本文整理匯總了Python中pandas.core.frame.DataFrame.items方法的典型用法代碼示例。如果您正苦於以下問題：Python DataFrame.items方法的具體用法？Python DataFrame.items怎麽用？Python DataFrame.items使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas.core.frame.DataFrame的用法示例。
在下文中一共展示了DataFrame.items方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: VAR
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import items [as 別名]
class VAR(object):
"""
Estimates VAR(p) regression on multivariate time series data
presented in pandas data structures.
Parameters
----------
data : DataFrame or dict of Series
p : lags to include
"""
def __init__(self, data, p=1, intercept=True):
try:
import statsmodels.tsa.vector_ar.api as sm_var
except ImportError:
import scikits.statsmodels.tsa.var as sm_var
self._data = DataFrame(_combine_rhs(data))
self._p = p
self._columns = self._data.columns
self._index = self._data.index
self._intercept = intercept
@cache_readonly
def aic(self):
"""Returns the Akaike information criterion."""
return self._ic['aic']
@cache_readonly
def bic(self):
"""Returns the Bayesian information criterion."""
return self._ic['bic']
@cache_readonly
def beta(self):
"""
Returns a DataFrame, where each column x1 contains the betas
calculated by regressing the x1 column of the VAR input with
the lagged input.
Returns
-------
DataFrame
"""
d = dict([(key, value.beta)
for (key, value) in self.ols_results.items()])
return DataFrame(d)
def forecast(self, h):
"""
Returns a DataFrame containing the forecasts for 1, 2, ..., n time
steps. Each column x1 contains the forecasts of the x1 column.
Parameters
----------
n: int
Number of time steps ahead to forecast.
Returns
-------
DataFrame
"""
forecast = self._forecast_raw(h)[:, 0, :]
return DataFrame(forecast, index=range(1, 1 + h),
columns=self._columns)
def forecast_cov(self, h):
"""
Returns the covariance of the forecast residuals.
Returns
-------
DataFrame
"""
return [DataFrame(value, index=self._columns, columns=self._columns)
for value in self._forecast_cov_raw(h)]
def forecast_std_err(self, h):
"""
Returns the standard errors of the forecast residuals.
Returns
-------
DataFrame
"""
return DataFrame(self._forecast_std_err_raw(h),
index=range(1, 1 + h), columns=self._columns)
@cache_readonly
def granger_causality(self):
"""Returns the f-stats and p-values from the Granger Causality Test.
If the data consists of columns x1, x2, x3, then we perform the
following regressions:
x1 ~ L(x2, x3)
x1 ~ L(x1, x3)
#.........這裏部分代碼省略.........