfK5JK9IcleSjyX5rtbaLWPH/HCS57bWfrCqrk7yna21f3Wi111NQe0LO/fmZb/0P5Kc+jy1lXbHA4/mgb0Hs33bBUmS+YWWBx89mM/duzfPunRTbrt/b/76czuz79B89hw4kovOncuXdu3P1z79wiy05NGDR7J7/+H86odue8Jrn79+TR7edzjPvvTcDAbJTXc/kqsu2phb79+70l/mCQ0qWZj45zEzqMyPbbxyy4bc/sCjU5//z5/3lHz23keyed2a3PfIwXzTl1+cd/7d7flfXvy03PnQ/vzdbQ/kh//Zl+WZF2/Ku//+jvzz512az9y7J3c9vD+79h3KBRvW5uVffnEuO39d7t19IF9+6bnZsHYm9+85mIvPncuXdh3Ihrnh1csXbZrLYFCZqcqamcrD+w7lnt0H8lVXnH+s5taSqmRudubYSOfCQkv1YNTzyPzCsnUzba2t+Ndz63178ppf+7vMDCprZwZZMzPImtnKmplB1s4MsnZ2kNnB8PHsTGVmMMjamUpVZXZQmTl6q8pgMNx29O9zZuzxoB6/b1DDXyxUZfR4eH8w2jczGL7H0W2V4Z8ZfzxIKnXsPBjU8PHwHBluO/q8o6+TidcbVCZec/jntPeuo6+bOvbeJ3qv8fNz+JrD54yb/Ouu4z44uedOnkeTZ9UTn1vjD5Ze48R7ncz7LHaqn8xzT1TT5P4nPNeVEwCn5MkGtZck+ZnW2reMHr85SVprPz92zJ+NjvmHqppNcm+Sre0EL76aglqSbHvTnyZJvvKyzfnjf/31HVezcnbvO5ydew+kqnLRprlsOmdpjU0OHJ7Pw/sOZcvGuezefzi79h3Orn2H8vStG3PgyHz2HjiSA4cXcuv9e7Jhbja3P/BoPnb7Q3lg78E8vO9wdu45mO3bzs93vuCyfOyOh/L5+x/NMy7ZmIOHF3LwyEK2bzs/R+Zb3vG3t+cZF2/MRZvOyXt33Jlv+YqLs/fgcDTxaResz+/vuCtHxoLZ2plBDs0vnJbvVV8dDdbnr1+T/Yfns2ZmkD2jEdeLNs2lJRP/ua/sO3Qk69fOZmbw2LZK8oVRqH3WJZuyZmaQmUE9bn7lNM+8eFM+e9+eZft63vW9X52XPvOixQ88CffvOZC3/48vZL61HJ5fyOEjLYfmF3JofiFH5hdy6MhCjiy0HDyykPmFliMLLYePLGRhNII9v9Cy0IbbFxZa5se2H5kfPl5oLQsLObYPzhTLGgifmLxP+bl1/Fy9aEg/QSZ/4nOX7ZcBJ/ELikx+fSf73OMH7yeG9MnXOrVfBpzoe3yyzz2Zv/snvO1J/YJi6e/zxK9v6X/3ffw3M+3xpCe89+T+RZ4/M/oF5aAqM4PhL+8OHJ7PQ/sOZ6Zy7JecR48Z/hIzE9tGdVRy7jmz+fl/8dwTv2kHnmwzkcuS3Dn2+K4kX3O8Y1prR6pqd5ILkzwwUcg1Sa5Jkqc97WlLKv5s8f4f/ad51X/+m9z58L7FDz6LbF6/5pS6Tp6zZiaXbl6XZNhMZcvGuYkXHv7xlU/dfGzTD37j06e+1r944VOP+z7f9/VXHrv/i6994j/eaf+g9xw4nHVrZo6NAD2492CqKpvXrcmghqOPu/YPj9lz4EgGg+SeXQdywYa1ufGuXbl087q01vKBm+7NP33G1lxy7jn5+88/kA1zs/mrz96fhYXkAzffm62b5vKiKy/IwkLL/XsO5vUvuSJJ8tl792TLxrn87J/ckm0Xrs8dD+7Lv/2mZ2Tn3gP5fz/yj3npM7dm/dqZXPepe/OibRfk7l3785WXbc4Hbr43z7pkUz5z7zD0zA4qWzbO5aXP3JpN58zmj/6/L2X3/kM5PN/y0mduzQXr1+b9N92bVz7nkuw9OJ9BJW00Wnrr/Xtz3vo1ueLCDVkzGilqGY7azbeWfQfnMzNTWTOotAxHJg8dmT8W1C6/YH3mF4ah5qj1a2ey79D8E77f565b3p5He07DZb0XbTonP/ltz1721z2e1loWWobhrQ2/7wtj29oo0A3DXUtLjh3TMpzTmuRxz22j1x0+TlqGwfDo3+vjnt+Obnvs9Y4+Z9qxaUt4r/bYtsfe+7Faj732E74XE4/HjnjivhM/uR1/VyZ/73iiOp6478TB+vHPbcfdt2iNizz3RDUt9r061RpP9D2e/tyl//2d8Otf9LnL8z6LzR450XlzMn9/i9U0ecTjnnuS58nJ1Hiih8v5b+ZEX+6iX8+ynWMn+2+mnWDf0p876aS+npM8xx53fHt8ZSfz+fL4Zx5n/2Kfi8mxX2LOLxz9OTOsYvO6NVm3ZubYLzTnM/wl6HwbHnf0eeM/d5Lh2sVnmqWMqL02yStbaz8wevzdSb6mtfbGsWNuGh1z1+jx50fHPDDtNZPVN6KW5NhvytdYsBoe52QvVzz6uXW858wvtMyYawgA9NyJRtSWkhjuTnL52OOnjrZNPWZ06ePmDJuKMGYwGnkAHu9k57cM5z4d/zlCGgBwpltKavhYkquq6sqqWpvk6iTXThxzbZLvGd1/bZIPnWh+GgAAAMe36OSP0ZyzNyb5swzb87+ztXZzVf1skh2ttWuTvCPJb1XVbUkeyjDMAQAAcAqWNEu/tXZdkusmtr117P6BJK9b3tIAAABWJxOmAAAAekZQAwAA6BlBDQAAoGcENQAAgJ4R1AAAAHpGUAMAAOgZQQ0AAKBnBDUAAICeEdQAAAB6RlADAADoGUENAACgZwQ1AACAnhHUAAAAekZQAwAA6BlBDQAAoGcENQAAgJ4R1AAAAHpGUAMAAOgZQQ0AAKBnqrXWzRtX7UzyxU7e/MS2JHmg6yI4Izl3eDKcP5wq5w6nyrnDqXLuLJ8rWmtbp+3oLKj1VVXtaK1t77oOzjzOHZ4M5w+nyrnDqXLucKqcOyvDpY8AAAA9I6gBAAD0jKD2RG/vugDOWM4dngznD6fKucOpcu5wqpw7K8AcNQAAgJ4xogYAANAzghoAAEDPCGpjquqVVfXZqrqtqt7UdT30Q1XdUVWfqqobqmrHaNsFVfXBqrp19Of5o+1VVb8yOoc+WVUvHHud7xkdf2tVfU9XXw+nT1W9s6rur6qbxrYt27lSVV81OhdvGz23VvYr5HQ5zrnzM1V19+iz54aqevXYvjePzoPPVtW3jG2f+nOsqq6squtH299bVWtX7qvjdKqqy6vqw1V1S1XdXFU/Otrus4cTOsG547OnL1prbsN5ejNJPp/knyRZm+TGJM/uui637m9J7kiyZWLbf0jyptH9NyX5xdH9Vyd5f5JK8uIk14+2X5DkC6M/zx/dP7/rr81t2c+Vb0jywiQ3nY5zJclHR8fW6Lmv6vprdjut587PJPnfpxz77NHPqLkkV45+ds2c6OdYkt9LcvXo/m8k+aGuv2a3ZTt3Lk3ywtH9TUk+NzpHfPa4neq547OnJzcjao95UZLbWmtfaK0dSvK7SV7TcU3012uSvHt0/91JvmNs+3va0EeSnFdVlyb5liQfbK091Fp7OMkHk7xypYvm9Gqt/XWShyY2L8u5Mtp3bmvtI234E+89Y6/FGe44587xvCbJ77bWDrbWbk9yW4Y/w6b+HBuNfrwsyftGzx8/DznDtdbuaa19YnR/T5JPJ7ksPntYxAnOnePx2bPCBLXHXJbkzrHHd+XEJyurR0vy51X18aq6ZrTt4tbaPaP79ya5eHT/eOeR82v1Wq5z5bLR/cntnN3eOLo87Z1HL13LyZ87FybZ1Vo7MrGds0xVbUvygiTXx2cPJ2Hi3El89vSCoAaL+/rW2guTvCrJj1TVN4zvHP2G0ToXLMq5wkn69SRPT/L8JPck+aVuy6HPqmpjkj9I8mOttUfG9/ns4USmnDs+e3pCUHvM3UkuH3v81NE2VrnW2t2jP+9P8kcZDvHfN7ocJKM/7x8dfrzzyPm1ei3XuXL36P7kds5SrbX7WmvzrbWFJP93hp89ycmfOw9meHnb7MR2zhJVtSbD/2j/dmvtD0ebffawqGnnjs+e/hDUHvOxJFeNutOsTXJ1kms7romOVdWGqtp09H6Sb05yU4bnxtGOWN+T5L+P7l+b5PWjrlovTrJ7dOnJnyX55qo6f3QJwTePtnH2W5ZzZbTvkap68ei6/9ePvRZnoaP/yR75zgw/e5LhuXN1Vc1V1ZVJrsqw2cPUn2Oj0ZQPJ3nt6Pnj5yFnuNHnwTuSfLq19stju3z2cELHO3d89vRI191M+nTLsBPS5zLsXPMTXdfj1v0tww5GN45uNx89LzK87vovk9ya5C+SXDDaXkl+bXQOfSrJ9rHX+r4MJ97eluR7u/7a3E7L+fI7GV4mcjjDa/G/fznPlSTbM/yB+fkkb0tSXX/Nbqf13Pmt0bnxyQz/g3Tp2PE/MToPPpuxDnzH+zk2+iz76Oic+v0kc11/zW7Ldu58fYaXNX4yyQ2j26t99rg9iXPHZ09PbjX6JgIAANATLn0EAADoGUENAACgZwQ1AACAnhHUAAAAekZQAwAA6BlBDQAAoGcENQAAgJ75/wF+7z6G6UPzHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "DFSjywGucIJ6",
        "outputId": "58d084fe-4e2c-4d95-eef2-a4d6e8576b17"
      },
      "source": [
        "plt.plot( df['population'][ df['population'] < 100000  ])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f66bcad95c0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvgbyJPBcZ--"
      },
      "source": [
        "tmp = df.groupby('country')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Tlh4WScdGy9",
        "outputId": "ddf8d9d1-4bd8-4158-cdc1-fd047b436ebc"
      },
      "source": [
        "len(tmp)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "224"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "x--WZg3-chkO",
        "outputId": "3557c7b0-2306-4a4c-a94a-84e38756c7e6"
      },
      "source": [
        "tmp.describe()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">lat</th>\n",
              "      <th colspan=\"8\" halign=\"left\">lng</th>\n",
              "      <th colspan=\"8\" halign=\"left\">population</th>\n",
              "      <th colspan=\"8\" halign=\"left\">id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>44.0</td>\n",
              "      <td>34.755439</td>\n",
              "      <td>1.654872</td>\n",
              "      <td>30.9600</td>\n",
              "      <td>33.689625</td>\n",
              "      <td>34.73335</td>\n",
              "      <td>36.229400</td>\n",
              "      <td>37.1298</td>\n",
              "      <td>44.0</td>\n",
              "      <td>67.488482</td>\n",
              "      <td>2.510152</td>\n",
              "      <td>61.8600</td>\n",
              "      <td>65.838025</td>\n",
              "      <td>68.44525</td>\n",
              "      <td>69.166325</td>\n",
              "      <td>71.1528</td>\n",
              "      <td>39.0</td>\n",
              "      <td>191654.487179</td>\n",
              "      <td>585899.547232</td>\n",
              "      <td>4938.0</td>\n",
              "      <td>17742.0</td>\n",
              "      <td>51075.0</td>\n",
              "      <td>151796.50</td>\n",
