How to use the quandl.ApiConfig.api_key function in Quandl

# Author: James Campbell
# Date Created: May 21st 2016
# Date Updated: 2 July 2019
# What: Example to get stock prices
from sys import exit
try:
    import quandl
except Exception:
    exit('quandl module required, run pip or pip3 install quandl --update')
try:
    from configs import myqkey
except Exception:
    print('no configs file set, create a file called configs.py and add var myqkey = "whatever"')
    myqkey = 'yoursecretkeyfromquandl.com'
# set API key
quandl.ApiConfig.api_key = myqkey  # get free key at quandl.com

dataset_data = quandl.Dataset('WIKI/AAPL').data(params={'start_date': '2001-01-01',
                                                        'end_date': '2010-01-01',
                                                        'collapse': 'annual',
                                                        'transformation': 'rdiff', 'rows': 4})
print('first date: {}'.format(dataset_data[0].date))
print('Total days of stock data available: {}'.format(len(dataset_data)))
print('The data includes the following columns: {}'.format(dataset_data.column_names))

def quandl_process(loop):
    quandl.ApiConfig.api_key = settings.QUANDL_API_KEY
    quandl_symbols = ["YAHOO/INDEX_VIX", "CBOE/VXV"]
    quandl_periods = [("monthly", 43200), (None, 1440), ("weekly", 10080)]

    loop.run_until_complete(gather(*[save_quandl_file(\
        sym=sym, quandl_periods=quandl_periods) for sym in quandl_symbols], \
        return_exceptions=True
    ))

# We are gonna use Scikit's LinearRegression model
from sklearn.linear_model import LinearRegression
import quandl
import math
import sys, os

predict_accuracy = 0.91
price_compare = "bid"

# FORMAT EXAMPLE
# epic_id = "KA.D.LLOY.DAILY.IP"
# epic_id = "KA.D.BARC.DAILY.IP"
# QUAND_REF = "LSE/LLOY"
# QUAND_REF = "LSE/BARC"

quandl.ApiConfig.api_key = "**************"
#MORE INFORMATION HERE:
#http://help.quandl.com/article/320-where-can-i-find-my-api-key

########################################################################################################################
REAL_OR_NO_REAL = 'https://demo-api.ig.com/gateway/deal'
API_ENDPOINT = "https://demo-api.ig.com/gateway/deal/session"
API_KEY = '**************' 
#API_KEY = '**************'
data = {"identifier":"**************","password": "**************"}
########################################################################################################################
########################################################################################################################
########################################################################################################################
# FOR REAL....
########################################################################################################################
########################################################################################################################
########################################################################################################################

symbol = 'PTT'
    print(json.dumps(getQuotes('SET:' + symbol), indent=2)) # for Stock of Thailand, There is a prefix with 'SET:'
    print()
except:
    print("Error:", sys.exc_info()[0])
    print("Description:", sys.exc_info()[1])
#http://www.google.com/finance/company_news?output=json&q=GOOG&start=0&num=1000

# Second example
# How to install quandl package
# https://github.com/quandl/quandl-python
# pip install quandl
print("+++++quandl example+++++")
import quandl
try:
    quandl.ApiConfig.api_key = 'YOUR_API_KEY' #(must register at https://www.quandl.com/)
    print("THAISE index:")
    data = quandl.get("THAISE/INDEX")
    #data = quandl.get("THAISE/INDEX", authtoken="YOUR_API_KEY")
    print(data.head())
except:
    print("Error:", sys.exc_info()[0])
    print("Description:", sys.exc_info()[1])

try:
    print("\nAAPL:")
    data = quandl.get("WIKI/AAPL", start_date="2006-10-01", end_date="2012-01-01")
    print(data.head())
    print()
except:
    print("Error:", sys.exc_info()[0])
    print("Description:", sys.exc_info()[1])

# 		company_details_values['country'] = 'NA'
	# 		company_details_values['phone_number'] = 'NA'
	# 		company_details_values['website'] = 'NA'



	# 	return render(request, 'visualisation/company.html', { 'date' : date_json  , 'price' : close_json , 'company' : company,'company_details' : company_details,'company_details_values' : company_details_values})

       
	# else:
	pred = {}
	


	arg = request.GET.get('company_name')
	quandl.ApiConfig.api_key = "23KLyzjn5UvKQog-DZyM"
	company = arg
	data = quandl.get_table('WIKI/PRICES', ticker = company,
		qopts = { 'columns': ['ticker', 'date', 'adj_close' , 'volume'] },
		date = { 'gte': '2015-12-31', 'lte': '2016-12-31' }, 
		paginate=True)
	data = data.set_index('ticker')
	print(type(data))
	date_col = data.ix[:,0]
	date_json = date_col.to_json(orient='records')
	close_col = data.ix[:,1]
	volume = data.ix[:,2]
	print(date_col)
	close_json = close_col.to_json(orient='records')
	print(date_json)
	print(close_json)

