Correctly Calculating min, max, and More... 2021-10-29 Copyright © 2020-2021 by Walter E. Brown. All rights reserved. 1 Welcome! 1 Sound check [London Fanfare Trumpets: Flourish Copyright © 2020-2021 by W alter E. Brown. All rights reserved. 2 Correctly Calculating min, max, and More Walter E. Brown, Ph.D. < webrown.cpp @ gmail.com > Edition: 2021-10-29. Copyright © serves as a stimulating introduction to the study of truth. — Walter Lippmann 7 Correctly Calculating min, max, and More... 2021-10-29 Copyright © 2020-2021 by Walter E. Brown. All rights reserved. 2

powerful Python data analysis toolkit, Release 0.13.1 min or max element respectively. Prior to 0.13.0 these would return the position of the min / max element. (GH6214) 1.2.2 Prior Version Deprecations/Changes values=’MEAN_TEMP’) df3 = pandas.concat([df2.min(), df2.mean(), df2.max()], axis=1,keys=["Min Tem", "Mean Temp", "Max Temp"]) The resulting DataFrame is: > df3 Min Tem Mean Temp Max Temp MONTH 1 -53.336667 indexes during join operations (GH3877) • Timestamp.min and Timestamp.max now represent valid Timestamp instances instead of the default date- time.min and datetime.max (respectively), thanks @SleepingPills

indexes during join operations (GH3877) • Timestamp.min and Timestamp.max now represent valid Timestamp instances instead of the default date- time.min and datetime.max (respectively), thanks @SleepingPills Series. – deprecated the unique method, can be replicated by select_column(key,column).unique() – min_itemsize parameter to append will now automatically create data_columns for passed keys 1.2.8 Enhancements return the calling object as before. A deprecation message has been added • Groupby aggregations Max/Min no longer exclude non-numeric data (GH2700) • Resampling an empty DataFrame now returns an empty DataFrame

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% 7.000000 75% 7.500000 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% 5 6.500000 50% 5 7.000000 rolling-moment functions to dictate how to handle 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

microseconds and nanoseconds separately. # Timedelta accessor In [9]: tds = Timedelta(’31 days 5 min 3 sec’) In [10]: tds.minutes Out[10]: 5L In [11]: tds.seconds Out[11]: 3L # datetime.timedelta accessor Rolling/Expanding Moments improvements • rolling_min(), rolling_max(), rolling_cov(), and rolling_corr() now return objects with all NaN when len(arg) < min_periods <= window rather than raising. (This makes 11, 12, 13]) In [15]: rolling_min(s, window=10, min_periods=5) ValueError: min_periods (5) must be <= window (4) New behavior In [70]: rolling_min(s, window=10, min_periods=5) Out[70]: 0 NaN 1 NaN

microseconds and nanoseconds separately. # Timedelta accessor In [9]: tds = Timedelta(’31 days 5 min 3 sec’) In [10]: tds.minutes Out[10]: 5L In [11]: tds.seconds Out[11]: 3L # datetime.timedelta accessor Rolling/Expanding Moments improvements • rolling_min(), rolling_max(), rolling_cov(), and rolling_corr() now return objects with all NaN when len(arg) < min_periods <= window rather than raising. (This makes Release 0.15.1 In [15]: rolling_min(s, window=10, min_periods=5) ValueError: min_periods (5) must be <= window (4) New behavior In [70]: rolling_min(s, window=10, min_periods=5) Out[70]: 0 NaN 1 NaN

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 the entire data 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 The describe() method provides 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 75% 38.000000 31.000000 max 80

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 the entire data 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 The describe() method provides 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 75% 38.000000 31.000000 max 80

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 the entire data Release 1.3.2 (continued from previous page) 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 The describe() method provides 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 75% 38.000000 31.000000 max 80

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 the entire data Release 1.3.3 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 The describe() method provides 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 75% 38.000000 31.000000 max 80