Bossche • MeeseeksMachine • Tom Augspurger • Will Ayd • William Ayd • jbrockmendel {{ header }} 4 Chapter 1. Whats new in 0.25.2 (October 15, 2019) CHAPTER TWO INSTALLATION The easiest way to install Dependency Minimum Version Notes BeautifulSoup4 4.6.0 HTML parser for read_html (see note) Jinja2 Conditional formatting with DataFrame.style PyQt4 Clipboard I/O PyQt5 Clipboard I/O PyTables 3.4 top-level read_html() function: Changed in version 0.23.0. • BeautifulSoup4 and html5lib • BeautifulSoup4 and lxml • BeautifulSoup4 and html5lib and lxml • Only lxml, although see HTML Table Parsing for

Series using the nullable integer dtype: In [3]: s = pd.Series([1, 2, None], dtype="Int64") In [4]: s Out[4]: 0 1 1 2 2 Length: 3, dtype: Int64 In [5]: s[2] Out[5]: Compared to np.nan, pd to arithmetic operations, pd.NA also propagates as “missing” or “unknown” in comparison operations: 4 Chapter 1. What’s new in 1.0.0 (January 29, 2020) pandas: powerful Python data analysis toolkit, Release powerful Python data analysis toolkit, Release 1.0.0 Package Minimum Version Changed beautifulsoup4 4.6.0 fastparquet 0.3.2 X gcsfs 0.2.2 lxml 3.8.0 matplotlib 2.2.2 numba 0.46.0 X openpyxl 2.5

Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4 Package overview 109 4.1 Data structures at a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 8.4 Panel4D (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 892 28.6 Panel4D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963

. . . . . . . . . . . . . . . . . . . . . 4 1.3 Bug fixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Getting started 5 2.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Creating example data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 4 API reference 897 4.1 Input/output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

powerful Python data analysis toolkit, Release 0.25.1 (continued from previous page) 3 dog 34.0 198.0 [4 rows x 3 columns] In [3]: animals.groupby("kind").agg( ...: min_height=pd.NamedAgg(column='height' accepted as well. In [4]: animals.groupby("kind").agg( ...: min_height=('height', 'min'), ...: max_height=('height', 'max'), ...: average_weight=('weight', np.mean), ...: ) ...: Out[4]: min_height max_height ...: ) ...: Out[5]: min_height max_height kind cat 9.1 9.5 dog 6.0 34.0 [2 rows x 2 columns] 4 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit, Release

3 pandas: powerful Python data analysis toolkit, Release 0.25.0 (continued from previous page) [4 rows x 3 columns] In [3]: animals.groupby("kind").agg( ...: min_height=pd.NamedAgg(column='height' accepted as well. In [4]: animals.groupby("kind").agg( ...: min_height=('height', 'min'), ...: max_height=('height', 'max'), ...: average_weight=('weight', np.mean), ...: ) ...: Out[4]: min_height max_height aggregation is the recommended alternative to the deprecated behavior when passing a dict to a Series 4 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit, Release

Out[3]: 0 2 1 3 2 NaN Length: 3, dtype: Int64 # comparison In [4]: s == 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[4]: 0 True 1 False 2 False Length: 3, dtype: bool # indexing \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[6]: ˓→ 0 NaN 1 4 2 NaN Length: 3, dtype: Int64 # coerce when needed In [7]: s + 0.01 \\\\\\\\\\\\\\\\\\\\\\\\\\\\ 'B': [1, 1, 3], 'C': list('aab')}) In [9]: df Out[9]: A B C 0 1 1 a (continues on next page) 4 Chapter 1. What’s New in 0.24.0 (January 25, 2019) pandas: powerful Python data analysis toolkit, Release

pandas.api.indexers.check_array_indexer . . . . . . . . . . . . . . . . . . . . . . . . . . . 2416 4 Development 2419 4.1 Contributing to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2779 5.8.2 Version 0.20.2 (June 4, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2782 5.8.3 Version 0.20 times, and time- indexed data. To introduction tutorial To user guide Straight to tutorial... 4 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.1.1 Data sets

pandas.api.indexers.check_array_indexer . . . . . . . . . . . . . . . . . . . . . . . . . . . 2416 4 Development 2419 4.1 Contributing to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2777 5.8.2 Version 0.20.2 (June 4, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2780 5.8.3 Version 0.20 times, and time- indexed data. To introduction tutorial To user guide Straight to tutorial... 4 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.1.0 Data sets

Data in Qt applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 4 Package overview 137 4.1 Data structures at a glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 8.4 Panel4D (Experimental) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981 28.6 Panel4D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056