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• pandas.core.groupby.DataFrameGroupBy.std (Python method, in pandas.core.groupby.DataFrameGroupBy.std)
• pandas.core.groupby.SeriesGroupBy.std (Python method, in pandas.core.groupby.SeriesGroupBy.std)
• pandas.core.resample.Resampler.std (Python method, in pandas.core.resample.Resampler.std)
• pandas.core.window.ewm.ExponentialMovingWindow.std (Python method, in pandas.core.window.ewm.ExponentialMovingWindow.std)
• pandas.core.window.expanding.Expanding.std (Python method, in pandas.core.window.expanding.Expanding.std)
• pandas.core.window.rolling.Rolling.std (Python method, in pandas.core.window.rolling.Rolling.std)
• pandas.core.window.rolling.Window.std (Python method, in pandas.core.window.rolling.Window.std)
• pandas.DataFrame.std (Python method, in pandas.DataFrame.std)
• pandas.DatetimeIndex.std (Python method, in pandas.DatetimeIndex.std)
• pandas.Series.std (Python method, in pandas.Series.std)
• pandas.core.groupby.DataFrameGroupBy.std

  pandas.core.groupby.DataFrameGroupBy.std DataFrameGroupBy.std(ddof=1, engine=None, engine_kwargs=None, numeric_only=False)[source] Compute standard deviation of groups, excluding missing values. For multiple groupin...

• pandas.core.groupby.SeriesGroupBy.std

  pandas.core.groupby.SeriesGroupBy.std SeriesGroupBy.std(ddof=1, engine=None, engine_kwargs=None, numeric_only=False)[source] Compute standard deviation of groups, excluding missing values. For multiple groupings, th...

• pandas.core.resample.Resampler.std

  pandas.core.resample.Resampler.std final Resampler.std(ddof=1, numeric_only=False, *args, **kwargs)[source] Compute standard deviation of groups, excluding missing values. Parameters: ddofint, default 1Degrees of...

• pandas.core.window.ewm.ExponentialMovingWindow.std

  pandas.core.window.ewm.ExponentialMovingWindow.std ExponentialMovingWindow.std(bias=False, numeric_only=False)[source] Calculate the ewm (exponential weighted moment) standard deviation. Parameters: biasbool, defa...

• pandas.core.window.expanding.Expanding.std

  pandas.core.window.expanding.Expanding.std Expanding.std(ddof=1, numeric_only=False, engine=None, engine_kwargs=None)[source] Calculate the expanding standard deviation. Parameters: ddofint, default 1Delta Degrees...

• pandas.core.window.rolling.Rolling.std

  pandas.core.window.rolling.Rolling.std Rolling.std(ddof=1, numeric_only=False, engine=None, engine_kwargs=None)[source] Calculate the rolling standard deviation. Parameters: ddofint, default 1Delta Degrees of Free...

• pandas.core.window.rolling.Window.std

  pandas.core.window.rolling.Window.std Window.std(ddof=1, numeric_only=False, **kwargs)[source] Calculate the rolling weighted window standard deviation. Parameters: numeric_onlybool, default FalseInclude only floa...

• pandas.DataFrame.std

  pandas.DataFrame.std DataFrame.std(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)[source] Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the...

• pandas.DatetimeIndex.std

  pandas.DatetimeIndex.std DatetimeIndex.std(*args, **kwargs)[source] Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using ddof. Parameters: axisint, optional...

• pandas.Series.std

  pandas.Series.std Series.std(axis=None, skipna=True, ddof=1, numeric_only=False, **kwargs)[source] Return sample standard deviation over requested axis. Normalized by N-1 by default. This can be changed using the dd...

• 10 minutes to pandas

  ...B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1...

• Chart visualization

  ..._C_function is a function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std). In this example the positions are given by columns a and b, while the value is given by column z. The bins are ag...

• Comparison with R / R libraries

  .......: } ....: ) ....: In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg(["mean", "std"]) Out[27]: mean std month week 5 1 63.653367 40.601965...

• Cookbook

  ..."day"]), axis=1) .....: ds.head() .....: %timeit pd.to_datetime(ds) .....: 2.7 ms +- 240 us per loop (mean +- std. dev. of 7 runs, 100 loops each) 1.09 ms +- 5.62 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) Ski...

• Enhancing performance

  ...se): In [5]: %timeit df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1) 84 ms +- 1.01 ms per loop (mean +- std. dev. of 7 runs, 10 loops each) Let’s take a look and see where the time is spent during this operation using the...

• Essential basic functionality

  ...), produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, …}, but the axis can be specified by name or integer: Series: no axis argument needed DataFrame: “index” (axis=0,...

• Group by: split-apply-combine

  ...be gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist...

• How to calculate summary statistics

  ..."Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200...

• IO tools (text, CSV, HDF5, …)

  ...ating and chunking below. Quoting, compression, and file format compression{'infer', 'gzip', 'bz2', 'zip', 'xz', 'zstd', None, dict}, default 'infer'For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip, bz2, zip, xz,...

• MultiIndex / advanced indexing
• pandas.api.extensions.ExtensionArray._reduce
• pandas.core.groupby.DataFrameGroupBy.describe
• pandas.core.groupby.DataFrameGroupBy.transform
• pandas.core.groupby.SeriesGroupBy.describe
• pandas.core.groupby.SeriesGroupBy.transform
• pandas.core.resample.Resampler.aggregate
• pandas.core.resample.Resampler.apply
• pandas.core.resample.Resampler.transform
• pandas.core.window.expanding.Expanding.aggregate
• pandas.core.window.rolling.Rolling.aggregate
• pandas.core.window.rolling.Window.mean
• pandas.core.window.rolling.Window.sum
• pandas.core.window.rolling.Window.var
• pandas.DataFrame.agg
• pandas.DataFrame.aggregate
• pandas.DataFrame.describe
• pandas.DataFrame.plot.hexbin
• pandas.DataFrame.rolling
• pandas.io.formats.style.Styler.to_latex
• pandas.Series.agg
• pandas.Series.aggregate
• pandas.Series.describe
• pandas.Series.rolling
• pandas.Timestamp.timetz
• pandas.Timestamp.tz
• Reshaping and pivot tables
• Time series / date functionality
• Version 0.12.0 (July 24, 2013)
• Version 0.13.0 (January 3, 2014)
• Version 0.13.1 (February 3, 2014)
• Version 0.14.0 (May 31 , 2014)