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Data Collection From Web APIs

All Contributors

A curated list of example code to collect data from Web APIs using DataPrep.Connector.

How to Contribute?

You can contribute to this project in two ways. Please check the contributing guide.

  1. Add your example code on this page
  2. Add a new configuration file to this repo

Why Contribute?

Index

Business

Yelp -- Collect Local Business Data

What's the phone number of Capilano Suspension Bridge Park?
from dataprep.connector import connect

# You can get ”yelp_access_token“ by following https://www.yelp.com/developers/documentation/v3/authentication
conn_yelp = connect("yelp", _auth={"access_token":yelp_access_token}, _concurrency = 5)

df = await conn_yelp.query("businesses", term = "Capilano Suspension Bridge Park", location = "Vancouver", _count = 1)

df[["name","phone"]]
id name phone
0 Capilano Suspension Bridge Park +1 604-985-7474
Which yoga store has the highest review count in Vancouver?
from dataprep.connector import connect

# You can get ”yelp_access_token“ by following https://www.yelp.com/developers/documentation/v3/authentication
conn_yelp = connect("yelp", _auth={"access_token":yelp_access_token}, _concurrency = 1)

  # Check all supported categories: https://www.yelp.ca/developers/documentation/v3/all_category_list
df = await conn_yelp.query("businesses", categories = "yoga", location = "Vancouver", sort_by = "review_count", _count = 1)
df[["name", "review_count"]]
id name review_count
0 YYOGA Downtown Flow 107
How many Starbucks stores in Seattle and where are they?
from dataprep.connector import connect

# You can get ”yelp_access_token“ by following https://www.yelp.com/developers/documentation/v3/authentication
conn_yelp = connect("yelp", _auth={"access_token":yelp_access_token}, _concurrency = 5)
df = await conn_yelp.query("businesses", term = "Starbucks", location = "Seattle", _count = 1000)

# Remove irrelevant data
df = df[(df['city'] == 'Seattle') & (df['name'] == 'Starbucks')]
df[['name', 'address1', 'city', 'state', 'country', 'zip_code']].reset_index(drop=True)
id name address1 city state country zip_code
0 Starbucks 515 Westlake Ave N Seattle WA US 98109
1 Starbucks 442 Terry Avenue N Seattle WA US 98109
... ....... ....... ...... .. .. ....
126 Starbucks 17801 International Blvd Seattle WA US 98158
What are the ratings for a list of resturants?
from dataprep.connector import connect
import pandas as pd
import asyncio
# You can get ”yelp_access_token“ by following https://www.yelp.com/developers/documentation/v3/authentication
conn_yelp = connect("yelp", _auth={"access_token":yelp_access_token}, _concurrency = 5)

names = ["Miku", "Boulevard", "NOTCH 8", "Chambar", "VIJ’S", "Fable", "Kirin Restaurant", "Cafe Medina", \
 "Ask for Luigi", "Savio Volpe", "Nicli Pizzeria", "Annalena", "Edible Canada", "Nuba", "The Acorn", \
 "Lee's Donuts", "Le Crocodile", "Cioppinos", "Six Acres", "St. Lawrence", "Hokkaido Santouka Ramen"]

query_list = [conn_yelp.query("businesses", term=name, location = "Vancouver", _count=1) for name in names]
results = asyncio.gather(*query_list)
df = pd.concat(await results)
df[["name", "rating", "city"]].reset_index(drop=True)
ID Name Rating City
0 Miku 4.5 Vancouver
1 Boulevard Kitchen & Oyster Bar 4.0 Vancouver
... ... ... ...
20 Hokkaido Ramen Santouka 4.0 Vancouver

Hunter -- Collect and Verify Professional Email Addresses

Who are executives of Asana and what are their emails?
from dataprep.connector import connect

# You can get ”hunter_access_token“ by registering as a developer https://hunter.io/users/sign_up
conn_hunter = connect("hunter", _auth={"access_token":'hunter_access_token'})

df = await conn_hunter.query('all_emails', domain='asana.com', _count=10)

df[df['department']=='executive']
first_name last_name email position department
0 Dustin Moskovitz dustin@asana.com Cofounder executive
1 Stephanie Heß shess@asana.com CEO executive
2 Erin Cheng erincheng@asana.com Strategic Initiatives executive
What is Dustin Moskovitz's email?
from dataprep.connector import connect

# You can get ”hunter_access_token“ by registering as a developer https://hunter.io/users/sign_up
conn_hunter = connect("hunter", _auth={"access_token":'hunter_access_token'})

df = await conn_hunter.query("individual_email", full_name='dustin moskovitz', domain='asana.com')

df
first_name last_name email position
0 Dustin Moskovitz dustin@asana.com Cofounder
Are the emails of Asana executives valid?
from dataprep.connector import connect

# You can get ”hunter_access_token“ by registering as a developer https://hunter.io/users/sign_up
conn_hunter = connect("hunter", _auth={"access_token":'hunter_access_token'})

employees = await conn_hunter.query("all_emails", domain='asana.com', _count=10)
executives = employees.loc[employees['department']=='executive']
emails = executives[['email']]

for email in emails.iterrows():
status = await conn_hunter.query("email_verifier", email=email[1][0])
emails['status'] = status

emails
email status
0 dustin@asana.com valid
3 shess@asana.com NaN
4 erincheng@asana.com NaN
How many available requests do I have left?
from dataprep.connector import connect

# You can get ”hunter_access_token“ by registering as a developer https://hunter.io/users/sign_up
conn_hunter = connect("hunter", _auth={"access_token":'hunter_access_token'})

df = await conn_hunter.query("account")
df
requests available
0 19475
What are the counts of each level of seniority of Intercom employees?
from dataprep.connector import connect

# You can get ”hunter_access_token“ by registering as a developer https://hunter.io/users/sign_up
conn_hunter = connect("hunter", _auth={"access_token":'hunter_access_token'})

df = await conn_hunter.query("email_count", domain='intercom.io')
df.drop('total', axis=1)
junior senior executive
0 0 2 2

Finance

Finnhub -- Collect Financial, Market, Economic Data

How to get a list of cryptocurrencies and their exchanges
import pandas as pd
from dataprep.connector import connect

# You can get ”finnhub_access_token“ by following https://finnhub.io/
conn_finnhub = connect("finnhub", _auth={"access_token":finnhub_access_token}, update=True)

df = await conn_finnhub.query('crypto_exchange')
exchanges = df['exchange'].to_list()
symbols = []
for ex in exchanges:
    data = await df.query('crypto_symbols', exchange=ex)
    symbols.append(data)
df_symbols = pd.concat(symbols)
df_symbols
id description displaySymbol symbol
0 Binance FRONT/ETH FRONT/ETH BINANCE:FRONTETH
1 Binance ATOM/BUSD ATOM/BUSD BINANCE:ATOMBUSD
... ... ... ...
281 Poloniex AKRO/BTC AKRO/BTC POLONIEX:BTC_AKRO
Which ipo in the current month has the highest total share values?
import calendar
from datetime import datetime
from dataprep.connector import connect

# You can get ”finnhub_access_token“ by following https://finnhub.io/
conn_finnhub = connect("finnhub", _auth={"access_token":finnhub_access_token}, update=True)

today = datetime.today()
days_in_month = calendar.monthrange(today.year, today.month)[1]
date_from = today.replace(day=1).strftime('%Y-%m-%d')
date_to = today.replace(day=days_in_month).strftime('%Y-%m-%d')
ipo_df = await conn_finnhub.query('ipo_calender', from_=date_from, to=date_to)
ipo_df[ipo_df['totalSharesValue'] == ipo_df['totalSharesValue'].max()]
id date exchange name numberOfShares ... totalSharesValue
5 2021-02-03 NYSE TELUS International (Cda) Inc. 33333333 ... 9.58333e+08
What are the average acutal earnings from the last 4 seasons of a list of 10 popular stocks?
import asyncio
import pandas as pd
from dataprep.connector import connect

