Page 547 - Python Data Science Handbook

Python vs. Pandas, 188-192 specialized ufuncs, 56
resampling and converting frequencies, specifying output, 56
197-199 trigonometric functions, 54
rolling statistics, 201 unstack() method, 130
Seattle bicycle counts example, 202-209 unsupervised learning
time-shifts, 199-201 clustering, 338-339, 353
typed arrays, 189 defined, 332
Timedelta type, 193 dimensionality reduction, 261, 340-342,
Timestamp type, 193 352, 355
timestamps, indexing data by, 192 PCA (see principal component analysis)
timing, of code, 12, 25-27
transform() method, 167 V
transforms validation (see model validation)
modifying, 270-272 validation curves, 366-370
text position and, 270-272 variables
triangulated surface plots, 295-298 dynamic typing, 34
trigonometric functions, 54 passing to and from shell, 18
tshift() function, 199-201 variance, in bias–variance trade-off, 364-366
two-fold cross-validation, 361 vectorized operations, 63
vectorized string operations, 178-188
U basics, 178
ufuncs (see universal functions) indicator variables, 183
unary ufuncs, 52 methods similar to Python string methods,
underfitting, 364, 371 180
underscore (_) shortcut, 15 methods using regular expressions, 181
universal functions (ufuncs), 50-58 recipe database example, 184-188
absolute value, 54 tables of, 180-184
advanced features, 56 vectorized item access and slicing, 183
aggregates, 57 Vega/Vega-Lite, 330
array arithmetic, 52 violin plot, 327
basics, 51 viridis colormap, 258
comparison operators as, 71-73 Vispy, 330
exponentials, 55 visualization software (see Matplotlib) (see Sea‐
index alignment, 116-118 born)
index preservation, 115
logarithms, 55 W
operating on data in Pandas, 115-127 Wickham, Hadley, 161
operations between DataFrame and Series, wildcard matching, 7
118 wireframe plot, 293
outer products, 58 word counts, 377-378
slowness of Python loops, 50

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