def get_quote_database(table, symbol, timerange):
'''
table: sqlite3 database table object
database primary key: date in mdates ordinal format
return: pandas dataframe
'''
selection = True
if len(timerange) == 1:
od1 = int(mdates.date2num(timerange[0]))
od2 = od1 + 1
elif len(timerange) == 2:
od1 = int(mdates.date2num(timerange[0]))
od2 = int(mdates.date2num(timerange[1])) + 1
print 'request range:', [od1, od2]
ts1, ts2 = table.get_timestamp_range()
print 'available data range: ', [ts1, ts2]
if ts1 and ts2:
if (od2 < ts1) or (od1 > ts2):
selection = False
if selection is True:
data = table.to_dataframe([od1, od2])
print data.head()
return data
else:
print "no selection available"
return pd.DataFrame({})
def quandl_api(userticker,scraped_data):
tickers = [userticker];
# api call to quandl, gte and lte are data bounds
data = quandl.get_table('WIKI/PRICES', ticker = tickers,
qopts = { 'columns': ['ticker', 'date', 'adj_close'] },
date = { 'gte': '2018-01-19', 'lte': '2018-10-20' },
paginate=True)
print(data.head(1))
# get most recent entry
onedata=data.head(1)
onedata = onedata.reset_index()
lastdate = onedata[['date'][0]]
lastdatevalue = lastdate[0]
lastclosing = onedata[['adj_close'][0]]
lastclosingvalue = float(lastclosing[0])
#convert date to human readable format
readabledate = "{:%B %d, %Y}".format(lastdatevalue)
# extract last closing of stock and conver to number
stripcomma = scraped_data["Previous Close"]
stripcomma = stripcomma.replace(',' , '')
currentclose = float(stripcomma)
# find difference in closing value
diff = currentclose - lastclosingvalue
diff = round(diff,2)
print(str(userticker), ' has had a change of', str(diff), 'since', readabledate)
# add change to scraped data file
strwrite = str(diff) + ' from ' + readabledate
scraped_data["change"] = strwrite
import pandas as pd
from pandas_datareader import data
start_date = '2019-01-01'
end_date = '2020-01-01'
ticker = 'AMZN'
data = data.get_data_yahoo(ticker, start_date, end_date)
data.head()
import matplotlib.pyplot as plt
from matplotlib import inline
from astropy.utils.tests import data
data['Adj Close'].plot(figsize=(10, 7))
plt.title('Adjusted Close Price of %s' % ticker, fontsize=16)
plt.ylabel('Price', fontsize=14)
plt.xlabel('Year', fontsize=14)
plt.grid(which='major', color='k', linestyle='_.', linewidth=0.5)
plt.show()
# Rolling Windows
rolling = goog.rolling(365, center = True)
data = pd.DataFrame({'input': goog,
'one-year rolling_mean': rolling.mean(),
'one-year rolling_std': rolling.std()})
ax = data.plot(style = ['-', '--', ':'])
ax.lines[0].set_alpha(0.3)
#%%
# example: Visualizing Seattle Bycicle Counts
data = pd.read_csv('data/Fremont_Bridge.csv', index_col = 'Date',
parse_dates =True)
#%%
print(data.head())
print(data.columns)
#%%
data.columns = ['Total', 'East', 'West']
#%%
data.dropna().describe()
#%%
# Visualizing the data
data.plot()
plt.ylabel('Hourly Bicycle Count')
#%% resample data to a coarser grid
weekly = data.resample('W').sum()
weekly.plot(style = [':', '--', '-'])
def get_data_summary(data):
print("datasize: ", len(data))
print("No. of columns : ", len(data.columns))
print("\nColumn List:", list(data.columns), "\n")
print("\n data glimpse\n")
print(data.head())
from datetime import date
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from pandas.plotting import scatter_matrix
# In[2]:
data = pd.read_csv(
'/Users/xiangliu/Desktop/CSC560 Data/pollution_us_2000_2016.csv')
data.shape
# In[3]:
data.head(3)
# In[4]:
le = data['CO AQI']
le[le.isnull()]
# In[5]:
##Look Into each value
data = data.dropna()
data.to_csv("/Users/xiangliu/Desktop/CSC560 Data/pollution_AQI.csv",
index=True,
sep=',')
# In[6]:
import pandas_datareader as pdr
df = pdr.get_data_fred('GS10')
#https://fred.stlouisfed.org/series/GS10
df.shape
df.head()
import pandas as pd
from pandas_datareader import data
# Set the start and end date
start_date = '1990-01-01'
end_date = '2019-04-27'
# Set the ticker
ticker = 'AMZN'
# Get the data
data = data.get_data_yahoo(ticker, start_date, end_date)
data.head()
data.shape
df = data.drop(['Volume'], axis=1)
import matplotlib.pyplot as plt
data['Adj Close'].plot()
df.plot()
plt.savefig('pandas_datareader_demo1.png')
import yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
data.shape
data.head(10)
# quantrautil is a module specific to Quantra to fetch stock data
from quantrautil import get_quantinsti_api_key
api_key = get_quantinsti_api_key()
data = quandl.get('EOD/AAPL', start_date='2017-1-1', end_date='2018-1-1', api_key= api_key)
# Note that you need to know the "Quandl code" of each dataset you download. In the above example, it is 'EOD/AAPL'.
# To get your personal API key, sign up for a free Quandl account. Then, you can find your API key on Quandl account settings page.
print("________________________")
print(data.head())'''
import pandas as pd
from pandas_datareader import data
data = data.get_data_yahoo('AAPL', '2017-01-01', '2018-01-01')
data.head()
print(data.head())
# Yahoo recently has become an unstable data source.
# If it gives an error, you may run the cell again, or try yfinance
import pandas as pd
from pandas_datareader import data
# Set the start and end date
start_date = '1990-01-01'
end_date = date.today() #'2019-02-01'
# Set the ticker
ticker = 'AMZN'
# Get the data
data = data.get_data_yahoo(ticker, start_date, end_date)
data.head()
