. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 8.2 Moving (rolling) statistics / moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 8.3 stat ops with level parameter passed (GH545) • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (GH374) 8 Chapter 1. What’s New pandas: powerful • DataFrame.convert_objects method for inferring better dtypes for object columns (GH302) • Add rolling_corr_pairwise function for computing Panel of correlation matrices (GH189) • Add margins option

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 8.2 Moving (rolling) statistics / moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 8.3 stat ops with level parameter passed (GH545) • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (GH374) 1.4 v.0.6.1 (December 13, 2011) 1.4.1 New • DataFrame.convert_objects method for inferring better dtypes for object columns (GH302) • Add rolling_corr_pairwise function for computing Panel of correlation matrices (GH189) 1.4. v.0.6.1 (December

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8.2 Moving (rolling) statistics / moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 8 stat ops with level parameter passed (GH545) • Ported skiplist data structure to C to speed up rolling_median by about 5-10x in most typical use cases (GH374) 1.5 v.0.6.1 (December 13, 2011) 1.5.1 New • DataFrame.convert_objects method for inferring better dtypes for object columns (GH302) • Add rolling_corr_pairwise function for computing Panel of correlation matrices (GH189) • Add margins option

experimental features) See Version Policy for more. {{ header }} 1.2 Enhancements 1.2.1 Using Numba in rolling.apply and expanding.apply We’ve added an engine keyword to apply() and apply() that allows the user For more details, see rolling apply documentation (GH28987, GH30936) 3 pandas: powerful Python data analysis toolkit, Release 1.0.0 1.2.2 Defining custom windows for rolling operations We’ve added api.indexers.BaseIndexer() class that allows users to define how window bounds are created during rolling operations. Users can define their own get_window_bounds method on a pandas. api.indexers.BaseIndexer()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 2.19.2 Rolling window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2496 3.9.1 Rolling window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2496 3 SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional data Excel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 2.19.2 Rolling window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2494 3.9.1 Rolling window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2494 3 SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional data Excel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818 2.19.2 Rolling window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2432 3.9.1 Rolling window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2432 3 SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional data Excel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785 2.19.2 Rolling window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 788 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2357 3.9.1 Rolling window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358 3.9 SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional data Excel

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 2.19.2 Rolling window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2432 3.9.1 Rolling window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2432 3 SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional data Excel

read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.12.0 Google Big Query access and cumprod() preserve the location of NaN values. This is somewhat different from expanding() and rolling(). For more details please see this note. In [86]: df.cumsum() Out[86]: one two three a 1.394981 date_range('2000-1-1', periods=150, freq='B')) NameError: name 'pd' is not defined In [192]: ma = price.rolling(20).mean() --------------------------------------------------------------------------- NameError