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 text representations of DataFrame now show swapaxes,transpose,pop * __iter__,keys,__contains__,__len__,__neg__,__invert__ * convert_objects,as_blocks,as_matrix,values * __getstate__,__setstate__ (compat remains in frame/panel) * __getattr__,__setattr__ non-unique indexing in series via .ix/.loc and __getitem__ (GH4246) – Fixed non-unique indexing memory allocation issue with .ix/.loc (GH4280) • DataFrame.from_records did not accept empty recarrays

. . . . . . . . . . . . . . . . . . . 172 3 Frequently Asked Questions (FAQ) 175 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 3.2 PeriodIndex values for columns that contain NA values and have dtype object (GH8778). 1.1.3 Performance • Reduce memory usage when skiprows is an integer in read_csv (GH8681) • Performance boost for to_datetime conversions dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect

. . . . . . . . . . . . . . . . . . . 166 3 Frequently Asked Questions (FAQ) 169 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 3.2 PeriodIndex dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect incorrect as it didn’t show the usage for the memory occupied by the underling data array. (GH8456) In [26]: dfi = DataFrame(1,index=pd.MultiIndex.from_product([[’a’],range(1000)]),columns=[’A’]) previous

database URI. You only need to create the engine once per database you are connecting to. For an in-memory sqlite database: 12 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release sqlalchemy import create_engine # Create your connection. In [44]: engine = create_engine(’sqlite:///:memory:’) This engine can then be used to write or read data to/from this database: In [45]: df = pd.DataFrame({’A’: axis frequency (GH5955) • Bug in downcasting inference with empty arrays (GH6733) • Bug in obj.blocks on sparse containers dropping all but the last items of same for dtype (GH6748) • Bug in unpickling

. . . . . . . . . . . . . . . . . 238 i 4 Frequently Asked Questions (FAQ) 241 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 4.2 Byte-Ordering supports arithmetic with np.ndarray (GH10638) • Support pickling of Period objects (GH10439) • .as_blocks will now take a copy optional argument to return a copy of the data, default is to copy (no change presence of the HTTP Content-Encoding header in the response (GH8685) • Enable writing Excel files in memory using StringIO/BytesIO (GH7074) • Enable serialization of lists and dicts to strings in ExcelWriter

TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 .dt . . . . . . . . . . . . . . . . . . . 341 4 Frequently Asked Questions (FAQ) 343 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 4.2 Byte-Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 22.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 22.11

TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 .dt . . . . . . . . . . . . . . . . . . . 343 4 Frequently Asked Questions (FAQ) 345 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 4.2 Byte-Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 22.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 22.11

8 Pandas Google BigQuery support has moved . . . . . . . . . . . . . . . . . . . . . 27 1.3.2.9 Memory Usage for Index is more Accurate . . . . . . . . . . . . . . . . . . . . . . 27 1.3.2.10 DataFrame TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 1.16.1.3 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 1.16.1.4 .dt accessor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 908 21.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 908 21.11

8 Pandas Google BigQuery support has moved . . . . . . . . . . . . . . . . . . . . . 56 1.5.2.9 Memory Usage for Index is more Accurate . . . . . . . . . . . . . . . . . . . . . . 56 1.5.2.10 DataFrame TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 vi 1.18.1.3 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 1.18.1.4 .dt accessor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942 21.12.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942 21.12

8 Pandas Google BigQuery support has moved . . . . . . . . . . . . . . . . . . . . . 26 1.2.2.9 Memory Usage for Index is more Accurate . . . . . . . . . . . . . . . . . . . . . . 26 i 1.2.2.10 DataFrame TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 1.15.1.3 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 1.15.1.4 .dt accessor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904 21.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904 21.11