459 lines
22 KiB
Plaintext
459 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Python: Drugie zadanie domowe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Drugie zadanie domowe będzie polegać na opracowaniu danych zawartych w pliku `gapminder.csv`, który znajduje się w tym katalogu (proszę wykorzystać ten plik, a nie ten w katalogu labs04). Ten arkusz poprowadzi Cię krok po kroku po zadaniu domowym."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 1** Wczytaj dane do zmiennej `data`, w taki sposób, żeby nazwa Państwa była kluczem."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv(\"gapminder.csv\", index_col=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 2** Znajdź najbardziej i najmniej zaludnione państwa na świecie."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 3** W ilu państwach współczynnik `female_BMI` jest większy od `male_BMI`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 4**\n",
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" 1. Zainstaluj bibliotekę `pycountry_convert` i zaimportuj ją.\n",
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" 1. Dodaj do danych kolumnę `continent`, która będzie zawierać nazwę kontynentu, na którym jest położone dane państwo. Wykorzystaj bibliotekę `pycountry_convert`. *Uwaga*: trzeba najpierw uzystać kod państwa w fomacie ISO-2, następnie uzystkać kod kontynentu, a na końcu uzyskać nazwę kontynentu."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 92,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Afghanistan Asia\n",
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"Albania Europe\n",
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"Algeria Africa\n",
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"Angola Africa\n",
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"Antigua and Barbuda North America\n",
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"Argentina South America\n",
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"Armenia Asia\n",
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"Australia Oceania\n",
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"Austria Europe\n",
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"Azerbaijan Asia\n",
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"Bahamas North America\n",
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"Bahrain Asia\n",
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"Bangladesh Asia\n",
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"Barbados North America\n",
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"Belarus Europe\n",
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"Belgium Europe\n",
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"Belize North America\n",
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"Benin Africa\n",
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"Bhutan Asia\n",
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"Bolivia South America\n",
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"Bosnia and Herzegovina Europe\n",
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"Botswana Africa\n",
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"Brazil South America\n",
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"Brunei Asia\n",
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"Bulgaria Europe\n",
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"Burkina Faso Africa\n",
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"Burundi Africa\n",
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"Cambodia Asia\n",
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"Cameroon Africa\n",
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"Canada North America\n",
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" ... \n",
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"Spain Europe\n",
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"Sri Lanka Asia\n",
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"Sudan Africa\n",
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"Suriname South America\n",
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"Swaziland Africa\n",
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"Sweden Europe\n",
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"Switzerland Europe\n",
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"Syria Asia\n",
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"Tajikistan Asia\n",
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"Tanzania Africa\n",
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"Thailand Asia\n",
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"Togo Africa\n",
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"Tonga Oceania\n",
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"Trinidad and Tobago North America\n",
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"Tunisia Africa\n",
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"Turkey Asia\n",
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"Turkmenistan Asia\n",
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"Uganda Africa\n",
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"Ukraine Europe\n",
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"United Arab Emirates Asia\n",
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"United Kingdom Europe\n",
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"United States North America\n",
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"Uruguay South America\n",
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"Uzbekistan Asia\n",
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"Vanuatu Oceania\n",
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"Venezuela South America\n",
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"Vietnam Asia\n",
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"Palestine Asia\n",
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"Zambia Africa\n",
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"Zimbabwe Africa\n",
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"Name: continent, Length: 175, dtype: object"
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]
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},
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"execution_count": 92,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conts = []\n",
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"for name in data.index:\n",
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" country_code = pycountry_convert.convert_countries.country_name_to_country_alpha2(name)\n",
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" cont_code = pycountry_convert.convert_country_alpha2_to_continent_code.country_alpha2_to_continent_code(country_code)\n",
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" cont = pycountry_convert.convert_continent_code_to_continent_name(cont_code)\n",
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" conts.append(cont)\n",
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" \n",
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"data['continent'] = conts\n",
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"data['continent']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 5**\n",
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"Oblicz ile osób mieszka na każdym z kontynentów."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 103,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>female_BMI</th>\n",
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" <th>male_BMI</th>\n",
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" <th>gdp</th>\n",
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" <th>population</th>\n",
