2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3078 5.6.4 What’s new in 0.25.0 (July 18, 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3081 5.7 Version 0.24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3528 5.16.3 Version 0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3535 5.17 Version 0.14 . . . • Fare: Indicating the fare. • Cabin: The cabin of passenger. • Embarked: The embarked category. 18 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.4.2 How do I

2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3083 5.6.4 What’s new in 0.25.0 (July 18, 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3086 5.7 Version 0.24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3533 5.16.3 Version 0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3540 5.17 Version 0.14 . . . • Fare: Indicating the fare. • Cabin: The cabin of passenger. • Embarked: The embarked category. 18 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.4.4 How do I

the DataFrame with multiple dtypes, DataFrame.to_numpy() is relatively expensive. In [18]: df2.to_numpy() Out[18]: array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 437482 -1.106914 2013-01-03 -1.323650 0.427355 0.835343 -0.000698 In [25]: df['20130102':'20130104'] 18 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.3 Out[25]: Essential basic functionality 41 pandas: powerful Python data analysis toolkit, Release 0.25.3 In [18]: df = pd.DataFrame({ ....: 'one': pd.Series(np.random.randn(3), index=['a', 'b', 'c']), ....: 'two':

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 18 IO Tools (Text, CSV, HDF5, ...) 357 18.1 CSV & Text files . . . . . . . . . . . . . . . . . . . . New pandas: powerful Python data analysis toolkit, Release 0.12.0 In [17]: html = df.to_html() In [18]: alist = pd.read_html(html, infer_types=True, index_col=0) In [19]: print df == alist[0] a b 0 D float16 E int32 dtype: object Forcing Date coercion (and setting NaT when not datelike) In [18]: s = Series([datetime(2001,1,1,0,0), ’foo’, 1.0, 1, ....: Timestamp(’20010104’), ’20010105’],dtype=’O’)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 1.7 v0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 1.8 v0.14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 18 Merge, join, and concatenate 547 18.1 Concatenating objects . . . . . . . . . . . . . . . . . . . Out[17]: 0 2013-01-01 1 2013-01-02 2 2013-01-03 3 2013-01-04 dtype: object In [18]: s.dt.strftime('%Y/%m/%d') Out[18]: 0 2013/01/01 1 2013/01/02 2 2013/01/03 3 2013/01/04 dtype: object The string

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 1.16 v0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 1.16.1 New Merging AsOf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 xv 18 Reshaping and Pivot Tables 791 18.1 Reshaping by pivoting DataFrame objects . . . . . . . . . . . 'two']] ....: In [17]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second']) In [18]: df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3], ....: 'B': np.arange(8)}, ....: index=index)

17 1.2.1.13 Other Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.2 Backwards incompatible API changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 1.15 v0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 1.15.1 New Merging AsOf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 18 Reshaping and Pivot Tables 787 18.1 Reshaping by pivoting DataFrame objects . . . . . . . . . . .

17 1.2.2.6 Indexing with a Boolean Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.2.7 PeriodIndex resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 1.18 v0.15.0 (October 18, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 1.18.1 New Merging AsOf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818 18 Reshaping and Pivot Tables 821 18.1 Reshaping by pivoting DataFrame objects . . . . . . . . . . .

25.0 Wes McKinney& PyData Development Team Jul 18, 2019 CONTENTS i ii pandas: powerful Python data analysis toolkit, Release 0.25.0 Date: Jul 18, 2019 Version: 0.25.0 Download documentation: PDF Python data analysis toolkit, Release 0.25.0 2 CONTENTS CHAPTER ONE WHAT’S NEW IN 0.25.0 (JULY 18, 2019) Warning: Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and 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 0.25.0 groupby aggregation (Deprecate

Python data analysis toolkit, Release 0.25.1 2 CONTENTS CHAPTER ONE WHAT’S NEW IN 0.25.0 (JULY 18, 2019) Warning: Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and 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 0.25.1 This type of aggregation is 'Lookup': {'TextField': 'Some text', (continues on next page) 6 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit, Release 0.25.1 (continued from previous page)