Page 545 - Python Data Science Handbook

regular expressions, 181 constructing, 101
regularization, 396-400 data indexing/selection in, 107-110
lasso regularization, 399 DataFrame as dictionary of, 110-112
ridge regression, 398 DataFrame object constructed from, 104
relational algebra, 146 DataFrame object constructed from dictio‐
resample() method, 197-199 nary of, 105
reset_index() method, 139 generalized NumPy array, 99
reshaping, 47 hierarchical indexing in, 128-141
ridge regression (L2 regularization), 398 index alignment in, 116
right join, 153 indexer attributes, 109
right_index keyword, 151-152 multiply indexed, 134
rolling statistics, 201 one-dimensional array, 108
runtime configuration (rc), 284 operations between DataFrame and, 118
shell, IPython
S basics, 16
scatter plots (see simple scatter plots) command-line commands, 18
Scikit-Learn package, 331, 343-346 commands, 16-19
API (see Estimator API) keyboard shortcuts in, 8
basics, 343-359 launching, 2
data as table, 343 magic commands, 19
data representation in, 343-346 passing values to and from, 18
Estimator API, 346-354 shift() function, 199-201
features matrix, 344 shortcuts
handwritten digit application, 354-358 accessing previous output, 15
support vector classifier, 408-411 command history, 9
target array, 344-345 IPython shell, 8-31
scipy.special submodule, 56 navigation, 8
script text entry, 9
plotting from, 219 simple histograms, 245-246
profiling, 27 simple line plots
Seaborn axes limits for, 228-230
bar plots, 321 labeling, 230-232
datasets and plot types, 313-329 line colors and styles, 226-228
faceted histograms, 318 Matplotlib, 224-232
factor plots, 319 simple (Matplotlib), 224-232
histograms, KDE, and densities, 314-317 simple linear regression, 390-392
joint distributions, 320 simple scatter plots
marathon finishing times example, 322-329 California city populations, 249-254
Matplotlib vs., 311-313 Matplotlib, 233-237
pair plots, 317 plt.plot, 233-235
stylesheet, 289 plt.plot vs. plt.scatter, 237
visualization with, 311-313 plt.scatter, 235-237
Seattle, bicycle traffic prediction in slice() operation, 183
linear regression, 400-405 slicing
time series, 202-209 MultiIndex with sorted/unsorted indices,
Seattle, rainfall statistics in, 70 137
semi-supervised learning, 333 NumPy arrays, 44-47
Series object (Pandas), 99-102 NumPy arrays: accessing subarrays, 44
as dictionary, 100, 107

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