In [2]: df[’A’].iloc[0] = np.nan In [3]: df Out[3]: A 0 NaN 1 bar 2 bah 3 foo 4 bar [5 rows x 1 columns] The recommended way to do this type of assignment is: In [4]: df = DataFrame(dict(A = np ’bar’]))) In [5]: df.ix[0,’A’] = np.nan In [6]: df Out[6]: A 0 NaN 1 bar 2 bah 3 foo 4 bar [5 rows x 1 columns] 1.1.1 Output Formatting Enhancements • df.info() view now display dtype info per column In [17]: pd.set_option(’show_dimensions’, True) In [18]: df Out[18]: 0 1 0 1 2 1 3 4 [2 rows x 2 columns] • The ArrayFormatter for datetime and timedelta64 now intelligently limit precision based

for all others (GH7011, GH6762, GH7015, GH7210) x = pd.Series(np.random.rand(10) > 0.5) y = True x + y # warning generated: should do x | y instead x / y # this raises because it doesn’t make sense show_dimensions’, ’truncate’): ....: print(dfd) ....: 0 ... 4 0 0 ... 4 .. .. ... .. 4 20 ... 24 [5 rows x 5 columns] # will not show dimensions since it is not truncated In [21]: with pd.option_context(’display groupby(’A’) In [24]: g.head(1) # filters DataFrame Out[24]: A B 0 1 2 2 5 6 In [25]: g.apply(lambda x: x.head(1)) # used to simply fall-through Out[25]: A B A 1 0 1 2 5 2 5 6 • groupby head and tail

up the SciPy stack (IPython, NumPy, Matplotlib, ) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing. After running the installer 566418 8.148927 25 10.0 2013-01-06 -1.718066 -2.939324 9.221163 30 15.0 In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A 1.833510 B 3.196941 C 2.602139 D 0.000000 F 4.000000 dtype: float64 Histogramming In [136]: ts = ts.cumsum() In [137]: ts.plot() Out[137]: x124144910> 34 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25

DataFrame({'x': range(5), ....: 't': pd.date_range('2000-01-01', periods=5)}) ....: In [58]: df.reindex([0.1, 1.9, 3.5], ....: method='nearest', ....: tolerance=0.2) ....: Out[58]: t x 0.1 2000-01-01 reindex(pd.to_datetime(['1999-12-31']), ....: method='nearest', ....: tolerance='1 day') ....: Out[60]: x 1999-12-31 0 tolerance is also exposed by the lower level Index.get_indexer and Index.get_loc methods display.precision. In [1]: pd.set_option('display.precision', 2) In [2]: pd.DataFrame({'x': [123.456789]}) Out[2]: x 0 123.5 If interpreting precision as “significant figures” this did work for scientific

Name attribute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 x 8.2 DataFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . improvements of note in each release. 1.1 v0.20.3 (July 7, 2017) This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade to downstream packages’ tests suites (GH16680) 1.1.1.1 Conversion • Bug in pickle compat prior to the v0.20.x series, when UTC is a timezone in a Series/DataFrame/Index (GH16608) • Bug in Series construction when

Column selection, addition, deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 x 8.2.9 Assigning New Columns in Method Chains . . . . . . . . . . . . . . . . . . . . . . . . . . 469 improvements of note in each release. 1.1 v0.20.2 (June 4, 2017) This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend DataFrame (GH15953) • Bug in read_csv() in which tarfile object inputs were raising an error in Python 2.x for the C engine (GH16530) • Bug where DataFrame.to_html() ignored the index_names parameter (GH16493)

sum greater than 2. In [36]: sf = Series([1, 1, 2, 3, 3, 3]) In [37]: sf.groupby(sf).filter(lambda x: x.sum() > 2) 3 3 4 3 5 3 dtype: int64 The argument of filter must a function that, applied to the = DataFrame({’A’: np.arange(8), ’B’: list(’aabbbbcc’)}) In [39]: dff.groupby(’B’).filter(lambda x: len(x) > 2) A B 2 2 b 3 3 b 4 4 b 5 5 b Alternatively, instead of dropping the offending groups pandas: powerful Python data analysis toolkit, Release 0.12.0 In [40]: dff.groupby(’B’).filter(lambda x: len(x) > 2, dropna=False) A B 0 NaN NaN 1 NaN NaN 2 2 b 3 3 b 4 4 b 5 5 b 6 NaN NaN 7 NaN NaN

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 x 5.11 Plotting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . note in each release. 1.1 v0.21.1 (December 12, 2017) This is a minor bug-fix release in the 0.21.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend compressed files. (GH17798) Various enhancements • Improved the import time of pandas by about 2.25x. (GH16764) • Support for PEP 519 – Adding a file system path protocol on most readers (e.g. read_csv())

DataFrame({'x': ['abc', None, 'def'], ....: 'y': [1, 2, np.nan], ....: 'z': [True, False, True]}) ....: In [19]: df Out[19]: x y z 0 abc 1.0 True 1 None 2.0 False 2 def NaN True [3 rows x 3 columns] Out[20]: x object y float64 z bool Length: 3, dtype: object In [21]: converted = df.convert_dtypes() In [22]: converted Out[22]: x y z 0 abc 1 True 1 2 False 2 def True [3 rows x 3 columns] columns] In [23]: converted.dtypes Out[23]: x string y Int64 z boolean Length: 3, dtype: object This is especially useful after reading in data using readers such as read_csv() and read_excel(). See

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 higher. See Plan for dropping Python 2.7 for 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', aggfunc='min') 3]: ˓→ min_height max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75 [2 rows x 3 columns] Pass the desired columns names as the **kwargs to .agg. The values of **kwargs should be