459 lines
22 KiB
Plaintext
459 lines
22 KiB
Plaintext
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{
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"cells": [
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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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|
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"image/png": "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
|
||
|
|
"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
|
||
|
|
}
|