def test_get_data_ibm():
f = get_data_stooq("IBM.UK")
assert f.shape[0] > 0
def test_get_data_stooq_dax():
f = get_data_stooq("^DAX")
assert f.shape[0] > 0
def test_stooq_googl():
f = get_data_stooq("GOOGL.US")
assert f.shape[0] > 0
def test_stooq_sp500():
f = get_data_stooq("^SPX")
assert f.shape[0] > 0
def test_stooq_clx19f():
f = get_data_stooq("CLX19.F", start="20190101", end="20190115")
assert f.shape[0] > 0
def test_get_data_stooq_dates():
f = get_data_stooq("SPY", start="20180101", end="20180115")
assert f.shape[0] == 9
def test_get_data_stooq_dji():
f = get_data_stooq("AMZN")
assert f.shape[0] > 0
def test_get_data_stooq_dji():
f = get_data_stooq('^DAX')
assert f.shape[0] > 0
def test_get_data_stooq_dates():
f = get_data_stooq('SPY', start='20180101', end='20180115')
assert f.shape[0] == 9
columns=['one', 'two'])
df
df.sum()
df.sum(axis=1) # or axis='columns'
df.mean(axis='columns', skipna=False)
df.idxmax() # idxmax idxmin return index value where max r min is attained
df.cumsum()
df.describe()
obj = pd.Series(['a', 'a', 'b', 'c'] * 4)
obj.describe()
df.count() # number of nonNA values
df.quantile(0.5)
Series(np.random.randn(10)).pct_change()
import pandas_datareader.data as web
all_data = {ticker: web.get_data_stooq(ticker) for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']}
price = DataFrame({ticker: data['Adj Close'] for ticker,data in all_data.items()})
volume = DataFrame({ticker: data['Volume'] for ticker,data in all_data.items})
returns = price.pct_change()
returns.tail()
returns.MSFT.corr(returns.IBM)
returns.corr()
returns.cov()
returns.corrwith(returns.IBM)
returns.corrwith(volume)
obj = pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])
uniques = obj.unique()
uniques
obj.value_counts()
pd.value_counts(obj.values, sort=False)
