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andrewhnberryandrewhnberry
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update 3d-exercise
Improved Readability
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‎.ipynb_checkpoints/maps_exercise-checkpoint.ipynb

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{
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"cells": [
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:44:33.442796Z",
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"start_time": "2020-04-23T18:44:32.388132Z"
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}
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},
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"outputs": [],
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"source": [
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"# import geopandas and matplotlib\n",
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"import geopandas as gpd\n",
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"import matplotlib.pyplot as plt"
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:44:33.532177Z",
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"start_time": "2020-04-23T18:44:33.495683Z"
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}
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},
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"outputs": [],
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"source": [
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"# countries data\n",
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"world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
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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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"**Print the top 5 rows of world dataframe.**"
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:42:59.340723Z",
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"start_time": "2020-04-23T18:42:59.319452Z"
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}
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},
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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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"**Remove continents which have only one country in dataframe.**\n"
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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": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-23T18:44:33.661829Z",
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"start_time": "2020-04-23T18:44:33.658772Z"
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}
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},
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"outputs": [],
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"source": [
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"# continents with only one country\n"
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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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"# remove these continents"
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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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"**Create dataframe with top 3 most populated countries from each continent and store the result in dataframe with name 'world_filtered'.**\n"
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:42:59.643052Z",
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"start_time": "2020-04-23T18:42:59.609214Z"
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}
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},
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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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"**Print the 'world_filtered' dataframe.**"
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:43:00.041229Z",
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"start_time": "2020-04-23T18:43:00.004937Z"
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}
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},
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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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"-----------------\n",
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"## Part I: Create a choropleth map of world:\n",
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"\n",
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" - First using **Geopandas**\n",
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" - Note: Geopandas may not work well on your computers, especially on Windows computers, it is advised to use Google Collab for this assignment. \n",
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" - Second using **Plotly**\n",
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" - Note: Plotly may not work well on Jupyter Labs, it is advised to use Jupyter Notebooks or Google Collab for the plotly portion. \n",
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"\n",
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"\n",
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"> #### Notes\n",
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"> * the colors of countries from 'world_filtered' are based on population\n",
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"> * other countries can stay white"
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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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"### GeoPandas"
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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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"# create figure and axes\n",
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"\n",
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"\n",
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"\n",
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"# create map from world_filtered data-frame\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# add the rest of the countries"
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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": "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": "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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"### Plotly"
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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": "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": "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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"-----------\n",
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"## Part II: More Geopandas\n",
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"\n",
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"In Geopandas:\n",
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"\n",
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"* add New York, Berlin, Paris, Toronto, Calgary, Tokyo to the map you have created before\n",
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" * to get geometry of these cities use the function, **gpd.tools.geocode**(['New York', 'Berlin', 'Paris','Toronto', 'Calgary', 'Tokyo'])\n",
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" * if you do not have the geopy library instaled, install it with the following command **in the jupyter notebook cell** `!pip install geopy` "
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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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"ExecuteTime": {
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"end_time": "2020-04-23T18:44:34.757007Z",
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"start_time": "2020-04-23T18:44:34.752365Z"
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}
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},
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"outputs": [],
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"source": [
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"# get geometry\n",
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"\n",
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"\n",
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"# create figure and axes\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# create map from world_filtered data-frame\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# add the rest of the countries\n",
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"\n",
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"\n",
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"\n",
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"# add cities\n"
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]
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},
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{
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}
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],
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"version": "3.7.9"
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