# You can get ”finnhub_access_token“ by following https://finnhub.io/
conn_finnhub = connect("finnhub", _auth={"access_token":finnhub_access_token}, update=True)

stock_list = ['TSLA', 'AAPL', 'WMT', 'GOOGL', 'FB', 'MSFT', 'COST', 'NVDA', 'JPM', 'AMZN']
query_list = [conn_finnhub.query('earnings', symbol=symbol) for symbol in stock_list]
query_results = asyncio.gather(*query_list)
stocks_df = pd.concat(await query_results)
stocks_df = stocks_df.groupby('symbol', as_index=False).agg({'actual': ['mean']})
stocks_df.columns = stocks_df.columns.get_level_values(0)
stocks_df = stocks_df.sort_values(by='actual', ascending=False).rename(columns={'actual': 'avg_actual'})
stocks_df.reset_index(drop=True)
id symbol avg_actual
0 GOOGL 12.9375
1 AMZN 8.5375
2 FB 2.4475
.. ... ...
9 TSLA 0.556
What is the earnings of last 4 quarters of a given company? (e.g. TSLA)
from dataprep.connector import connect
from datetime import datetime, timedelta, timezone

# You can get ”finnhub_access_token“ by following https://finnhub.io/
conn_finnhub = connect("finnhub", _auth={"access_token":finnhub_access_token}, update=True)

today = datetime.now(tz=timezone.utc)
oneyear = today - timedelta(days = 365)
start = int(round(oneyear.timestamp()))

result = await conn_finnhub.query('earnings_calender', symbol='TSLA', from_=start, to=today)
result = result.set_index('date')
result
id date epsActual epsEstimate hour quarter ... symbol year
0 2021-01-27 0.8 1.37675 amc 4 ... TSLA 2020
1 2020-10-21 0.76 0.600301 amc 3 ... TSLA 2020
2 2020-07-22 0.436 -0.0267036 amc 2 ... TSLA 2020
.. ... ... ... ... ... ... ... ...
3 2011-02-15 -0.094 -0.101592 amc 4 ... TSLA 2010

Geocoding

MapQuest -- Collect Driving Directions, Maps, Traffic Data

Where is the Simon Fraser University? Give all the places if there is more than one campus.
from dataprep.connector import connect

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)

BC_BBOX = "-139.06,48.30,-114.03,60.00"
campus = await conn_map.query("place", q = "Simon Fraser University", sort = "relevance", bbox = BC_BBOX, _count = 50)
campus = campus[campus["name"] == "Simon Fraser University"].reset_index()
id index name country state city address postalCode coordinates details
0 0 Simon Fraser University CA BC Burnaby 8888 University Drive E V5A 1S6 [-122.90416, 49.27647] ...
1 2 Simon Fraser University CA BC Vancouver 602 Hastings St W V6B 1P2 [-123.113431, 49.284626] ...
How many KFC are there in Burnaby? What are their address?
from dataprep.connector import connect

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)

BC_BBOX = "-139.06,48.30,-114.03,60.00"
kfc = await conn_map.query("place", q = "KFC", sort = "relevance", bbox = BC_BBOX, _count = 500)
kfc = kfc[(kfc["name"] == "KFC") & (kfc["city"] == "Burnaby")].reset_index()
print("There are %d KFCs in Burnaby" % len(kfc))
print("Their addresses are:")
kfc['address']

There are 1 KFCs in Burnaby

Their addresses are:

id address
0 5094 Kingsway
The ratio of Starbucks to Tim Hortons in Vancouver?
from dataprep.connector import connect

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)
VAN_BBOX = '-123.27,49.195,-123.020,49.315'
starbucks = await conn_map.query('place', q='starbucks', sort='relevance', bbox=VAN_BBOX, page='1', pageSize = '50', _count=200)
timmys = await conn_map.query('place', q='Tim Hortons', sort='relevance', bbox=VAN_BBOX, page='1', pageSize = '50', _count=200)

is_vancouver_sb = starbucks['city'] == 'Vancouver'
is_vancouver_tim = timmys['city'] == 'Vancouver'
sb_in_van = starbucks[is_vancouver_sb]
tim_in_van = timmys[is_vancouver_tim]
print('The ratio of Starbucks:Tim Hortons in Vancouver is %d:%d' % (len(sb_in_van), len(tim_in_van)))

The ratio of Starbucks:Tim Hortons in Vancouver is 188:120

What is the closest gas station from Metropolist and how far is it?
from dataprep.connector import connect
from numpy import radians, sin, cos, arctan2, sqrt

def distance_in_km(cord1, cord2):
    R = 6373.0

    lat1 = radians(cord1[1])
    lon1 = radians(cord1[0])
    lat2 = radians(cord2[1])
    lon2 = radians(cord2[0])

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
    c = 2 * arctan2(sqrt(a), sqrt(1 - a))
    distance = R * c

    return(distance)

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)
METRO_TOWN = [-122.9987, 49.2250]
METRO_TOWN_string = '%f,%f' % (METRO_TOWN[0], METRO_TOWN[1])
nearest_petro = await conn_map.query('place', q='gas station', sort='distance', location=METRO_TOWN_string, page='1', pageSize = '1')
print('Metropolist is %fkm from the nearest gas station' % distance_in_km(METRO_TOWN, nearest_petro['coordinates'][0]))
print('The gas station is %s at %s' % (nearest_petro['name'][0], nearest_petro['address'][0]))

Metropolist is 0.376580km from the nearest gas station

The gas station is Chevron at 4692 Imperial St

In BC, which city has the most amount of shopping centers?
from dataprep.connector import connect

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)
BC_BBOX = "-139.06,48.30,-114.03,60.00"
GROCERY = 'sic:541105'
shop_list = await conn_map.query("place", sort="relevance", bbox=BC_BBOX, category=GROCERY, _count=500)
shop_list = shop_list[shop_list["state"] == "BC"]
shop_list.groupby('city')['name'].count().sort_values(ascending=False).head(10)
city count
Vancouver 42
Victoria 24
Surrey 15
Burnaby 14
... ...
North Vancouver 8
Where is the nearest grocery of SFU? How many miles far? And how much time estimated for driving?
from dataprep.connector import connect

# You can get ”mapquest_access_token“ by following https://developer.mapquest.com/
conn_map = connect("mapquest", _auth={"access_token": mapquest_access_token}, _concurrency = 10)
SFU_LOC = '-122.90416, 49.27647'
GROCERY = 'sic:541105'
nearest_grocery = await conn_map.query("place", location=SFU_LOC, sort="distance", category=GROCERY)
destination = nearest_grocery.iloc[0]['details']
name = nearest_grocery.iloc[0]['name']
route = await conn_map.query("route", from_='8888 University Drive E, Burnaby', to=destination)
total_distance = sum([float(i)for i in route.iloc[:]['distance']])
total_time = sum([int(i)for i in route.iloc[:]['time']])
print('The nearest grocery of SFU is ' + name + '. It is ' + str(total_distance) + ' miles far, and It is expected to take ' + str(total_time // 60) + 'm' + str(total_time % 60)+'s of driving.')
route