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" <th>under5mortality</th>\n",
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" <th>life_expectancy</th>\n",
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" <th>fertility</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>continent</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Africa</th>\n",
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" <td>1259.33213</td>\n",
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" <td>1181.44512</td>\n",
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" <td>280335.0</td>\n",
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" <td>9.772803e+08</td>\n",
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" <td>4763.00</td>\n",
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" <td>3136.10</td>\n",
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" <td>246.50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Asia</th>\n",
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" <td>1114.28765</td>\n",
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" <td>1084.74598</td>\n",
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" <td>1038232.0</td>\n",
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" <td>3.949400e+09</td>\n",
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" <td>1302.98</td>\n",
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" <td>3201.41</td>\n",
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" <td>108.48</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Europe</th>\n",
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" <td>980.30978</td>\n",
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" <td>1009.60877</td>\n",
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" <td>1173410.0</td>\n",
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" <td>7.219954e+08</td>\n",
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" <td>273.60</td>\n",
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" <td>2929.30</td>\n",
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" <td>59.56</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>North America</th>\n",
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" <td>526.06040</td>\n",
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" <td>497.74034</td>\n",
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" <td>338430.0</td>\n",
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" <td>5.235798e+08</td>\n",
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" <td>392.38</td>\n",
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" <td>1412.20</td>\n",
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" <td>45.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Oceania</th>\n",
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" <td>297.20093</td>\n",
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" <td>279.58957</td>\n",
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" <td>102803.0</td>\n",
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" <td>3.425711e+07</td>\n",
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" <td>310.00</td>\n",
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" <td>683.20</td>\n",
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" <td>34.02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>South America</th>\n",
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" <td>322.37322</td>\n",
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" <td>308.59791</td>\n",
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" <td>139888.0</td>\n",
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" <td>3.881582e+08</td>\n",
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" <td>288.70</td>\n",
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" <td>886.80</td>\n",
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" <td>30.10</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" female_BMI male_BMI gdp population \\\n",
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"continent \n",
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"Africa 1259.33213 1181.44512 280335.0 9.772803e+08 \n",
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"Asia 1114.28765 1084.74598 1038232.0 3.949400e+09 \n",
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"Europe 980.30978 1009.60877 1173410.0 7.219954e+08 \n",
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"North America 526.06040 497.74034 338430.0 5.235798e+08 \n",
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"Oceania 297.20093 279.58957 102803.0 3.425711e+07 \n",
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"South America 322.37322 308.59791 139888.0 3.881582e+08 \n",
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"\n",
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" under5mortality life_expectancy fertility \n",
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"continent \n",
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"Africa 4763.00 3136.10 246.50 \n",
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"Asia 1302.98 3201.41 108.48 \n",
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"Europe 273.60 2929.30 59.56 \n",
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"North America 392.38 1412.20 45.00 \n",
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"Oceania 310.00 683.20 34.02 \n",
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"South America 288.70 886.80 30.10 "
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]
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},
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"execution_count": 103,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.groupby('continent').sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 6** Narysyj wykres słupkowy pokazujący ile państw leży na każdym z kontynentów."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 102,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f7cceba78d0>"
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]
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},
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"execution_count": 102,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"data.continent.value_counts().plot('bar')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 7** \n",
|
|
" * Kolumna `gdp` zawiera informacje o PKB na obywatela. Stwórz nową kolumnę `gdp_total`, która będzie informować o PKB danego kraju.\n",
|
|
" * Oblicz ile wynosi suma światowego PKB (kolumna `gdp_total`).\n",
|
|
" * Oblicz ile krajów jest odpowiedzialnych za wytworzenie 80% światego PKB."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 8** Wyświetl wszystkie europejskie państwa, w których oczekiwana długość życia wynosi conajmniej 80 lat. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 9** Znajdź państwo, które ma najbardziej zbliżone PKB do Polski. Spróbuj rozwiązać to zadanie w jednej linijce."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 10 (ostatnie)**\n",
|
|
" * Zobacz czy masz zainstalowaną bibliotekę `requests`, która sluży do wykonywania zapytań HTTP. Jeżeli nie, to zainstaluj ją. \n",
|
|
" * Uruchom z funkcję `requests.get` podając jako argument link: `https://aws.random.cat/meow`. Wynik zapisz do zmiennej response.\n",
|
|
" * Wykonaj metodę `json()` na zmiennej `response` która zwróci Ci słownik, w którym będzie klucz `file`. Zapisz wartość `file` do zmiennej `url`.\n",
|
|
" * Zaimportuj `Image` i `display` z biblioteki `from IPython.core.display`.\n",
|
|
" * Stwórz obiekt Image podając w konstruktorze zmienną `url`,a wynik zapisz do `image`.\n",
|
|
" * Wykonaj funkcję `display` na obiekcie `image`.\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": "3.7.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|