Sync pandas-fast-flexible-intuitive with the maintenance update of 'Fast, Flexible, Easy and Intuitive: How to Speed Up Your pandas Projects'

Author: martin-martin

pandas-fast-flexible-intuitive/README.md

@@ -4,9 +4,15 @@ Corresponding data for ["Fast, Flexible, Easy and Intuitive: How to Speed Up You
 
 ## Running the Script
 
+Install the pinned dependencies first:
+
+```bash
+$ python -m pip install -r requirements.txt
+```
+
 The module `tutorial/__main__.py` is the script that mimics the tutorial code.  A `__main__.py` script has the effect of executing when the containing package is run as `python -m <packagename>`.
 
-Here is an example output (MacBook Pro, 3.1 GHz Intel Core i5):
+Here is an example output (Python 3.14, pandas 3.0.6):
 
 ```bash
  pandas-fast-flexible-intuitive$ python3 -m tutorial
@@ -17,32 +23,32 @@ Companion code to https://realpython.com/fast-flexible-pandas.
 Note: most of the functions here modify a Pandas DataFrame in-place,
 which is generally not great practice but used with caution here.
 
-Python version: 3.6.6
-Pandas version: 0.23.2
+Python version: 3.14.6
+Pandas version: 3.0.6
 
 Timing code ...
 
 Best of 3 trials with 10 function calls per trial:
-Function `convert` ran in average of 1.348 seconds.
+Function `convert` ran in average of 0.374 seconds.
 
 Best of 3 trials with 100 function calls per trial:
-Function `convert_with_format` ran in average of 0.025 seconds.
+Function `convert_with_format` ran in average of 0.018 seconds.
 
 Best of 2 trials with 10 function calls per trial:
-Function `apply_tariff_loop` ran in average of 3.313 seconds.
+Function `apply_tariff_loop` ran in average of 1.338 seconds.
 
 Best of 3 trials with 50 function calls per trial:
-Function `apply_tariff_iterrows` ran in average of 0.650 seconds.
+Function `apply_tariff_iterrows` ran in average of 0.326 seconds.
 
 Best of 3 trials with 100 function calls per trial:
-Function `apply_tariff_withapply` ran in average of 0.202 seconds.
+Function `apply_tariff_withapply` ran in average of 0.067 seconds.
 
 Best of 3 trials with 1000 function calls per trial:
-Function `apply_tariff_isin` ran in average of 0.004 seconds.
+Function `apply_tariff_isin` ran in average of 0.003 seconds.
 
 Best of 3 trials with 1000 function calls per trial:
 Function `apply_tariff_cut` ran in average of 0.001 seconds.
 
 Best of 3 trials with 1000 function calls per trial:
-Function `apply_tariff_digitize` ran in average of 0.001 seconds.
+Function `apply_tariff_digitize` ran in average of 0.000 seconds.
 ```

pandas-fast-flexible-intuitive/requirements.txt

@@ -0,0 +1,2 @@
+numpy==2.5.3
+pandas==3.0.6

pandas-fast-flexible-intuitive/tutorial/__main__.py

@@ -12,7 +12,7 @@
 from .timer import timeit
 
 
-# Two functions for conversion of object dtype to datetime
+# Two functions for conversion of str dtype to datetime
 # Note that you could also specify `parse_dates` within `pd.read_csv()`
 @timeit(repeat=3, number=10)
 def convert(df, column_name):