. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146 26.1.5 More advanced techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 26.2 Using numba . . . . . . . . . . . . . . . . . . . 1176 30 pandas Ecosystem 1177 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177 30.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS

. . . . . . . . . . . . . . . . . . . 622 25 Pandas Ecosystem 623 25.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 25.2 Visualization general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS including: • Comparison with SQL, which should be useful for those familiar with SQL but still learning pandas. • Comparison with R, idiom translations from R to pandas. • Enhancing Performance, ways

make sharing data across data analysis languages easy. Parquet can use a variety of compression techniques to shrink the file size as much as possible while still maintaining good read performance. Parquet investigate how to speed up certain functions operating on pandas DataFrames using three different techniques: Cython, Numba and pandas.eval(). We will see a speed improvement of ~200 when we use Cython and concentrate our efforts here. More advanced techniques There is still hope for improvement. Here’s an example of using some more advanced Cython techniques: In [12]: %%cython ....: cimport cython .

general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS concentrate our efforts here. 19.1.5 More advanced techniques There is still scope for improvement, here’s an example of using some more advanced cython techniques: In [16]: %%cython ....: cimport cython .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1010 27.1.5 More advanced techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011 27.2 Using numba . . . . . . . . . . . . . . . . . . . 1045 31 pandas Ecosystem 1047 31.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 31.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 27.1.5 More advanced techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 27.2 Using numba . . . . . . . . . . . . . . . . . . . 1047 31 pandas Ecosystem 1049 31.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049 31.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1110 26.1.5 More advanced techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 26.2 Using numba . . . . . . . . . . . . . . . . . . . 1140 30 pandas Ecosystem 1141 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1141 30.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108 26.1.5 More advanced techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109 26.2 Using numba . . . . . . . . . . . . . . . . . . . 1138 30 pandas Ecosystem 1139 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 30.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS

..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: 322 Chapter 2. User Guide pandas: powerful Rowling 2005 29.99 None 2 web XQuery Kick Start Vaidyanathan Nagarajan 2003 49.99 None 3 web Learning XML Erik T. Ray 2003 39.95 paperback Read in the content of the “books.xml” file and pass it to

..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: In [321]: df = pd.read_xml("https://www Rowling 2005 29.99 None 2 web XQuery Kick Start Vaidyanathan Nagarajan 2003 49.99 None 3 web Learning XML Erik T. Ray 2003 39.95 paperback Read in the content of the “books.xml” file and pass it to