The nearest grocery of SFU is Nesters Market. It is 1.234 miles far, and It is expected to take 3m21s of driving.

id index narrative distance time
0 0 Start out going east on University Dr toward Arts Rd. 0.348 57
1 1 Turn left to stay on University Dr. 0.606 84
2 2 Enter next roundabout and take the 1st exit onto University High St. 0.28 60
3 3 9000 UNIVERSITY HIGH STREET is on the left. 0 0

Lifestyle

Spoonacular -- Collect Recipe, Food, and Nutritional Information Data

Which foods are unhealthy, i.e.,have high carbs and high fat content?
from dataprep.connector import connect
import pandas as pd

dc = connect('spoonacular', _auth={'access_token': API_key}, concurrency=3, update=True)

df = await dc.query('recipes_by_nutrients', minFat=65, maxFat=100, minCarbs=75, maxCarbs=100, _count=20)

df["calories"] = pd.to_numeric(df["calories"]) # convert string type to numeric
df = df[df['calories']>1100] # considering foods with more than 1100 calories per serving to be unhealthy

df[["title","calories","fat","carbs"]].sort_values(by=['calories'], ascending=False)
id title calories fat carbs
2 Brownie Chocolate Chip Cheesecake 1210 92g 79g
8 Potato-Cheese Pie 1208 80g 96g
0 Stuffed Shells with Beef and Broc 1192 72g 81g
3 Coconut Crusted Rockfish 1187 72g 92g
4 Grilled Ratatouille 1143 82g 88g
7 Pecan Bars 1121 84g 91g
Which meat dishes are rich in proteins?
from dataprep.connector import connect

dc = connect('spoonacular', _auth={'access_token': API_key}, concurrency=3, update=True)

df = await dc.query('recipes', query='beef', diet='keto', minProtein=25, maxProtein=60, _count=5)
df = df[["title","nutrients"]]

# Output of 'nutrients' column : [{'title': 'Protein', 'amount': 22.3768, 'unit': 'g'}]
g = [] # to extract the exact amount of Proteins in grams and store as list
for i in df["nutrients"]:
  z = i[0]
  g.append(z['amount'])
  
df.insert(1,'Protein(g)',g)
df[["title","Protein(g)"]].sort_values(by='Protein(g)',ascending=False)
id title Protein(g)
3 Strip steak with roasted cherry tomatoes and v... 56.2915
0 Low Carb Brunch Burger 53.7958
2 Entrecote Steak with Asparagus 41.6676
1 Italian Style Meatballs 35.9293
Which Italian Vegan dishes are popular?
from dataprep.connector import connect

dc = connect('spoonacular', _auth={'access_token': API_key}, concurrency=3, update=True)

df = await dc.query('recipes', query='popular veg dishes', cuisine='italian', diet='vegan', _count=20)
df[["title"]]
id Title
0 Vegan Pea and Mint Pesto Bruschetta
1 Gluten Free Vegan Gnocchi
2 Fresh Tomato Risotto with Grilled Green Vegeta...
What are the top 5 liked chicken recipes with common ingredients?
from dataprep.connector import connect
import pandas as pd

dc = connect('spoonacular', _auth={'access_token': API_key}, concurrency=3, update=True)

df= await dc.query('recipes_by_ingredients', ingredients='chicken,buttermilk,salt,pepper')
df['likes'] = pd.to_numeric(df['likes'])

df[['title', 'likes']].sort_values(by=['likes'], ascending=False).head(5)
id title likes
9 Oven-Fried Ranch Chicken 561
1 Fried Chicken and Wild Rice Waffles with Pink ... 78
6 CCC: Carla Hall’s Fried Chicken 47
2 Buttermilk Fried Chicken 12
0 My Pantry Shelf 10
What is the average calories for high calorie Korean foods?
from dataprep.connector import connect
from statistics import mean 

dc = connect('spoonacular', _auth={'access_token': API_key}, concurrency=3, update=True)

df = await dc.query('recipes', query='korean', minCalories = 500)
nutri = df['nutrients'].tolist()

calories = []
for i in range(len(nutri)):
  calories.append(nutri[i][0]['amount'])

print('Average calories for high calorie Korean foods:', mean(calories),'kcal')

Average calories for high calorie Korean foods: 644.765 kcal

Music

MusixMatch -- Collect Music Lyrics Data

What is Katy Perry's Twitter URL?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

df = await conn_musixmatch.query("artist_info", artist_mbid = "122d63fc-8671-43e4-9752-34e846d62a9c")

df[['name', 'twitter_url']]
name twitter_url
0 Katy Perry https://twitter.com/katyperry
What album is the song "Gone, Gone, Gone" in?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

df = await conn_musixmatch.query("track_matches", q_track = "Gone, Gone, Gone")

df[['name', 'album_name']]
name album_name
0 Gone, Gone, Gone The World From the Side of the Moon
Which artist/artists group is most popular in Canada?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

df = await conn_musixmatch.query("top_artists", country = "Canada")

df['name'][0]
'BTS'
How many genres are in the Musixmatch database?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

df = await conn_musixmatch.query("genres")

len(df)
362
Who is the most popular American artist named Michael?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token}, _concurrency = 5)

df = await conn_musixmatch.query("artists", q_artist = "Michael")
df = df[df['country'] == "US"].sort_values('rating', ascending=False)

df['name'].iloc[0]
'Michael Jackson'
What is the genre of the album "Atlas"?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

album = await conn_musixmatch.query("album_info", album_id = 11339785)
genres = await conn_musixmatch.query("genres")
album_genre = genres[genres['id'] == album['genre_id'][0][0]]['name']

album_genre.iloc[0]
'Soundtrack'
What is the link to lyrics of the most popular song in the album "Yellow"?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token}, _concurrency = 5)

df = await conn_musixmatch.query("album_tracks", album_id = 10266231)
df = df.sort_values('rating', ascending=False)

df['track_share_url'].iloc[0]
'https://www.musixmatch.com/lyrics/Coldplay/Yellow?utm_source=application&utm_campaign=api&utm_medium=SFU%3A1409620992740'
What are Lady Gaga's albums from most to least recent?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token}, update = True)

df = await conn_musixmatch.query("artist_albums", artist_mbid = "650e7db6-b795-4eb5-a702-5ea2fc46c848", s_release_date = "desc")

df.name.unique()
array(['Chromatica', 'Stupid Love',
       'A Star Is Born (Original Motion Picture Soundtrack)', 'Your Song'],
      dtype=object)
Which artists are similar to Lady Gaga?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

df = await conn_musixmatch.query("related_artists", artist_mbid = "650e7db6-b795-4eb5-a702-5ea2fc46c848")

df
id name rating country twitter_url updated_time artist_alias_list
0 6985 Cast 41 2015-03-29T03:32:49Z [キャスト]
1 7014 black eyed peas 77 US https://twitter.com/bep 2016-06-30T10:07:05Z [The Black Eyed Peas, ブラック・アイド・ピーズ, heiyandoud...
2 269346 OneRepublic 74 US https://twitter.com/OneRepublic 2015-01-07T08:21:52Z [ワンリパブリツク, Gong He Shi Dai, Timbaland presents...
3 276451 Taio Cruz 60 GB 2016-06-30T10:32:58Z [タイオ クルーズ, tai ou ke lu zi, Trio Cruz, Jacob M...
4 409736 Inna 54 RO https://twitter.com/inna_ro 2014-11-13T03:37:43Z [インナ]
5 475281 Skrillex 62 US https://twitter.com/Skrillex 2013-11-05T11:28:57Z [スクリレックス, shi qi lei ke si, Sonny, Skillrex]
6 13895270 Imagine Dragons 82 US https://twitter.com/Imaginedragons 2013-11-05T11:30:28Z [イマジン・ドラゴンズ, IMAGINE DRAGONS]
7 27846837 Shawn Mendes 80 CA 2015-02-17T10:33:56Z [ショーン・メンデス, xiaoenmengdezi]
8 33491890 Rihanna 81 GB https://twitter.com/rihanna 2018-10-15T20:32:58Z [りあーな, Rihanna, 蕾哈娜, Rhianna, Riannah, Robyn R...
9 33491981 Avicii 74 SE https://twitter.com/avicii 2018-04-20T18:27:01Z [アヴィーチー, ai wei qi, Avicci]
What are the highest rated songs in Canada from highest to lowest popularity?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token}, _concurrency = 5)