              "      <td>3678034.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1.004534e+09</td>\n",
              "      <td>292725.828734</td>\n",
              "      <td>1.004003e+09</td>\n",
              "      <td>1.004274e+09</td>\n",
              "      <td>1.004514e+09</td>\n",
              "      <td>1.004797e+09</td>\n",
              "      <td>1.004994e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>50.0</td>\n",
              "      <td>41.137240</td>\n",
              "      <td>0.604507</td>\n",
              "      <td>39.8833</td>\n",
              "      <td>40.707850</td>\n",
              "      <td>41.09430</td>\n",
              "      <td>41.562575</td>\n",
              "      <td>42.3582</td>\n",
              "      <td>50.0</td>\n",
              "      <td>19.950260</td>\n",
              "      <td>0.376904</td>\n",
              "      <td>19.4500</td>\n",
              "      <td>19.657475</td>\n",
              "      <td>19.88330</td>\n",
              "      <td>20.172200</td>\n",
              "      <td>20.9900</td>\n",
              "      <td>50.0</td>\n",
              "      <td>33835.380000</td>\n",
              "      <td>66517.629442</td>\n",
              "      <td>3607.0</td>\n",
              "      <td>6965.0</td>\n",
              "      <td>9832.5</td>\n",
              "      <td>24187.75</td>\n",
              "      <td>418495.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.008545e+09</td>\n",
              "      <td>254867.410503</td>\n",
              "      <td>1.008050e+09</td>\n",
              "      <td>1.008363e+09</td>\n",
              "      <td>1.008558e+09</td>\n",
              "      <td>1.008765e+09</td>\n",
              "      <td>1.008978e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>62.0</td>\n",
              "      <td>33.199642</td>\n",
              "      <td>4.342151</td>\n",
              "      <td>19.5686</td>\n",
              "      <td>31.764225</td>\n",
              "      <td>35.33210</td>\n",
              "      <td>36.266650</td>\n",
              "      <td>36.9000</td>\n",
              "      <td>62.0</td>\n",
              "      <td>3.345492</td>\n",
              "      <td>3.542263</td>\n",
              "      <td>-8.1478</td>\n",
              "      <td>0.341500</td>\n",
              "      <td>3.36140</td>\n",
              "      <td>6.063650</td>\n",
              "      <td>9.5500</td>\n",
              "      <td>57.0</td>\n",
              "      <td>189968.666667</td>\n",
              "      <td>455149.392092</td>\n",
              "      <td>216.0</td>\n",
              "      <td>41685.0</td>\n",
              "      <td>125480.0</td>\n",
              "      <td>173531.00</td>\n",
              "      <td>3415811.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>1.012499e+09</td>\n",
              "      <td>317397.741255</td>\n",
              "      <td>1.012000e+09</td>\n",
              "      <td>1.012210e+09</td>\n",
              "      <td>1.012479e+09</td>\n",
              "      <td>1.012780e+09</td>\n",
              "      <td>1.012981e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>American Samoa</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-14.274000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14.2740</td>\n",
              "      <td>-14.274000</td>\n",
