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 21.11.2 Old style constructor usage . . . . . . . . . . . . . . . .

TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 .dt accessor . . . . . . . . . . . . . . . . . 341 4 Frequently Asked Questions (FAQ) 343 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 4.2 Byte-Ordering . . . . . . . . . . 819 22.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 22.11.2 Old style constructor usage . . . . . . . . . . . . . . . .

TimedeltaIndex/Scalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 .dt accessor . . . . . . . . . . . . . . . . . 343 4 Frequently Asked Questions (FAQ) 345 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 4.2 Byte-Ordering . . . . . . . . . . 821 22.11.1 Memory Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 22.11.2 Old style constructor usage . . . . . . . . . . . . . . . .

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 21.12.2

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 21.11.2 Old style constructor usage . . . . . . . . . . . . . . . .

and end indices used for each window during the rolling aggregation. For more details and example usage, see the custom window rolling documentation 1.2.3 Converting to Markdown We’ve added to_markdown() now can read binary Excel (.xlsb) files by passing engine='pyxlsb'. For more details and example usage, see the Binary Excel files documentation. Closes GH8540. • The partition_cols argument in DataFrame text_col 3 non-null object float_col 3 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 152.0+ bytes pandas 1.0.0 In [34]: df = pd.DataFrame({"int_col": [1, 2, 3], ....: "text_col":

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 869 2.24.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 869 2.24.2 Using if/truth encouraged to read HTML Table Parsing gotchas. It explains issues surrounding the installa- tion and usage of the above three libraries. 1.4.2 Package overview pandas is a Python package providing fast, 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain

. . . . . . . . . . . . . . . . . 172 3 Frequently Asked Questions (FAQ) 175 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 3.2 PeriodIndex 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 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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 869 2.24.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 869 2.24.2 Using if/truth encouraged to read HTML Table Parsing gotchas. It explains issues surrounding the installa- tion and usage of the above three libraries. 1.4.2 Package overview pandas is a Python package providing fast, 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain

. . . . . . . . . . . . . . . . . 166 3 Frequently Asked Questions (FAQ) 169 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 3.2 PeriodIndex 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 behavior: