Chapter 6 problem set 2 - UCD-pbio-rclub/python_problems GitHub Wiki

Ch. 6

We are going to being looking at NFL player stats for the 2018 season

1. Using the information provided here create the proper url to use with requests.get(url) to get the overall stats for each player for the season.

url = 'http://api.fantasy.nfl.com/v1/players/stats'
resp = requests.get(url)
resp

2. Process the JSON of the response and create a pandas data frame with columns for each player's name, position, team, and season points

data = resp.json()
data2 = data['players']
data3 = pd.DataFrame(data2, columns=['name', 'position','teamAbbr', 'seasonPts'])

3. Find the top five quarterbacks this season based on points

data3[data3['position']=='QB'].sort_values(by=['seasonPts'], ascending=False).head()

This is an API that might be cool to to use also in the next chapter when we use regular expressions. It can search for lyrics, using the artist/band and song

The url is https://api.lyrics.ovh/v1/

First, we define the parameters of the search (band AND song)

artist = "queen" #try to use full names
song = "bohemian rhapsody" #with spaces
# This is how the url would look:
url = 'https://api.lyrics.ovh/v1/' #an API that searches lyrics
url= url + artist.lower() + "/" + song.lower()

1. How would you interact with the API in python to get the lyrics?

import requests
request = requests.get(url)
request #response should be 200
response = request.json()

2. What is returned?

print (response)

type(resp)

4. How can you unpack the lyrics into a string object?

lyrics = list(response.values())[0]
print (lyrics)

There is a RESTful API to query NWS weather forecasts.

I am going to make your life somewhat easier by providing you with a URL that you can use: https://api.weather.gov/points/38.5449%2C-121.7405/forecast will get you the forecast for Davis.

In python get the data returned by that url and parse it to give you a data frame with the name of the day, temperature, wind speed and direction, and the detailed forecast.

import requests

import pandas as pd

url = "https://api.weather.gov/points/38.5449%2C-121.7405/forecast"

resp = requests.get(url)

data = resp.json()

weather = pd.DataFrame(data['properties']['periods'], columns=['name', 'temperature', 'windSpeed', 'windDirection', 'detailedForecast'])

Here is the link: https://www.worlddancesport.org/Athlete/List#divisionFilter=General&divisionFilter=Professional&genderFilter=Male&genderFilter=Female&statusFilter=Active&countryFilter=-1&ageGroupFilter=180&ageGroupFilter=179&ageGroupFilter=177&ageGroupFilter=178&ageGroupFilter=175&ageGroupFilter=176&ageGroupFilter=197&ageGroupFilter=198&ageGroupFilter=199&ageGroupFilter=200&ageGroupFilter=201&ageGroupFilter=202&ageGroupFilter=203&ageGroupFilter=204&profileFilter=false&formAction=&Column=AgeGroup&Direction=Ascending&page=1&pageSize=25

# Download the excel file from the website.

xlsx = pd.ExcelFile('HW6/WDSF Athletes on 2019-01-20.xlsx')
Athletes = pd.read_excel(xlsx, 'Couples')
Athletes.head()

Athletes_Representing = Athletes['Representing']
Athletes_Representing.value_counts()

Top1Athletes = Athletes.Representing.value_counts().head(1).index[0]
Top1Athletes
RussiaAthletes = Athletes[Athletes.Representing == Top1Athletes]
RussiaAthletes
RussiaAthletes.to_excel('HW6/RussiaAthletes.xlsx')
RAthletes_Age = RussiaAthletes['Age group']
RAthletes_Age.value_counts()

I used the stat from Golden State Warriors (https://www.nba.com/warriors/stats).

1. Get the players' stat. (This site contains two tables, so pick up one of the tables to work with).

data = pd.read_html('https://www.nba.com/warriors/stats', header=0)
table1 = data[0]
# I tried to use requests.get(), but got an error message. 

2. Convert the extracted data to binary format.

dat2 = dat1.to_pickle('dat2_pickle')
pd.read_pickle('dat2_pickle') #just for confirmation 

From the 1st 250 inputs of the UC Davis page on thegradcafe (https://www.thegradcafe.com/survey/index.php?q=uc+davis&t=a&o=&pp=250)

1. Get the DataFrame

frame = pd.read_html('https://www.thegradcafe.com/survey/index.php?q=uc+davis&t=a&o=&pp=250', header=0)[0]
print(type(frame))

2. Find out the most frequent program that people reported in this page.

program = frame['Program (Season)'].value_counts().head(1)
print(program) 

3. Try the whole process on all the inputs in the database. (Hint: return all the DataFrame into a list and then use pd.concat(dataframe_list). Be careful this method will fuse the DataFrames with their headers. Think how to solve this.)

I want to bake a cake for my friend's birthday. I found this API with lots of recipes, and I want to find a recipe for a cake with nutella in it. However, my friend is allergic to eggs, so I can't use any recipes that include eggs.

Please find recipes (and their URLs) of cakes that won't make my friend sick!

url = "http://www.recipepuppy.com/api/"

parameters = {"i": "nutella", "q" : "cake"}

resp = requests.get(url, params=parameters)

data = resp.json()

searchreturn = data['results']

for all in searchreturn:
    if 'egg' not in all['ingredients']:
        print(all['title'] + " : " + all['href'])