df = await conn_musixmatch.query("top_tracks", country = 'CA')

df[df['is_explicit'] == 0].sort_values('rating', ascending = False).reset_index()
index id name rating commontrack_id has_instrumental is_explicit has_lyrics has_subtitles album_id album_name artist_id artist_name track_share_url updated_time genres
0 5 201621042 Dynamite 99 114947355 0 0 1 1 39721115 Dynamite - Single 24410130 BTS https://www.musixmatch.com/lyrics/BTS/Dynamite... 2021-01-15T16:40:48Z [Pop]
1 9 187880919 Before You Go 99 103153140 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-11-20T08:44:05Z [Pop, Alternative]
2 7 189704353 Breaking Me 98 105304416 0 0 1 1 34892017 Keep On Loving 42930474 Topic feat. A7S https://www.musixmatch.com/lyrics/Topic-8/Brea... 2021-01-19T16:57:29Z [House, Dance]
3 3 189626475 Watermelon Sugar 95 103096346 0 0 1 1 36101498 Fine Line 24505463 Harry Styles https://www.musixmatch.com/lyrics/Harry-Styles... 2020-02-14T08:07:12Z [Music]
What are other songs in the same album as the song "Before You Go"?
from dataprep.connector import connect

# You can get ”musixmatch_access_token“ by registering as a developer https://developer.musixmatch.com/#
conn_musixmatch = connect("musixmatch", _auth={"access_token":musixmatch_access_token})

song = await conn_musixmatch.query("track_info", commontrack_id = 103153140)
album = await conn_musixmatch.query("album_tracks", album_id = song["album_id"][0])

album
id name rating commontrack_id has_instrumental is_explicit has_lyrics has_subtitles album_id album_name artist_id artist_name track_share_url updated_time genres
0 186884178 Grace 31 87857108 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-04-09T10:21:29Z [Folk-Rock]
1 186884184 Bruises 68 70395936 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2020-07-31T12:58:04Z [Music, Alternative]
2 186884187 Hold Me While You Wait 89 95176135 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2020-08-02T07:23:21Z [Music]
3 186884189 Someone You Loved 95 89461086 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2020-06-22T15:34:07Z [Pop, Alternative]
4 186884190 Maybe 31 95541701 0 1 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-05-20T11:41:00Z [Music]
5 186884191 Forever 67 95541702 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-11-18T10:46:36Z [Music]
6 186884192 One 31 95541699 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-05-19T04:08:23Z [Music]
7 186884193 Don't Get Me Wrong 31 95541698 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-12-20T08:25:26Z [Music]
8 186884194 Hollywood 31 95541700 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2019-05-21T08:00:54Z [Music]
9 186884195 Lost on You 31 73530089 0 0 1 1 35611759 Divinely Uninspired To A Hellish Extent (Exten... 33258132 Lewis Capaldi https://www.musixmatch.com/lyrics/Lewis-Capald... 2020-03-17T08:35:18Z [Alternative]

Spotify -- Collect Albums, Artists, and Tracks Metadata

How many followers does Eminem have?
from dataprep.connector import connect

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

df = await conn_spotify.query("artist", q="Eminem", _count=500)

df.loc[df['# followers'].idxmax(), '# followers']
41157398
How many singles does Pink Floyd have that are available in Canada?
from dataprep.connector import connect

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

artist_name = "Pink Floyd"
df = await conn_spotify.query("album", q = artist_name, _count = 500)

df = df.loc[[(artist_name in x) for x in df['artist']]]
df = df.loc[[('CA' in x) for x in df['available_markets']]]
df = df.loc[df['total_tracks'] == '1']
df.shape[0]
12
In the last quarter of 2020, which artist released the album with the most tracks?
from dataprep.connector import connect
import pandas as pd

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

df = await conn_spotify.query("album", q = "2020", _count = 500)

df['date'] = pd.to_datetime(df['release_date'])
df = df[df['date'] > '2020-10-01'].drop(columns = ['image url', 'external urls', 'release_date'])
df['total_tracks'] = df['total_tracks'].astype(int)
df = df.loc[df['total_tracks'].idxmax()]
print(df['album_name'] + ", by " + df['artist'][0] + ", tracks: " + str(df['total_tracks']))
ASOT 996 - A State Of Trance Episode 996 (Top 50 Of 2020 Special), by Armin van Buuren ASOT Radio, tracks: 172
Who is the most popular artist: Eminem, Beyonce, Pink Floyd and Led Zeppelin
# and what are their popularity ratings?
from dataprep.connector import connect

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

artists_and_num_followers = []
for artist in ['Beyonce', 'Pink Floyd', 'Eminem', 'Led Zeppelin']:
    df = await conn_spotify.query("artist", q = artist, _count = 500) 
    num_followers = df.loc[df['# followers'].idxmax(), 'popularity']
    artists_and_num_followers.append((artist, num_followers))

print(sorted(artists_and_num_followers, key=lambda x: x[1], reverse=True))
[('Eminem', 94.0), ('Beyonce', 88.0), ('Pink Floyd', 83.0), ('Led Zeppelin', 81.0)]```python
Who are the top 5 artists with the most followers from the current Billboard top 100 artists?
from dataprep.connector import connect
from bs4 import BeautifulSoup
import requests

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

web_page = requests.get("https://www.billboard.com/charts/artist-100")
html_soup = BeautifulSoup(web_page.text, 'html.parser')
artist_100 = html_soup.find_all('span', class_ = 'chart-list-item__title-text')

artists = {}
artists_top5 = []
for artist in artist_100:
    df_temp = await conn_spotify.query("artist", q = artist.text.strip(), _count = 10)
    df_temp = df_temp.loc[df_temp['popularity'].idxmax()]
    artists[df_temp['name']] = df_temp['# followers']
artists_top5 = sorted(artists, key = artists.get, reverse = True)[:5]
artists_top5
['Ed Sheeran', 'Ariana Grande', 'Drake', 'Justin Bieber', 'Eminem']
For a list of top 10 most popular albums from rollingstone.com which album has most selling markets (countries) around the world in 2020?
from dataprep.connector import connect
import asyncio

# You can get ”spotify_client_id“ and "spotify_client_secret" by registering as a developer https://developer.spotify.com/dashboard/#
conn_spotify = connect("spotify", _auth={"client_id":spotify_client_id, "client_secret":spotify_client_secret}, _concurrency=3)

def count_markets(text):
    lst = text.split(',')
    return len(lst)

album_artists = ["Folklore", "Fetch the Bolt Cutters", "YHLQMDLG", "Rough and Rowdy Ways", "Future Nostalgia",
                 "RTJ4", "Saint Cloud", "Eternal Atake", "What’s Your Pleasure", "Punisher"]

album_list = [conn_spotify.query("album", q = name, _count = 1) for name in album_artists]
combined = asyncio.gather(*album_list)
df = pd.concat(await combined).reset_index()
df = df.drop(columns = ['image url', 'external urls', 'index'])
df['market_count'] = df['available_markets'].apply(lambda x: count_markets(x))
df = df.loc[df['market_count'].idxmax()]
print(df['album_name'] + ", by " + df['artist'][0] + ", with " + str(df['market_count']) + " avalible countries")
folklore, by Taylor Swift, with 92 avalible countries

