>>> string_data[0] = None >>> string_data 0 None 1 artichoke 2 NaN 3 avocado dtype: object >>> string_data.isnull() 0 True 1 False 2 True 3 False dtype: bool >>> from numpy import nan as NA >>> data = Series([1, NA, 3.5, NA, 7]) >>> data.dropna() 0 1.0 2 3.5 4 7.0 dtype: float64 >>> data 0 1.0 1 NaN 2 3.5 3 NaN 4 7.0 dtype: float64 >>> data[data.notnull()] 0 1.0 2 3.5 4 7.0
def calculate_sp_returns(self):
sp = self.get_sp_data()
data = pd.DataFrame({'sp_adj_close':sp['Adj Close']}, index=sp.index)
data[['sp_returns']] = data[['sp_adj_close']]/data[['sp_adj_close']].shift(1)-1
sp_return = data.dropna()
return sp_return
>>> pd.value_counts(obj.values,sort=False) a 3 c 3 b 1 d 1 dtype: int64 >>> from numpy import nan as NA >>> data=pd.Series([1,NA,3.5,NA,7]) >>> data 0 1.0 1 NaN 2 3.5 3 NaN 4 7.0 dtype: float64 >>> data.dropna() 0 1.0 2 3.5 4 7.0 dtype: float64 >>> data 0 1.0 1 NaN 2 3.5 3 NaN 4 7.0 dtype: float64 >>> data[data.notnull()] 0 1.0 2 3.5 4 7.0
def calculate_stock_returns(self):
stock = self.get_data()
data = pd.DataFrame({'stock_adj_close':stock['Adj Close']}, index=stock.index)
data[['stock_returns']] = data[['stock_adj_close']]/data[['stock_adj_close']].shift(1)-1
stock_return = data.dropna()
return stock_return
