regression • Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols name x to the sole right-hand side variable. We can do a moving window regression to see how the relationship changes over time: In [205]: model = ols(y=rets[’AAPL’], x=rets.ix[:, [’GOOG’]], .....: window=250) unbalanced panel data (see this article if this means nothing to you). Suppose we wanted to model the relationship between the magnitude of the daily return and trading volume among a group of stocks, and we want

regression • Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols name x to the sole right-hand side variable. We can do a moving window regression to see how the relationship changes over time: In [205]: model = ols(y=rets[’AAPL’], x=rets.ix[:, [’GOOG’]], .....: window=250) unbalanced panel data (see this article if this means nothing to you). Suppose we wanted to model the relationship between the magnitude of the daily return and trading volume among a group of stocks, and we want

regression • Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols name x to the sole right-hand side variable. We can do a moving window regression to see how the relationship changes over time: In [205]: model = ols(y=rets[’AAPL’], x=rets.ix[:, [’GOOG’]], .....: window=250) unbalanced panel data (see this article if this means nothing to you). Suppose we wanted to model the relationship between the magnitude of the daily return and trading volume among a group of stocks, and we want

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore 386 Chapter 3. User Guide pandas: powerful Python data analysis toolkit, Release 1 before factorization. sort [bool, default False] Sort uniques and shuffle codes to maintain the relationship. na_sentinel [int, default -1] Value to mark “not found”. size_hint [int, optional] Hint to

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle codes to maintain the relationship. na_sentinel [int, default -1] Value to mark “not found”. size_hint [int, optional] Hint to

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle codes to maintain the relationship. na_sentinel [int, default -1] Value to mark “not found”. size_hint [int, optional] Hint to

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle codes to maintain the relationship. na_sentinel [int, default -1] Value to mark “not found”. size_hint [int, optional] Hint to

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle labels to maintain the relationship. order [None] Deprecated since version 0.23.0: This parameter has no effect and is deprecated

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle labels to maintain the relationship. order [None] Deprecated since version 0.23.0: This parameter has no effect and is deprecated

how decisions are made and how the various elements of our commu- nity interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit ('one', 'second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes before factorization. sort [bool, default False] Sort uniques and shuffle codes to maintain the relationship. na_sentinel [int, default -1] Value to mark “not found”. size_hint [int, optional] Hint to