News

Guardian -- Collect Guardian News Data

Which news section contain most mentions related to bitcoin ?
from dataprep.connector import connect, info, Connector
import pandas as pd

conn_guardian = connect('guardian', update = True, _auth={'access_token': API_key}, concurrency=3)
df3 = await conn_guardian.query('article', _q='covid 19', _count=1000)
df3.groupby('section').count().sort_values("headline", ascending=False)
section headline url publish_date
World news 378 378 378
Business 103 103 103
US news 76 76 76
Opinion 72 72 72
Sport 53 53 53
Australia news 49 49 49
Society 44 44 44
Politics 34 34 34
Football 28 28 28
Global development 26 26 26
UK news 26 26 26
Education 17 17 17
Environment 14 14 14
Technology 10 10 10
Film 10 10 10
Science 8 8 8
Books 8 8 8
Life and style 7 7 7
Television & radio 6 6 6
Media 4 4 4
Culture 4 4 4
Stage 4 4 4
News 4 4 4
Travel 2 2 2
WEHI: Brighter together 2 2 2
Xero: Resilient business 2 2 2
Money 2 2 2
The new rules of work 1 1 1
LinkedIn: Hybrid workplace 1 1 1
Global 1 1 1
Getting back on track 1 1 1
Westpac Scholars: Rethink tomorrow 1 1 1
Food 1 1 1
All together 1 1 1
Find articles with covid precautions ?
from dataprep.connector import connect, Connector

conn_guardian = connect('guardian', update = True, _auth={'access_token': API_key}, concurrency=3)
df2 = await conn_guardian.query('article', _q='covid 19 protect',  _count=100)
df2[df2.section=='Opinion']
id headline section url publish_date
0 Billionaires made $1tn since Covid-19. They ca... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-09T11:32:20Z
1 Jeff Bezos became even richer thanks to Covid-... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-13T07:30:00Z
20 Here's how to tackle the Covid-19 anti-vaxxers... Opinion https://www.theguardian.com/commentisfree/2020... 2020-11-26T16:02:14Z
41 Can the UK deliver on the Covid vaccine rollou... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-11T09:00:24Z
68 Covid-19 has turned back the clock on working ... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-10T14:19:27Z
84 The Guardian view on Covid-19 promises: season... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-14T18:42:10Z
88 The Guardian view on responding to the Covid-1... Opinion https://www.theguardian.com/commentisfree/2020... 2020-12-30T18:58:05Z

Times -- Collect New York Times Data

Who is the author of article 'Yellen Outlines Economic Priorities, and Republicans Draw Battle Lines'
from dataprep.connector import connect

# You can get ”times_access_token“ by following https://developer.nytimes.com/apis
conn_times = connect("times", _auth={"access_token":times_access_token})
df = await conn_times.query('ac',q='Yellen Outlines Economic Priorities, and Republicans Draw Battle Lines')
df[["authors"]]
id authors
0 By Alan Rappeport
What is the newest news from Ottawa
from dataprep.connector import connect

# You can get ”times_access_token“ by following https://developer.nytimes.com/apis
conn_times = connect("times", _auth={"access_token":times_access_token})
df = await conn_times.query('ac',q="ottawa",sort='newest')
df[['headline','authors','abstract','url','pub_date']].head(1)
headline ... pub_date
0 21 Men Accuse Lincoln Project Co-Founder of Online Harassment ... 2021-01-31T14:48:35+0000
What are Headlines of articles where Trump was mentioned in the last 6 months of 2020 in the technology news section
from dataprep.connector import connect

# You can get ”times_access_token“ by following https://developer.nytimes.com/apis
conn_times = connect("times", _auth={"access_token":times_access_token})
df = await conn_times.query('ac',q="Trump",fq='section_name:("technology")',begin_date='20200630',end_date='20201231',sort='newest', _count=50)

print(df['headline'])
print("Trump was mentioned in " + str(len(df)) + " articles")
id headline
0 No, Trump cannot win Georgia’s electoral votes through a write-in Senate campaign.
1 How Misinformation ‘Superspreaders’ Seed False Election Theories
2 No, Trump’s sister did not publicly back him. He was duped by a fake account.
.. ...
49 Trump Official’s Tweet, and Its Removal, Set Off Flurry of Anti-Mask Posts

Trump was mentioned in 50 articles

What is the ranking of times a celebrity is mentioned in a headline in latter half of 2020?
from dataprep.connector import connect
import pandas as pd
# You can get ”times_access_token“ by following https://developer.nytimes.com/apis
conn_times = connect("times", _auth={"access_token":times_access_token})
celeb_list = ['Katy Perry', 'Taylor Swift', 'Lady Gaga', 'BTS', 'Rihanna', 'Kim Kardashian']
number_of_mentions = []
for i in celeb_list:
    df1 = await conn_times.query('ac',q=i,begin_date='20200630',end_date='20201231')
    df1 = df1[df1['headline'].str.contains(i)]
    a = len(df1['headline'])
    number_of_mentions.append(a)

print(number_of_mentions)
    
ranking_df = pd.DataFrame({'name': celeb_list, 'number of mentions': number_of_mentions})
ranking_df = ranking_df.sort_values(by=['number of mentions'], ascending=False)
ranking_df

[2, 6, 3, 6, 1, 0]

name number of mentions
1 Taylor Swift 6
3 BTS 6
2 Lady Gaga 3
0 Katy Perry 2
4 Rihanna 1
5 Kim Kardashian 0

Science

DBLP -- Collect Computer Science Publication Data

Who wrote this paper?
from dataprep.connector import connect
conn_dblp = connect("dblp")
df = await conn_dblp.query("publication", q = "Scikit-learn: Machine learning in Python", _count = 1)
df[["title", "authors", "year"]]
id title authors year
0 Scikit-learn - Machine Learning in Python. [Fabian Pedregosa, Gaël Varoquaux, Alexandre G... 2011
How to fetch all publications of Andrew Y. Ng?
from dataprep.connector import connect

conn_dblp = connect("dblp", _concurrency = 5)
df = await conn_dblp.query("publication", author = "Andrew Y. Ng", _count = 2000)
df[["title", "authors", "venue", "year"]].reset_index(drop=True)
id title authors venue year
0 The 1st Agriculture-Vision Challenge - Methods... [Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennife... [CVPR Workshops] 2020
... ... ... ... ...
242 An Experimental and Theoretical Comparison of ... [Michael J. Kearns, Yishay Mansour, Andrew Y. ... [COLT] 1995
How to fetch all publications of NeurIPS 2020?
from dataprep.connector import connect

conn_dblp = connect("dblp", _concurrenncy = 5)
df = await conn_dblp.query("publication", q = "NeurIPS 2020", _count = 5000)

# filter non-neurips-2020 papers
mask = df.venue.apply(lambda x: 'NeurIPS' in x)
df = df[mask]
df = df[(df['year'] == '2020')]
df[["title", "venue", "year"]].reset_index(drop=True)
id title venue year
0 Towards More Practical Adversarial Attacks on ... [NeurIPS] 2020
... ... ... ...
1899 Triple descent and the two kinds of overfittin... [NeurIPS] 2020

Shopping

Etsy -- Collect Handmade Marketplace Data.

