def brandsModelsCars():
query = """
select brand.id, brand.name, model.id, model.name, car.id, car.name, car.power, car.weight, car.year, car.image_url
from car
inner join model on car.model_id = model.id
inner join brand on brand.id = model.brand_id
"""
args = []
search = request.args.get('search')
order = request.args.get('sort')
if search is None and order is None:
# No url query param: let's randomize the cars we fetch
countCursor = get_db().cursor()
countCursor.execute("select count(*) from car")
count = countCursor.fetchone()[0]
args = list(range(LIMIT_NUMBER))
for i in range(0, LIMIT_NUMBER):
args[i] = randrange(1, count, 1)
if i == 0:
query = query + "where car.id = ? "
else:
query = query + "or car.id = ? "
else:
# url query param for search and/or order
if search is not None:
search = "%" + search.upper() + "%"
query = query + " where upper(brand.name) like ? or upper(model.name) like ? or upper(car.name) like ?"
args = [search] * 3
if order == "power" or order == "ratio" or order == "weight":
if order == "ratio":
order = "weight / car.power asc"
elif order == "power":
order = "power desc"
elif order == "weight":
order = "weight asc"
query = query + " order by car." + order
query = query + LIMIT_SQL_CLAUSE
cursor = get_db().cursor()
cursor.execute(query, args)
rows = cursor.fetchall()
result = buildBrandModelCars(rows)
return jsonify(result)
def delete_orders(id):
con = get_db()
cur = con.cursor()
cur.execute("DELETE FROM client_order WHERE id = ?", [id])
result = cur.rowcount
con.commit()
return "%s ROW(S) DELETED" % result
def menu():
cursor = get_db().cursor()
cursor.execute("SELECT * FROM menu")
rows = cursor.fetchall()
objects = list(
map(lambda m: {
"id": m[0],
"title": m[1],
"price": m[2]
}, rows))
return jsonify(objects)
def main():
db = get_db()
nasdaq = db.NASDAQ
nasdaq.drop()
csv = pandas.read_csv('migrations/companylist.csv')
csv = csv.drop(['LastSale', 'ADR TSO', 'IPOyear', 'Unnamed: 9'], 1)
csv.columns = [
'symbol', 'name', 'market_cap', 'sector', 'industry', 'summary_quote'
]
documents = csv.to_dict('index')
documents = documents.values()
nasdaq.insert_many(documents)
def preference():
if request.method == 'POST':
db = init_db.get_db()
if 'name' in request.json:
cur = db.execute(
'select * from users where GroupPassword={}'.format(
request.json['password']))
entries = cur.fetchall()
if len(entries) == 0:
db.execute(
'insert into swipes (GroupPassword) values ({})'.format(
request.json['password']))
db.commit()
db.execute(
'insert into users (UserName, GroupPassword, LocationPreference) values (?, ?, ?)',
[
request.json['name'], request.json['password'],
request.json['location']
])
db.commit()
cur1 = db.execute('select * from users')
cur2 = db.execute('select * from swipes')
return json.dumps(dict(cur2.fetchone()))
else:
cur = db.execute(
'select * from users where GroupPassword={}'.format(
request.json['password']))
numUsers = len(cur.fetchone())
cur = db.execute(
'select * from swipes where GroupPassword={}'.format(
request.json['password']))
numSwipes = dict(cur.fetchone())
matched = ""
if numUsers in numSwipes.values():
matched = str(
numSwipes.keys()[numSwipes.values().index(numUsers)])
if request.json['pref'] == 'noPref':
if matched != "":
return matched
return restaurants.iloc[request.json['count']].to_json(
orient='index')
else:
if matched != "":
return matched
preferred_location = restaurants.loc[restaurants['ZONE'] ==
request.json['pref']]
preferred_location = preferred_location.sort_values(
by='HEURISTIC', ascending=False)
return preferred_location.iloc[request.json['count']].to_json(
orient='index')
def get_orders():
cursor = get_db().cursor()
cursor.execute(
"SELECT id, client, descr, price, datetime(date) date FROM client_order"
)
rows = cursor.fetchall()
objects = list(
map(
lambda m: {
"id": m[0],
"client": m[1],
"description": m[2],
"price": m[3],
"date": m[4]
}, rows))
return jsonify(objects)
def food():
cursor = get_db().cursor()
cursor.execute("SELECT * FROM food")
rows = cursor.fetchall()
objects = list(
map(
lambda m: {
"id": m[0],
"menuId": m[1],
"title": m[2],
"description": m[3],
"category": m[4],
"price": m[5],
"photo": m[6]
}, rows))
return jsonify(objects)
def post_orders():
validation_errors = validate_payload(
request, ['id', 'date', 'description', 'price', 'client'])
if len(validation_errors) > 0:
return "\n".join(validation_errors), 400
order = request.get_json(force=True, silent=True)
if not datetime_valid(order["date"]):
return "You sent an unexpected date format. Expected format is ISO string like '2016-12-13T21:20:37.593194+00:00Z'", 400
con = get_db()
cur = con.cursor()
cur.execute("SELECT * FROM client_order WHERE id = ?", [order["id"]])
rows = cur.fetchall()
if (len(rows) > 0):
return "Impossible to create the order with the id '%s': this id already exists" % order[
"id"], 400
cur.execute(
"INSERT INTO client_order(id, client, descr, price, date) VALUES (?, ?, ?, ?, ?)",
(order["id"], order["client"], order["description"], order["price"],
order["date"]))
result = cur.rowcount
con.commit()
return "%s ROW(S) INSERTED" % result
from sklearn.metrics import mean_squared_error
import urllib
import urllib2
from pandas_datareader import DataReader
import datetime
import time
import csv
import re
import pymongo
from pandas.tseries.offsets import BDay
from init_db import get_db
db = get_db()
stocks = []
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime.today() + datetime.timedelta(days=1)
discriminator = None
if (datetime.datetime.utcnow().weekday() < 4
or datetime.datetime.utcnow().weekday() == 6
) and datetime.datetime.utcnow() < datetime.datetime.fromordinal(
datetime.datetime.utcnow().date().toordinal()) + datetime.timedelta(
hours=22):
discriminator = datetime.datetime.utcnow().date()
else:
def cars():
cursor = get_db().cursor()
cursor.execute("select * from car" + LIMIT_SQL_CLAUSE)
rows = cursor.fetchall()
return jsonify(rows)
def models():
cursor = get_db().cursor()
cursor.execute("select * from model")
rows = cursor.fetchall()
return jsonify(rows)
def brands():
cursor = get_db().cursor()
cursor.execute("select * from brand")
rows = cursor.fetchall()
return jsonify(rows)
