. . . . . . . . . . . . . . . . . . . . . . . . . . 246 6 10 Minutes to pandas 249 6.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 22 Categorical Data 649 22.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 2015) 51 pandas: powerful Python data analysis toolkit, Release 0.17.0 Out[54]: True For ease of creation of series of categorical data, we have added the ability to pass keywords when calling .astype()

. . . . . . . . . . . . . . . . . . . . . . . . . . 184 5 10 Minutes to pandas 187 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 21 Categorical Data 551 21.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications

. . . . . . . . . . . . . . . . . . . . . . . . . . 178 5 10 Minutes to pandas 181 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 21 Categorical Data 541 21.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be a transparent change with only very limited API implications

. . . . . . . . . . . . . . . . . . . . . . . . . . . 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

• Further performance tweaking of Series.__getitem__ for standard use cases • Avoid Index dict creation in some cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error pandas namespace. They all take the following arguments to specify either a static (full sample) or dynamic (moving window) regression: • window_type: ’full sample’ (default), ’expanding’, or rolling • window: nan, 0.606 , 1.3342]) 16.2 SparseList SparseList is a list-like data structure for managing a dynamic collection of SparseArrays. To create one, simply call the SparseList constructor with a fill_value

• Further performance tweaking of Series.__getitem__ for standard use cases • Avoid Index dict creation in some cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error pandas namespace. They all take the following arguments to specify either a static (full sample) or dynamic (moving window) regression: • window_type: ’full sample’ (default), ’expanding’, or rolling • window: nan, 0.606 , 1.3342]) 16.2 SparseList SparseList is a list-like data structure for managing a dynamic collection of SparseArrays. To create one, simply call the SparseList constructor with a fill_value

• Further performance tweaking of Series.__getitem__ for standard use cases • Avoid Index dict creation in some cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error pandas namespace. They all take the following arguments to specify either a static (full sample) or dynamic (moving window) regression: • window_type: ’full sample’ (default), ’expanding’, or rolling • window: nan, 0.606 , 1.3342]) 16.2 SparseList SparseList is a list-like data structure for managing a dynamic collection of SparseArrays. To create one, simply call the SparseList constructor with a fill_value

Customarily, we import as follows: In [1]: import numpy as np In [2]: import pandas as pd Object creation See the Intro to data structures section. Creating a Series by passing a list of values, letting 'full') Oftentimes when appending large amounts of data to a store, it is useful to turn off index creation for each append, then recreate at the end. In [514]: df_1 = pd.DataFrame(np.random.randn(10, 2) relax this and allow a user-specified truncation to occur. Pass min_itemsize on the first table creation to a-priori specify the minimum length of a particular string column. min_itemsize can be an integer

. . . . . . . . . . . . . . . . . . . . . . . . . . 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). •