What are the products I can get when I search for "winter jackets"?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth={'access_token': etsy_access_key}, _concurrency = 5)
# Item search
df = await conn_etsy.query("items", keywords = "winter jackets")
df[['title',"url","description","price","currency"]]
id title url description price currency quantity
0 White coat,cashmere coat,wool jacket with belt... https://www.etsy.com/listing/646692584/white-c... ★Please leave your phone number to me while yo... 183.00 USD 1
1 Vintage 90's Nike ACG Parka Jacket Large N... https://www.etsy.com/listing/937300597/vintage... Vintage 90's Nike ACG Parka Jacket Large N... 110.00 USD 1
... ... ... ... ... ... ... ... .... ..
24 Miss yo 2018 Vintage Checker Jacket for Blythe... https://www.etsy.com/listing/613790308/miss-yo... ~~ Welcome to our shop ~~\n\nSet include:\n1 Vin... 52.00 SGD 1
What's the favorites for the shop “CrazedGaming”?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth={'access_token': etsy_access_key}, _concurrency = 5)

# Shop search
df = await conn_etsy.query("shops", shop_name = "CrazedGaming",  _count = 1)
df[["name", "url", "favorites"]]
id Name Url Favorites
0 CrazedGaming https://www.etsy.com/shop/CrazedGaming?utm_sou... 265
What are the top 10 custom photo pillows ranked by number of favorites?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth = {"access_token": etsy_access_key}, _concurrency = 5)

# Item search sort by favorites
df_cp_pillow = await conn_etsy.query("items", keywords = "custom photo pillow", _count = 7000)
df_cp_pillow = df_cp_pillow.sort_values(by = ['favorites'], ascending = False)
df_top10_cp_pillow = df_cp_pillow.iloc[:10]
df_top10_cp_pillow[['title', 'price', 'currency', 'favorites', 'quantity']]
id title price currency favorites quantity
68 Custom Pet Photo Pillow, Valentines Day Gift, ... 29.99 USD 9619.0 320.0
193 Custom Shaped Dog Photo Pillow Personalized Mo... 29.99 USD 5523.0 941.0
374 Custom PILLOW Pet Portrait - Pet Portrait Pill... 49.95 USD 5007.0 74.0
196 Personalized Cat Pillow Mothers Day Gift for M... 29.99 USD 3839.0 939.0
69 Photo Sequin Pillow Case, Personalized Sequin ... 25.49 USD 3662.0 675.0
637 Family photo sequin pillow | custom image reve... 28.50 USD 3272.0 540.0
44 Custom Pet Pillow Custom Cat Pillow best cat l... 20.95 USD 2886.0 14.0
646 Sequin Pillow with Photo Personalized Photo Re... 32.00 USD 2823.0 1432.0
633 Personalized Name Pillow, Baby shower gift, Ba... 16.00 USD 2511.0 6.0
4416 Letter C pillow Custom letter Alphabet pillow ... 24.00 USD 2284.0 4.0
What are the prices of active products for quantities (>10) for a particular searched keyword "blue 2021 weekly spiral planner"?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth={'access_token': etsy_access_key}, _concurrency = 5)

# Item search and filters
planner_df = await conn_etsy.query("items", keywords = "blue 2021 weekly spiral planner", _count = 100)

result_df = planner_df[((planner_df['state'] == 'active') & (planner_df['quantity'] > 10))]
result_df
id title state url description price currency quantity views favorites
1 2021 Plaid About You Medium Daily Weekly Month... active https://www.etsy.com/listing/789842329/2021-pl... Planning and organizing life is a snap with th... 15.99 USD 496 100 11
2 2021 Undated Diary Planner , Notebook Weekly D... active https://www.etsy.com/listing/917640414/2021-un... A6 2021 Yearly Monthly Weekly Agenda Planner ,... 12.00 GBP 792 3433 168
. ... ... ... ... ... ... ... ... .. ... ... ...
85 July 2020-June 2021 Big Blue Year Large Daily ... active https://www.etsy.com/listing/776300099/july-20... This 12-month academic year planner offers a c... 6.95 USD 493 454 31
What's the average price for blue denim frayed jacket on Etsy selling in USD currency?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth = {"access_token": etsy_access_key}, _concurrency = 5)

# Item search and filters 
df_dbfjacket = await conn_etsy.query("items", keywords = "blue denim frayed jacket", _count = 500)
df_dbfjacket = df_dbfjacket[df_dbfjacket['currency'] == 'USD'].astype(float)

# Calculate average price
average_price = round(df_dbfjacket['price'].mean(), 2)
print("The average price for blue denim frayed jacket is: $", average_price)

The average price for blue denim frayed jacket is: $ 58.82

What are the top 10 viewed for keyword “ceramic wind chimes” with a given word “handmade” present in the description?
from dataprep.connector import connect

# You can get ”etsy_access_key“ by following https://www.etsy.com/developers/documentation/getting_started/oauth
conn_etsy = connect("etsy", _auth = {"access_token": etsy_access_key}, _concurrency = 5)

# Item search
df = await conn_etsy.query("items", keywords = "ceramic wind chimes",  _count = 2000)

# Filter and sorting
df = df[(df["description"].str.contains('handmade'))]
new_df = df[["title", "url", "views"]]
new_df.sort_values(by="views", ascending=False).reset_index(drop=True).head(10)
id title url views
0 Hanging ceramic wind chime in gloss white glaz... https://www.etsy.com/listing/101462779/hanging... 24406
1 Trending Now! Best Seller Birthday Gift for Mo... https://www.etsy.com/listing/555128094/trendin... 17058
2 Beautiful Ceramic outdoor hanging wind chime -... https://www.etsy.com/listing/155966922/beautif... 9758
3 Wind Chime, Garden Yard Art for Outdoor Home D... https://www.etsy.com/listing/159252106/wind-ch... 8850
4 Ceramic cow bells | wind chime bell | wall han... https://www.etsy.com/listing/538608210/ceramic... 6540
5 Mom Gift Ideas Housewarming Gifts Garden Decor... https://www.etsy.com/listing/171539253/mom-gif... 6123
6 Ceramic Wind Chimes single strand Wall Hanging... https://www.etsy.com/listing/598234797/ceramic... 5288
7 Handcraft Ceramic Bird Wind Chime/ Bird Windch... https://www.etsy.com/listing/697798625/handcra... 4733
8 Glass Wind Chime Green Leaves Windchime Garden... https://www.etsy.com/listing/744753959/glass-w... 4579
9 Handmade ceramic and driftwood wind chimes Bea... https://www.etsy.com/listing/615210251/handmad... 2774

Social

Twitch -- Collect Twitch Streams and Channels Information

How many followers does the Twitch user "Logic" have?
from dataprep.connector import connect

# You can get ”twitch_access_token“ by registering https://www.twitch.tv/#
conn_twitch = connect("twitch", _auth={"access_token":twitch_access_token}, _concurrency=3)

df = await conn_twitch.query("channels", query="logic", _count = 1000)

df = df.where(df['name'] == 'logic').dropna()
df = df[['name', 'followers']]
df.reset_index()
index name followers
0 0 logic 540274.0
Which 5 Twitch users that speak English have the most views and what games do they play?
from dataprep.connector import connect