              "      <td>-14.27400</td>\n",
              "      <td>-14.274000</td>\n",
              "      <td>-14.2740</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-170.704600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-170.7046</td>\n",
              "      <td>-170.704600</td>\n",
              "      <td>-170.70460</td>\n",
              "      <td>-170.704600</td>\n",
              "      <td>-170.7046</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12576.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12576.0</td>\n",
              "      <td>12576.0</td>\n",
              "      <td>12576.0</td>\n",
              "      <td>12576.00</td>\n",
              "      <td>12576.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.016977e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.016977e+09</td>\n",
              "      <td>1.016977e+09</td>\n",
              "      <td>1.016977e+09</td>\n",
              "      <td>1.016977e+09</td>\n",
              "      <td>1.016977e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>7.0</td>\n",
              "      <td>42.522429</td>\n",
              "      <td>0.035015</td>\n",
              "      <td>42.4664</td>\n",
              "      <td>42.504250</td>\n",
              "      <td>42.51670</td>\n",
              "      <td>42.549200</td>\n",
              "      <td>42.5670</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.534929</td>\n",
              "      <td>0.037359</td>\n",
              "      <td>1.4933</td>\n",
              "      <td>1.507400</td>\n",
              "      <td>1.53310</td>\n",
              "      <td>1.552600</td>\n",
              "      <td>1.5981</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22151.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22151.0</td>\n",
              "      <td>22151.0</td>\n",
              "      <td>22151.0</td>\n",
              "      <td>22151.00</td>\n",
              "      <td>22151.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.020562e+09</td>\n",
              "      <td>292444.070553</td>\n",
              "      <td>1.020009e+09</td>\n",
              "      <td>1.020480e+09</td>\n",
              "      <td>1.020594e+09</td>\n",
              "      <td>1.020742e+09</td>\n",
              "      <td>1.020886e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wallis And Futuna</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-14.293300</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14.2933</td>\n",
              "      <td>-14.293300</td>\n",
              "      <td>-14.29330</td>\n",
              "      <td>-14.293300</td>\n",
              "      <td>-14.2933</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-178.158300</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-178.1583</td>\n",
              "      <td>-178.158300</td>\n",
              "      <td>-178.15830</td>\n",
              "      <td>-178.158300</td>\n",
              "      <td>-178.1583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.876732e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.876732e+09</td>\n",
              "      <td>1.876732e+09</td>\n",
              "      <td>1.876732e+09</td>\n",
              "      <td>1.876732e+09</td>\n",
