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

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

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

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

..: 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

. . . . . . . . . . . . . . . . . . . 895 30 pandas Ecosystem 897 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897 30.2 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. 136 Chapter 1. What’s New pandas:

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

substantial projects that you feel should be on this list, please let us know. 5.1 Statistics and machine learning 5.1.1 Statsmodels Statsmodels is the prominent Python “statistics and econometrics library” and pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering “primi- tives”. Users can contribute their own primitives in computing. 5.6.3 Dask-ML Dask-ML enables parallel and distributed machine learning using Dask alongside existing machine learning libraries like Scikit-Learn, XGBoost, and TensorFlow. 5.6.4 Koalas Koalas