# You can get ”twitch_access_token“ by registering https://www.twitch.tv/#
conn_twitch = connect("twitch", _auth={"access_token":twitch_access_token}, _concurrency=3)

df = await conn_twitch.query("channels",query="%", _count = 1000)

df = df[df['language'] == 'en']
df = df.sort_values('views', ascending = False)
df = df[['name', 'views', 'game', 'language']]
df = df.head(5)
df.reset_index()
index name views game language
0 495 Fextralife 1280705870 The Elder Scrolls Online en
1 9 Riot Games 1265668908 League of Legends en
2 16 ESL_CSGO 548559390 Counter-Strike: Global Offensive en
3 160 BeyondTheSummit 462493560 Dota 2 en
4 1 shroud 433902453 Rust en
Which channel has the most viewers for each of the top 10 games?
from dataprep.connector import connect

# You can get ”twitch_access_token“ by registering https://www.twitch.tv/#
conn_twitch = connect("twitch", _auth={"access_token":twitch_access_token}, _concurrency=3)

df = await conn_twitch.query("streams", query="%", _count = 1000)

# Group by games, sum viewers and sort by total viewers
df_new = df.groupby(['game'], as_index = False)['viewers'].agg('sum').rename(columns = {'game':'games', 'viewers':'total_viewers'})
df_new = df_new.sort_values('total_viewers',ascending = False)

# Select the channel with most viewers from each game 
df_2 = df.loc[df.groupby(['game'])['viewers'].idxmax()]

# Select the most popular channels for each of the 10 most popular games
df_new = df_new.head(10)['games']
best_games = df_new.tolist()
result_df = df_2[df_2['game'].isin(best_games)]
result_df = result_df.head(10)
result_df = result_df[['game','channel_name', 'viewers']]
result_df.reset_index()
index game channel_name viewers
0 3 seonghwazip 32126
1 21 Call of Duty: Warzone FaZeBlaze 7521
2 9 Dota 2 dota2mc_ru 16118
3 2 Escape From Tarkov summit1g 33768
4 15 Fortnite Fresh 10371
5 8 Hearthstone SilverName 16765
6 22 Just Chatting Trainwreckstv 6927
7 0 League of Legends LCK_Korea 77613
8 10 Minecraft Tfue 15209
9 11 VALORANT TenZ 13617
(1) What is the number of Fortnite and Valorant streams in the past 24 hours? (2) Is there any relationship between viewers and channel followers?
from dataprep.connector import connect
import pandas as pd

# You can get ”twitch_access_token“ by registering https://www.twitch.tv/#
conn_twitch = connect("twitch", _auth = {"access_token":twitch_access_token}, _concurrency = 3)

df = await conn_twitch.query("streams", query = "%fortnite%VALORANT%", _count = 1000)

df = df[['stream_created_at', 'game', 'viewers', 'channel_followers']]
df['stream_created_at'] = df['stream_created_at'].astype('str') # Convert date to string

for idx, value in enumerate(df['stream_created_at']):
    df.loc[idx,'stream_created_at'] = value[0:9] + ' ' + value[-9:-1] # Extract datetime

df['stream_created_at'] = pd.to_datetime(df['stream_created_at']) 
df['diff'] = pd.Timestamp.now().normalize() - df['stream_created_at'] 
df['diff'] = df['diff'].dt.total_seconds().astype('int') 

df2 = df[['channel_followers', 'viewers']].corr(method='pearson') # Find correlation (part 2)

df = df[df['diff'] > 864000] # Find streams in last 24 hours

options = ['Fortnite', 'VALORANT']
df = df[df['game'].isin(options)]
df = df.groupby(['game'], as_index=False)['diff'].agg('count').rename(columns={'diff':'count'})

# Print correlation part 2
print("Correlation between viewers and channel followers:")
print(df2)

# Print part 1
print('Number of streams in the past 24 hours:')
df
Correlation between viewers and channel followers:
                   channel_followers   viewers
channel_followers           1.000000  0.851698
viewers                     0.851698  1.000000

Number of streams in the past 24 hours:

game count
0 Fortnite 3
1 VALORANT 3

Twitter -- Collect Tweets Information

What are the 10 latest english tweets by SFU handle (@SFU) ?
from dataprep.connector import connect

dc = connect('twitter', _auth={'client_id':client_id, 'client_secret':client_secret})

# Querying 100 tweets from @SFU
df = await dc.query("tweets", _q="from:@SFU -is:retweet", _count=100)

# Filtering english language tweets
df = df[df['iso_language_code'] == 'en'][['created_at', 'text']]

# Displaying latest 10 tweets
df = df.iloc[0:10,]
print('-----------')
for index, row in df.iterrows():   
    print(row['created_at'], row['text'])
    print('-----------')
-----------
Mon Feb 01 23:59:16 +0000 2021 Thank you to these #SFU student athletes for sharing their insights. #BlackHistoryMonth2021 https://t.co/WGCvGrQOzu
-----------
Mon Feb 01 23:00:56 +0000 2021 How can #SFU address issues of inclusion & access for #Indigenous students & work with them to support their educat… https://t.co/knEM0SSHYu
-----------
Mon Feb 01 21:37:30 +0000 2021 DYK: New #SFU research shows media gender bias; men are quoted 3 times more often than women. #GenderGapTracker loo… https://t.co/c77PsNUIqV
-----------
Mon Feb 01 19:55:03 +0000 2021 With the temperatures dropping, how will you keep warm this winter? Check out our tips on what to wear (and footwea… https://t.co/EOCuYbio4P
-----------
Mon Feb 01 18:06:49 +0000 2021 COVID-19 has affected different groups in unique ways. #SFU researchers looked at the stresses facing “younger” old… https://t.co/gMvcxOlWvb
-----------
Mon Feb 01 16:18:51 +0000 2021 Please follow @TransLink for updates. https://t.co/nQDZQ5JYlt
-----------
Fri Jan 29 23:00:02 +0000 2021 #SFU researchers Caroline Colijn and Paul Tupper performed a modelling exercise to see if screening with rapid test… https://t.co/07aU3SP0j2
-----------
Fri Jan 29 19:01:32 +0000 2021 un/settled, a towering photo-poetic piece at #SFU's Belzberg Library, aims to centre Blackness & celebrate Black th… https://t.co/F6kp0Lwu5A
-----------
Fri Jan 29 17:02:34 +0000 2021 Learning that it’s okay to ask for help is an important part of self-care—and so is recognizing when you don't have… https://t.co/QARn1CRLyp
-----------
Fri Jan 29 00:44:11 +0000 2021 @shashjayy @shashjayy Hi Shashwat, I've spoken to my colleagues in Admissions. They're looking into it and will respond to you directly.
-----------
What are top 10 users based on retweet count ?
from dataprep.connector import connect

dc = connect('twitter', _auth={'client_id':client_id, 'client_secret':client_secret})

# Querying 1000 retweets and filtering only english language tweets
df = await dc.query("tweets", q='RT AND is:retweet', _count=1000)
df = df[df['iso_language_code'] == 'en']

# Iterating over tweets to get users and Retweet Count
retweets = {}
for index, row in df.iterrows():
  if row['text'].startswith('RT'):
      # Eg. tweet 'RT @Crazyhotboye: NMS?\nLeveled up to 80' 
      user_retweeted = row['text'][4:row['text'].find(':')]
      if user_retweeted in retweets:
          retweets[user_retweeted] += 1
      else:
          retweets[user_retweeted] = 1
          