              "      <td>1.876732e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bank</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.776400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.7764</td>\n",
              "      <td>31.776400</td>\n",
              "      <td>31.77640</td>\n",
              "      <td>31.776400</td>\n",
              "      <td>31.7764</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.226900</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.2269</td>\n",
              "      <td>35.226900</td>\n",
              "      <td>35.22690</td>\n",
              "      <td>35.226900</td>\n",
              "      <td>35.2269</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.934000e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.934000e+09</td>\n",
              "      <td>1.934000e+09</td>\n",
              "      <td>1.934000e+09</td>\n",
              "      <td>1.934000e+09</td>\n",
              "      <td>1.934000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yemen</th>\n",
              "      <td>42.0</td>\n",
              "      <td>14.599500</td>\n",
              "      <td>1.055239</td>\n",
              "      <td>12.6519</td>\n",
              "      <td>13.929200</td>\n",
              "      <td>14.55000</td>\n",
              "      <td>15.404600</td>\n",
              "      <td>16.9358</td>\n",
              "      <td>42.0</td>\n",
              "      <td>45.557967</td>\n",
              "      <td>2.674193</td>\n",
              "      <td>42.9511</td>\n",
              "      <td>43.967975</td>\n",
              "      <td>44.38975</td>\n",
              "      <td>45.525575</td>\n",
              "      <td>54.0239</td>\n",
              "      <td>39.0</td>\n",
              "      <td>171140.153846</td>\n",
              "      <td>480127.964823</td>\n",
              "      <td>189.0</td>\n",
              "      <td>16704.0</td>\n",
              "      <td>41652.0</td>\n",
              "      <td>105400.00</td>\n",
              "      <td>2957000.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.887512e+09</td>\n",
              "      <td>312838.636977</td>\n",
              "      <td>1.887000e+09</td>\n",
              "      <td>1.887197e+09</td>\n",
              "      <td>1.887536e+09</td>\n",
              "      <td>1.887794e+09</td>\n",
              "      <td>1.887985e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zambia</th>\n",
              "      <td>35.0</td>\n",
              "      <td>-13.342017</td>\n",
              "      <td>2.343323</td>\n",
              "      <td>-17.8500</td>\n",
              "      <td>-14.984600</td>\n",
              "      <td>-13.45960</td>\n",
              "      <td>-12.006650</td>\n",
              "      <td>-8.8400</td>\n",
              "      <td>35.0</td>\n",
              "      <td>27.707497</td>\n",
              "      <td>2.865051</td>\n",
              "      <td>22.6800</td>\n",
              "      <td>25.843350</td>\n",
              "      <td>28.21190</td>\n",
              "      <td>28.808850</td>\n",
              "      <td>33.1700</td>\n",
              "      <td>35.0</td>\n",
              "      <td>135563.885714</td>\n",
              "      <td>304404.596300</td>\n",
              "      <td>1336.0</td>\n",