# We are gonna use Scikit's LinearRegression model
from sklearn.linear_model import LinearRegression
import quandl
import math
import sys, os

predict_accuracy = 0.98
price_compare = "bid"

# FORMAT EXAMPLE
# epic_id = "KA.D.LLOY.DAILY.IP"
# epic_id = "KA.D.BARC.DAILY.IP"
# QUAND_REF = "LSE/LLOY"
# QUAND_REF = "LSE/BARC"

quandl.ApiConfig.api_key = "*********"
#MORE INFORMATION HERE:
#http://help.quandl.com/article/320-where-can-i-find-my-api-key

########################################################################################################################
REAL_OR_NO_REAL = 'https://demo-api.ig.com/gateway/deal'
API_ENDPOINT = "https://demo-api.ig.com/gateway/deal/session"
#API_KEY = '*********' 
API_KEY = '*********'
data = {"identifier":"*********","password": "*********"}
########################################################################################################################
########################################################################################################################
########################################################################################################################
# FOR REAL....
########################################################################################################################
########################################################################################################################
########################################################################################################################

def get_adj_close(self, selected):
        """
        Get a 3D dataframe of Adjusted close price from Quandl.
        """
        # get adjusted closing prices of 5 selected companies with Quandl
        quandl.ApiConfig.api_key = 'CxU5-dDyxppBFzVgGG6z'
        data = quandl.get_table('WIKI/PRICES', ticker=selected,
                                qopts={'columns': ['date', 'ticker', 'adj_close']},
                                date={'gte': START_TRAIN, 'lte': END_TRAIN}, paginate=True)
        return data

import pickle
import pandas as pd 
import quandl 
import matplotlib.pyplot as plt 
from matplotlib import style

style.use('seaborn')

quandl.ApiConfig.api_key = 'rFsSehe51RLzREtYhLfo'

def mortgage_30yr():
	df = quandl.get('FMAC/MORTG', trim_start="1975-01-01")
	df['Value'] = (df['Value'] - df['Value'][0]) / df['Value'][0] * 100
	df = df.resample('M').mean()
	df.rename(columns={'Value': 'M30'}, inplace=True)
	df = df['M30']
	return df 

def sp500_data():
    df = quandl.get("YAHOO/INDEX_GSPC", trim_start="1975-01-01")
    df["Adjusted Close"] = (df["Adjusted Close"]-df["Adjusted Close"][0]) / df["Adjusted Close"][0] * 100.0
    df=df.resample('M').mean()
    df.rename(columns={'Adjusted Close':'sp500'}, inplace=True)
    df = df['sp500']
    return df

import matplotlib.pyplot as plt
import os
import pandas as pd
from matplotlib import style
from my_utils import my_gather, my_df_cols, FEATURES
from my_keys import quandl_api_key
import numpy as np
from sklearn import svm, preprocessing
import quandl
style.use("ggplot")

style.use('dark_background')

path = "/Users/tomiwa/Downloads/intraQuarter"

quandl.ApiConfig.api_key = quandl_api_key

data = quandl.Dataset("WIKI/KO" ).data(params={ 'start_date':'2001-12-01', 'end_date':'2010-12-30'})

def Key_Stats(gather=my_gather):
    statspath = path + '/_KeyStats'
    stock_list = [x[0] for x in os.walk(statspath)]
    df = pd.DataFrame(columns=my_df_cols)
    sp500_df = pd.DataFrame.from_csv("YAHOO-INDEX_GSPC.csv")

    stock_list = stock_list[1:550:7]
    print('stock_list: ', stock_list)
    time.sleep(5)

    ticker_list = []
    counter = 0
    for each_dir in stock_list:  # first 25 elements (1-50 skip 2)

"""
DEPRECATED: DOESN'T WORK ANY MORE
"""

import quandl
import pandas as pd
from sysdata.quandl.quandl_utils import load_private_key
from sysdata.fx.spotfx import fxPricesData, fxPrices
from syscore.fileutils import get_filename_for_package

quandl.ApiConfig.api_key = load_private_key()

NOT_IN_QUANDL_MSG = "You can't add, delete, or get a list of codes for Quandl FX data"
QUANDL_CCY_CONFIG_FILE = get_filename_for_package("sysdata.quandl.QuandlFXConfig.csv")

class quandlFxPricesData(fxPricesData):
    def __repr__(self):
        return "Quandl FX price data"

    def _get_fx_prices_without_checking(self, currency_code):
        qcode = self._get_qcode(currency_code)
        try:
            fx_prices = quandl.get(qcode)
        except Exception as exception:
            self.log.warn("Can't get QUANDL data for %s error %s" % (qcode, exception))
            return fxPrices.create_empty()