# Sorting and displaying top 10 users
cols = ['User', 'RT_Count']
retweets_df = pd.DataFrame(list(retweets.items()), columns=cols)
retweets_df = retweets_df.sort_values(by=['RT_Count'], ascending=False).reset_index(drop=True).iloc[0:10,:]
retweets_df
id User RT_Count
0 John_Greed 195
1 uEatCrayons 85
2 Demo2020cracy 78
3 store_pup 75
4 miknitem_oasis 61
5 MarkCrypto23 54
6 realmamivee 52
7 trailblazers 50
8 devilsvalentine 40
9 SharingforCari1 38
What are the trending topics (Top 10) in twitter now based on hashtags count?
from dataprep.connector import connect
import pandas as pd
import json

dc = connect('twitter', _auth={'client_id':client_id, 'client_secret':client_secret})

pd.options.mode.chained_assignment = None
df = await dc.query("tweets", q=False, _count=2000)

def extract_tags(tags):
  tags_tolist = json.loads(tags.replace("'", '"'))
  only_tag = [str(t['text']) for t in tags_tolist]
  return only_tag

# remove tweets which do not have hashtag
has_hashtags = df[df['hashtags'].str.len() > 2]
# only 'en' tweets are our interests
has_hashtags = has_hashtags[has_hashtags['iso_language_code'] == 'en']
has_hashtags['tag_list'] = has_hashtags['hashtags'].apply(lambda t: extract_tags(t))
tags_and_text = has_hashtags[['text','tag_list']]
tag_count = tags_and_text.explode('tag_list').groupby(['tag_list']).agg(tag_count=('tag_list', 'count'))
# remove tag with only one occurence
tag_count = tag_count[tag_count['tag_count'] > 1]
tag_count = tag_count.sort_values(by=['tag_count'], ascending=False).reset_index()
# Top 10 hashtags
tag_count = tag_count.iloc[0:10,:]
tag_count
id tag_list tag_count
0 jobs 52
1 TractorMarch 24
2 corpsehusbandallegations 22
3 SidNaazians 10
4 GodMorningTuesday 8
5 SupremeGodKabir 7
6 hiring 7
7 نماز_راہ_نجات_ہے 6
8 London 5
9 TravelTuesday 5

Video

Youtube -- Collect Youtube's Content MetaData.

What are the top 10 Fitness Channels?
from dataprep.connector import connect, info

dc = connect('youtube', _auth={'access_token': auth_token})

df = await dc.query('videos', q='Fitness', part='snippet', type='channel', _count=10)
df[['title', 'description']]
id title description
0 Jordan Yeoh Fitness Hey! Welcome to my Youtube channel! I got noth...
1 FitnessBlender 600 free full length workout videos & counting...
2 The Fitness Marshall Get early access to dances by clicking here: h...
3 POPSUGAR Fitness POPSUGAR Fitness offers fresh fitness tutorial...
4 LiveFitness Hi, I am Nicola and I love all things fitness!...
5 TpindellFitness Strive for progress, not perfection.
6 Love Sweat Fitness My personal weight loss journey of 45 pounds c...
7 Martial Arts Fitness Welcome To My Channel. I love Martial Arts 🥇 ...
8 Zuzka Light My name is Zuzka Light, and my channel is all ...
9 Fitness Factory Lüdenscheid Schaut unter ff-luedenscheid.com Kostenlos übe...
Whats the top Playlists of a list of Singers?
from dataprep.connector import connect, info
import pandas as pd

dc = connect('youtube', _auth={'access_token': auth_token})

df = pd.DataFrame()
singers = [
  'taylor swift',
  'ed sheeran',
  'shawn mendes',
  'ariana grande',
  'michael jackson',
  'selena gomez',
  'lady gaga',
  'shreya ghoshal',
  'bruno mars',
  ]

for singer in singers:
  df1 = await dc.query('videos', q=singer, part='snippet', type='playlist',
                 _count=1)
  df = df.append(df1, ignore_index=True)

df[['title', 'description', 'channelTitle']]
id title description channelTitle
0 Taylor Swift Discography Sarah Bella
1 Ed Sheeran - New And Best Songs (2021) Best Of Ed Sheeran 2021 || Ed Sheeran Greatest... Full Albums!
2 Shawn Mendes: The Album 2018 (Full Album) WorldMusicStream
3 Ariana Grande - Positions (Full Album) October 30, 2020. lo115
4 Michael Jackson Mix Michael Jackson's Songs. Leo Meneses
5 Selena Gomez - Rare [FULL ALBUM 2020] selena gomez,selena gomez rare album,selena go... THUNDERS
6 Lady Gaga - Greatest Hits Lady Gaga - Greatest Hits 01 The Edge Of Glory... Gunther Ruymen
7 Shreya Ghoshal Tamil Hit Songs | #TamilSongs |... Sony Music South
8 The Best of Bruno Mars Warner Music Australia
What are the top 10 sports activities?
from dataprep.connector import connect, info
import pandas as pd
dc = connect('youtube', _auth={'access_token': auth_token})

df = await dc.query('videos', q='Sports', part='snippet', type='activity', _count=10)
df[['title', 'description', 'channelTitle']]
title description channelTitle
0 Sports Tak Sports Tak, as the name suggests, is all about... Sports Tak
1 Sports sport : an activity involving physical exertio... Sports
2 Greatest Sports Moments UPDATE: I AM IN THE PROCESS OF MAKING REVISION... WTD Productions
3 Viagra Boys - Sports (Official Video) Director: Simon Jung DOP: Paul Evans Producer:... viagra boys
4 Volleyball Open Tournament, Jagdev Kalan || 12... Volleyball Open Tournament, Jagdev Kalan || 12... Fine Sports
5 Beach Bunny - Sports booking/inquires: beachbunnymusic@gmail.com hu... Beach Bunny
6 Top 100 Best Sports Bloopers 2020 Watch the Top 100 best sports bloopers from 20... Crazy Laugh Action
7 Memorable Moments in Sports History Memorable Moments in Sports History! SUBSCRİBE... Cenk Bezirci
8 Craziest “Saving Lives” Moments in Sports History Craziest “Saving Lives” Moments in Sports Hist... Highlight Reel
9 Most Savage Sports Highlights on Youtube (S01E01) I do these videos ever year or so, they are ba... Joseph Vincent

Weather

OpenWeatherMap -- Collect Current and Historical Weather Data

What is the temperature of London, Ontario?
from dataprep.connector import connect

owm_connector = connect("openweathermap", _auth={"access_token":access_token})
df = await owm_connector.query('weather',q='London,Ontario,CA')
df[["temp"]]
id temp
0 267.96
What is the wind speed in each provincial capital city?
from dataprep.connector import connect
import pandas as pd
import asyncio

conn = connect("openweathermap", _auth={'access_token':'899b50a47d4c9dad99b6c61f812b786e'}, _concurrency = 5)

names = ["Edmonton", "Victoria", "Winnipeg", "Fredericton", "St. John's", "Halifax", "Toronto", "Charlottetown", \
 "Quebec City", "Regina", "Yellowknife", "Iqaluit", "Whitehorse"]

query_list = [conn.query("weather", q = name) for name in names]
results = asyncio.gather(*query_list)
df = pd.concat(await results)
df['name'] = names
df[["name", "wind"]].reset_index(drop=True)
id name wind
0 Edmonton 6.17
1 Victoria 1.34
2 Winnipeg 2.57
3 Fredericton 4.63
4 St. John's 5.14
5 Halifax 5.14
6 Toronto 1.76
7 Charlottetown 5.14
8 Quebec City 3.09
9 Regina 4.12
10 Yellowknife 3.60
11 Iqaluit 5.66
12 Whitehorse 9.77

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A curated list of example code to collect data from Web APIs using DataPrep.Connector.

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