Page 544 - Python Data Science Handbook

display contexts, 218-220 facial recognition example, 442-445
factor plots, 319 for dimensionality reduction, 436
from an IPython shell, 219 handwritten digit example, 437-440,
from script, 219 440-442
histograms, binnings, and density, 245-249 manifold learning vs., 455
IPython notebook, 220 meaning of components, 438-439
joint distributions, 320 noise filtering, 440-442
labeling simple line plots, 230-232 strengths/weaknesses, 445
line colors and styles, 226-228 visualization with, 437
manual customization, 282-284 profiling
Matplotlib, 217 full scripts, 27
multiple subplots, 262-268 line-by-line, 28
of errors, 237-240 memory use, 29
pair plots, 317 projections (see map projections)
plot legends, 249-255 pseudo-cylindrical projections, 302
Seaborn, 311-313 Python
simple line plots, 224-232 installation considerations, xiv
simple scatter plots, 233-237 Python 2.x vs. Python 3, xiii
stylesheets for, 285-290 reasons for using, xii
text and annotation for, 268-275
three-dimensional, 290-298 Q
three-dimensional function, 241-245 query() method
ticks, 275-282 DataFrame.query() method, 213
two-dimensional function, 69 when to use, 214
various Python graphics libraries, 330 question mark (?), accessing IPython documen‐
plt.axes() function, 263-264 tation with, 3
plt.contour() function, 241-244 quicksort algorithm, 87
plt.GridSpec() function, 266-268
plt.imshow() function, 243-244 R
plt.legend() command, 249-254
plt.plot() function radial basis function, 412
color arguments, 226 rainfall statistics, 70
plt.scatter vs., 237 random forests
scatter plots with, 233-235 advantages/disadvantages, 432
plt.scatter() function classifying digits with, 430-432
plt.plot vs., 237 defined, 426
simple scatter plots with, 235-237 ensembles of estimators, 426-428
plt.subplot() function, 264 motivating with decision trees, 421-426
plt.subplots() function, 265 regression, 428
polynomial basis functions, 393 RandomizedPCA, 442
polynomial regression model, 366 rcParams dictionary, changing defaults via, 284
pop() method, 111 RdBu colormap, 258
population data, US, merge and join operations record arrays, 96
with, 154-158 reduce() method, 57
principal axes, 434-436 regression, 428-433
principal component analysis (PCA), 433-515 (see also specific forms, e.g.: linear regres‐
basics, 433-442 sion)
choosing number of components, 440 regression task
eigenfaces example, 442-445 defined, 332
machine learning, 335-338

526 | Index