. . . . . . . . . . . . . . . . . . . . . . . . . . 246 6 10 Minutes to pandas 249 6.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 22 Categorical Data 649 22.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now

. . . . . . . . . . . . . . . . . . . . . . . . . . 394 5 10 Minutes to pandas 397 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 8.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 878 21 Categorical Data 879 21.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879

. . . . . . . . . . . . . . . . . . . . . . . . . . 396 5 10 Minutes to pandas 399 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 882 21 Categorical Data 883 21.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883

. . . . . . . . . . . . . . . . . . . . . . . . . . 350 6 10 Minutes to pandas 353 6.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 22 Categorical Data 797 22.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797

. . . . . . . . . . . . . . . . . . . . . . . . . . 348 6 10 Minutes to pandas 351 6.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 9.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 9.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 22 Categorical Data 795 22.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795

. . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5 10 Minutes to Pandas 81 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 0.12.0 dtype(’O’) 1.2.7 API changes • Added to_series() method to indicies, to facilitate the creation of indexers (GH3275) • HDFStore – added the method select_column to select a single column from B C foo bar bar one -0.195183 -1.332316 1.684194 two -0.137506 2.138582 0.118417 Multi-table creation via append_to_multiple and selection via select_as_multiple can create/select from multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . 425 5 10 Minutes to pandas 427 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 8.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 914 21 Categorical Data 915 21.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915

Improved performance of Period constructor, additionally benefitting PeriodArray and PeriodIndex creation (GH24084, GH24118) • Improved performance of tz-aware DatetimeArray binary operations (GH24491) change, where it now correctly works per group (GH21200, GH21235). • Bug preventing hash table creation with very large number (2^32) of rows (GH22805) • Bug in groupby when grouping on categorical causes Customarily, we import as follows: In [1]: import numpy as np In [2]: import pandas as pd 3.2.1 Object Creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas

. . . . . . . . . . . . . . . . . . . . . . . . . . 110 5 10 Minutes to Pandas 113 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 index type, Float64Index. This will be automatically created when passing floating values in index creation. This enables a pure label-based slicing paradigm that makes [],ix,loc for scalar indexing and slicing rows are NOT written), also settable via the option io.hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.2.7 DataFrame repr Changes The HTML and plain

. . . . . . . . . . . . . . . . . . . . . . . . . . 138 5 10 Minutes to Pandas 141 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 251 250 D1 253 252 255 254 [24 rows x 4 columns] You can use a pd.IndexSlice to shortcut the creation of these slices In [55]: idx = pd.IndexSlice In [56]: df.loc[idx[:,:,[’C1’,’C3’]],idx[:,’foo’]] DataFrame (GH6525) • Regression from 0.13 in the treatment of numpy datetime64 non-ns dtypes in Series creation (GH6529) • .names attribute of MultiIndexes passed to set_index are now preserved (GH6459). •