What Volatile Means (and Doesn't Mean) Copyright © 2024 by Ben Saks and Dan Saks 0-1 What Volatile Means (and Doesn’t Mean) by Ben Saks and Dan Saks September, 2024 1 Saks & Associates These notes +1-412-521-4117 (voice) service@saksandassociates.com saksandassociates.com 2What Volatile Means (and Doesn't Mean) Copyright © 2024 by Ben Saks and Dan Saks 0-2 Introduction  The volatilequalifier is a vital doesn’t provide  Workarounds for compiler issues regarding volatile 8What Volatile Means (and Doesn't Mean) Copyright © 2024 by Ben Saks and Dan Saks 0-3 Why volatile is Necessary  Many device drivers

of your data: In [19]: df.describe() Out[19]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.019114 -0.237323 1.536861 -0.610132 std 1.117102 1.415574 0.988006 0.416115 min -1.323650 -2.769586 Release 0.25.3 In [61]: df.mean() Out[61]: A -0.286344 B -0.489887 C 1.536861 D 5.000000 F 3.000000 dtype: float64 Same operation on the other axis: In [62]: df.mean(1) Out[62]: 2013-01-01 1.698387 DataFrame. Most of these are aggregations (hence producing a lower-dimensional result) like sum(), mean(), and quantile(), but some of them, like cumsum() and cumprod(), produce an object of the same size

df.apply(lambda x: x.describe()) Out[903]: 0 1 2 3 count 10.000000 10.000000 10.000000 10.000000 mean -0.556258 -0.268695 -0.215066 0.073787 std 0.775352 1.072879 1.431537 0.962624 min -1.696646 -2 error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (GH536) • Default name assignment when calling reset_index on DataFrame respectively (GH319) • Implemented operators ‘&’, ‘|’, ‘^’, ‘-‘ on DataFrame (GH347) • Added Series.mad, mean absolute deviation • Added QuarterEnd DateOffset (PR321) • Added dot to DataFrame (GH65) • Added

df.apply(lambda x: x.describe()) Out[904]: 0 1 2 3 count 10.000000 10.000000 10.000000 10.000000 mean -0.556258 -0.268695 -0.215066 0.073787 std 0.775352 1.072879 1.431537 0.962624 min -1.696646 -2 error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (GH536) • Default name assignment when calling reset_index on DataFrame respectively (GH319) • Implemented operators ‘&’, ‘|’, ‘^’, ‘-‘ on DataFrame (GH347) • Added Series.mad, mean absolute deviation • Added QuarterEnd DateOffset (PR321) • Added dot to DataFrame (GH65) • Added

describe() Out[931]: A bar count 3.000000 mean 0.182047 std 2.350329 min -1.581790 25% -1.152040 50% -0.722290 75% 1.063965 max 2.850221 foo count 5.000000 mean -0.425297 std 1.057399 min -1.868914 df.apply(lambda x: x.describe()) Out[934]: 0 1 2 3 count 10.000000 10.000000 10.000000 10.000000 mean -0.372564 0.069529 0.149059 -0.135687 std 0.544436 1.021552 1.537344 0.905893 min -1.039777 -1.246778 error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class also (GH536) • Default name assignment when calling reset_index on DataFrame

[38]: g.describe() Out[38]: B A 1 count 1.000000 mean 4.000000 std NaN min 4.000000 25% 4.000000 50% 4.000000 75% 4.000000 ... ... 5 mean 7.000000 std 1.414214 min 6.000000 25% 6.500000 50% describe() Out[42]: A B 0 count 2 1.000000 mean 1 4.000000 std 0 NaN min 1 4.000000 25% 1 4.000000 50% 1 4.000000 75% 1 4.000000 ... .. ... 1 mean 5 7.000000 std 0 1.414214 min 5 6.000000 25% resampling; rolling_max() de- faults to max, rolling_min() defaults to min, and all others default to mean (GH6297) 22 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.14.0

sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.4 pandas.core.window.Rolling.median quantile . . . . . . . . . . . . . . . . . . . . . . . . . 1769 34.12.1.15pandas.core.window.Window.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1770 34.12.1.16pandas.core.window.Window.sum sum . . . . . . . . . . . . . . . . . . . . . . . . . 1771 34.12.2.3 pandas.core.window.Expanding.mean . . . . . . . . . . . . . . . . . . . . . . . . . 1771 34.12.2.4 pandas.core.window.Expanding.median

sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.4 pandas.core.window.Rolling.median quantile . . . . . . . . . . . . . . . . . . . . . . . . . 1907 34.15.1.15pandas.core.window.Window.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1907 34.15.1.16pandas.core.window.Window.sum sum . . . . . . . . . . . . . . . . . . . . . . . . . 1908 34.15.2.3 pandas.core.window.Expanding.mean . . . . . . . . . . . . . . . . . . . . . . . . . 1909 34.15.2.4 pandas.core.window.Expanding.median

sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.4 pandas.core.window.Rolling.median quantile . . . . . . . . . . . . . . . . . . . . . . . . . 1639 34.12.1.15pandas.core.window.Window.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1639 34.12.1.16pandas.core.window.Window.sum sum . . . . . . . . . . . . . . . . . . . . . . . . . 1641 34.12.2.3 pandas.core.window.Expanding.mean . . . . . . . . . . . . . . . . . . . . . . . . . 1641 34.12.2.4 pandas.core.window.Expanding.median

straightforward. To introduction tutorial To user guide Straight to tutorial... Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on statistics of the numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 Aggregating statistics What is the average age of the Titanic passengers? In [4]: titanic["Age"].mean() Out[4]: 29.69911764705882 Different statistics are available and can be applied to columns with