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

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

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sat, 02 Nov 2019 Prob 16:04:51 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== 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

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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 970 2.26.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 970 2.26.2 Using if/truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2744 4.9.2 Checking memory leaks with valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2745 4.10 Extending Cabin 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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969 2.26.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969 2.26.2 Using if/truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2744 4.9.2 Checking memory leaks with valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2745 4.10 Extending Cabin 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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 930 2.26.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 930 2.26.2 Using if/truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2665 4.9.2 Checking memory leaks with valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2666 4.10 Extending Cabin 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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 2.26.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 2.26.2 Using if/truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2839 4.9.2 Checking memory leaks with valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2840 4.10 Extending Cabin 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

(FAQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 2.26.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 2.26.2 Using if/truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2837 4.9.2 Checking memory leaks with valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2838 4.10 Extending Cabin 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