/ moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 11.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 11.4 Exponentially series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. CONTENTS 1 pandas: powerful Python rolling_corr, ewmcov, ewmcorr, expanding_cov, expanding_corr to allow the calculation of moving window covariance and correlation matrices (GH4950). See Computing rolling pairwise covariances and correlations

series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. Many of these principles are here missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) • Static and moving window linear and panel regression 4.1 Data structures at a take two Series or DataFrames. Otherwise, they all accept the following arguments: • window: size of moving window • min_periods: threshold of non-null data points to require (otherwise result is NA)

series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. Many of these principles are here missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) • Static and moving window linear and panel regression 4.1 Data structures at a take two Series or DataFrames. Otherwise, they all accept the following arguments: • window: size of moving window • min_periods: threshold of non-null data points to require (otherwise result is NA)

series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. Many of these principles are here missing or mostly constant (some fixed value) • Moving window statistics (rolling mean, rolling standard deviation, etc.) • Static and moving window linear and panel regression 4.1 Data structures at a take two Series or DataFrames. Otherwise, they all accept the following arguments: • window: size of moving window • min_periods: threshold of non-null data points to require (otherwise result is NA)

/ moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 14.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 14.4 Exponentially series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. CONTENTS 1 pandas: powerful Python min_periods <= window rather than raising. (This makes all rolling func- tions consistent in this behavior). (GH7766) Prior to 0.15.0 In [69]: s = Series([10, 11, 12, 13]) In [15]: rolling_min(s, window=10, min_periods=5)

/ moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 14.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 14.4 Exponentially series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. CONTENTS 1 pandas: powerful Python rolling_cov(), and rolling_corr() now return objects with all NaN when len(arg) < min_periods <= window rather than raising. (This makes all rolling func- tions consistent in this behavior). (GH7766)

Groupby Describe Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.3.2.12 Window Binary Corr/Cov operations return a MultiIndex DataFrame . . . . . . . . 31 1.3.2.13 HDFStore where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 1.7.1.2 .groupby(..) syntax with window and resample operations . . . . . . . . . . . 93 1.7.1.3 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 1.8.1.1 Window functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 111 1.8.1.2 Changes

Groupby Describe Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.2.2.12 Window Binary Corr/Cov operations return a MultiIndex DataFrame . . . . . . . . 29 1.2.2.13 HDFStore where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 1.6.1.2 .groupby(..) syntax with window and resample operations . . . . . . . . . . . 91 1.6.1.3 Method chaininng improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 1.7.1.1 Window functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 109 1.7.1.2 Changes

/ moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 11.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 11.4 Exponentially series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc. CONTENTS 1 pandas: powerful Python data with a PeriodIndex will result in a higher frequency TimeSeries that spans the original time window In [20]: prng = period_range(’2012Q1’, periods=2, freq=’Q’) In [21]: s = Series(np.random.randn(len(prng))

functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 2.15.2 Window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 1252 3.3.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1253 3.3.7 Computations / descriptive stats . . . . . . . . . . . . . . . . . . . . . . . . . . 1745 3.4.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1747 vi 3.4.7 Computations / descriptive