              "      <td>14113.5</td>\n",
              "      <td>47554.0</td>\n",
              "      <td>125760.50</td>\n",
              "      <td>1742979.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1.894531e+09</td>\n",
              "      <td>312835.383734</td>\n",
              "      <td>1.894033e+09</td>\n",
              "      <td>1.894251e+09</td>\n",
              "      <td>1.894635e+09</td>\n",
              "      <td>1.894786e+09</td>\n",
              "      <td>1.894966e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>26.0</td>\n",
              "      <td>-18.807485</td>\n",
              "      <td>1.439256</td>\n",
              "      <td>-22.2167</td>\n",
              "      <td>-19.920300</td>\n",
              "      <td>-18.64070</td>\n",
              "      <td>-17.844325</td>\n",
              "      <td>-16.5333</td>\n",
              "      <td>26.0</td>\n",
              "      <td>29.947850</td>\n",
              "      <td>1.633048</td>\n",
              "      <td>25.8333</td>\n",
              "      <td>29.188750</td>\n",
              "      <td>30.02500</td>\n",
              "      <td>31.088050</td>\n",
              "      <td>32.6694</td>\n",
              "      <td>25.0</td>\n",
              "      <td>146464.080000</td>\n",
              "      <td>334123.358243</td>\n",
              "      <td>2148.0</td>\n",
              "      <td>24210.0</td>\n",
              "      <td>41767.0</td>\n",
              "      <td>79174.00</td>\n",
              "      <td>1606000.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.716479e+09</td>\n",
              "      <td>278618.775549</td>\n",
              "      <td>1.716033e+09</td>\n",
              "      <td>1.716240e+09</td>\n",
              "      <td>1.716506e+09</td>\n",
              "      <td>1.716627e+09</td>\n",
              "      <td>1.716967e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>224 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    lat             ...            id              \n",
              "                  count       mean  ...           75%           max\n",
              "country                             ...                            \n",
              "Afghanistan        44.0  34.755439  ...  1.004797e+09  1.004994e+09\n",
              "Albania            50.0  41.137240  ...  1.008765e+09  1.008978e+09\n",
              "Algeria            62.0  33.199642  ...  1.012780e+09  1.012981e+09\n",
              "American Samoa      1.0 -14.274000  ...  1.016977e+09  1.016977e+09\n",
              "Andorra             7.0  42.522429  ...  1.020742e+09  1.020886e+09\n",
              "...                 ...        ...  ...           ...           ...\n",
              "Wallis And Futuna   1.0 -14.293300  ...  1.876732e+09  1.876732e+09\n",
              "West Bank           1.0  31.776400  ...  1.934000e+09  1.934000e+09\n",
              "Yemen              42.0  14.599500  ...  1.887794e+09  1.887985e+09\n",
              "Zambia             35.0 -13.342017  ...  1.894786e+09  1.894966e+09\n",
              "Zimbabwe           26.0 -18.807485  ...  1.716627e+09  1.716967e+09\n",
              "\n",
              "[224 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWu6xH7mdLL8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}