option of downcasting the newly (or already) numeric data to a smaller dtype, which can conserve memory: In [391]: m = ['1', 2, 3] In [392]: pd.to_numeric(m, downcast='integer') # smallest signed int float64 sf 100 non-null float64 gidp 100 non-null float64 dtypes: float64(9), int64(11), object(3) memory usage: 18.1+ KB However, using to_string will return a string representation of the DataFrame in judge of this, given side-by-side code comparisons) This page is also here to offer a bit of a translation guide for users of these R packages. For transfer of DataFrame objects from pandas to R, one option

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

. . . . . . . . . . . . . . . . . 238 i 4 Frequently Asked Questions (FAQ) 241 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 4.2 Byte-Ordering 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 (GH10485) • Bug in read_csv when using a converter which generates a uint8 type (GH9266) • Bug causes memory leak in time-series line and area plot (GH9003) • Bug when setting a Panel sliced along the major

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

Excel 2007 XML documents using openpyxl 1.1.2 Performance improvements • Improve performance and memory usage of fillna on DataFrame • Can concatenate a list of Series along axis=1 to obtain a DataFrame MultiIndex.get_level_values can accept the level name 1.3.2 Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format. • Memory-efficent “sparse” versions of the standard data structures for storing data that is mostly missing

Excel 2007 XML documents using openpyxl 1.2.2 Performance improvements • Improve performance and memory usage of fillna on DataFrame • Can concatenate a list of Series along axis=1 to obtain a DataFrame MultiIndex.get_level_values can accept the level name 1.4.2 Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format. • Memory-efficent “sparse” versions of the standard data structures for storing data that is mostly missing

Excel 2007 XML documents using openpyxl 1.3.2 Performance improvements • Improve performance and memory usage of fillna on DataFrame • Can concatenate a list of Series along axis=1 to obtain a DataFrame MultiIndex.get_level_values can accept the level name 1.5.2 Performance improvements • Improve memory usage of DataFrame.describe (do not copy data unnecessarily) (PR #425) • Optimize scalar value lookups 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format. • Memory-efficent “sparse” versions of the standard data structures for storing data that is mostly missing