gbarreiro

cat verylongfile.txt | more # shows the content of verylongfile.txt split in pages
cat verylongfile.txt | less # shows the content of verylongfile.txt split in pages, with support for scrolling and backwards navigation
cat verylongfile.txt | grep "I love you" # shows the lines where the string "I love you" is found

gbarreiro

help nano # brief summary about the command "nano"
man nano # detailed info about the command "nano"

gbarreiro

# Calculate the average percentage of restaurants of each cuisine for the 9 analyzed cities
famous_cuisines = cuisines_city_normalized.mean(axis=0)

# Get the top 10 cuisines around the world
famous_cuisines = famous_cuisines.sort_values(ascending=True)
famous_cuisines = famous_cuisines.tail(10)

# Plot the data in a horizontal bar graph
famous_cuisines.plot(kind='barh')
plt.xlabel('Average % of restaurants in the cities')

gbarreiro

percentages = {}
for city, cuisine in city_local_cuisines.items():
    # For each city, get the percentage of local cuisine restaurants
    percentage = cuisines_city_normalized.at[city, cuisine]
    percentages[city] = percentage

# Plot the data in a bar graph
df_percentages = pd.Series(percentages).sort_values(ascending=True)
df_percentages.plot(kind='barh')
plt.xlabel('% of local cuisine restaurants')

gbarreiro

fig = plt.figure()
for index, cuisine in enumerate(city_local_cuisines.values()):
    # For the selected cuisine, plot a bar graph with the cities where there are more restaurants of that cuisine
    ax = fig.add_subplot(3, 3, index+1)
    cuisine_cities = cuisines_city_normalized[cuisine]
    cuisine_cities = cuisine_cities.sort_values(ascending=True).head(9)

    # Plot in red the bar with the city where this cuisine is local, plot in blue the rest
    color = ''
    for i in cuisine_cities.keys():

gbarreiro

fig = plt.figure()
for index, city in enumerate(cities):
    # For each city, get the top 10 cuisines and plot them in a horizontal bar graph
    ax = fig.add_subplot(3, 3, index+1)

    # Get the top 10 cuisines for the city
    cuisines = cuisines_city_normalized.loc[city]
    cuisines = cuisines.sort_values(ascending=False).head(10).sort_values(ascending=True)
    
    # Plot in red the bar with the local cuisine, plot in blue the rest

gbarreiro

# One-hot encoding
restaurants_onehot = pd.get_dummies(restaurants['Cuisine'], prefix="", prefix_sep="")
restaurants_onehot = restaurants_onehot.multiply(restaurants['Number of restaurants'], axis=0)
restaurants_onehot = pd.concat([restaurants['City'], restaurants_onehot], axis=1)

# Group by city
cuisines_city = restaurants_onehot.groupby('City').sum().reset_index()
cuisines_city = cuisines_city.set_index(keys='City')

# Normalize the numbers

gbarreiro

restaurants = pd.DataFrame(columns=['City', 'Cuisine', 'Number of restaurants']) # define the DataFrame where the results will be stored

for city in cities:
    for cuisine_name, category_id in cuisines.items():
        # For each city, retrieve the top restaurants from Foursquare for each cuisine...
        query = requests.get("{url}&near={city}&categoryId={category_id}".format(url=URL, city=city, category_id=category_id))
        if query.status_code == 200:
            number = query.json()
            restaurants = restaurants.append({
                'City': city,

gbarreiro

# Define the dependencies
import pandas as pd
import requests
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
mpl.style.use(['seaborn'])

# Foursquare API credentials and endpoint
CLIENT_ID = # your client ID

gbarreiro

version: "3.8"
services:
    database:
        image: mysql:latest
        volumes:
            - db-data:/var/lib/mysql/data
        command: --default-authentication-plugin=mysql_native_password # run this command in the container
        networks:
            - database-api
        restart: always # the container must be always on