/1/2000’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) 6.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to Unbiased variance skew Unbiased skewness (3rd moment) kurt Unbiased kurtosis (4th moment) quantile Sample quantile (value at %) cumsum Cumulative sum cumprod Cumulative product cummax Cumulative maximum rolling_skew Unbiased skewness (3rd moment) rolling_kurt Unbiased kurtosis (4th moment) rolling_quantile Sample quantile (value at %) rolling_apply Generic apply rolling_cov Unbiased covariance (binary) rolling_corr

/1/2000’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) 6.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to Unbiased variance skew Unbiased skewness (3rd moment) kurt Unbiased kurtosis (4th moment) quantile Sample quantile (value at %) cumsum Cumulative sum cumprod Cumulative product cummax Cumulative maximum rolling_skew Unbiased skewness (3rd moment) rolling_kurt Unbiased kurtosis (4th moment) rolling_quantile Sample quantile (value at %) rolling_apply Generic apply rolling_cov Unbiased covariance (binary) rolling_corr

/1/2000’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) 6.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to Unbiased variance skew Unbiased skewness (3rd moment) kurt Unbiased kurtosis (4th moment) quantile Sample quantile (value at %) cumsum Cumulative sum cumprod Cumulative product cummax Cumulative maximum rolling_skew Unbiased skewness (3rd moment) rolling_kurt Unbiased kurtosis (4th moment) rolling_quantile Sample quantile (value at %) rolling_apply Generic apply rolling_cov Unbiased covariance (binary) rolling_corr

[5]: store.get_storer(’df’).attrs.my_attribute {’A’: 10} 6.10 Computation Numerical integration (sample-based) of a time series 6.11 Miscellaneous The Timedeltas docs. Operating with timedeltas Create 0’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) ...: 8.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to specialized cython routines that are especially fast when dealing with arrays that have nans. Here is a sample (using 100 column x 100,000 row DataFrames): Operation 0.11.0 (ms) Prior Vern (ms) Ratio to Prior

store.get_storer(’df’).attrs.my_attribute Out[9]: {’A’: 10} 7.10 Computation Numerical integration (sample-based) of a time series 144 Chapter 7. Cookbook pandas: powerful Python data analysis toolkit, 0’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) ...: 9.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to specialized cython routines that are especially fast when dealing with arrays that have nans. Here is a sample (using 100 column x 100,000 row DataFrames): Operation 0.11.0 (ms) Prior Version (ms) Ratio to Prior

both of which are supported by pandas’ IO facilities. 7.10 Computation Numerical integration (sample-based) of a time series 7.11 Miscellaneous The Timedeltas docs. Operating with timedeltas Create 0’, periods=5), ...: minor_axis=[’A’, ’B’, ’C’, ’D’]) ...: 9.1 Head and Tail To view a small sample of a Series or DataFrame object, use the head and tail methods. The default number of elements to specialized cython routines that are especially fast when dealing with arrays that have nans. Here is a sample (using 100 column x 100,000 row DataFrames): Operation 0.11.0 (ms) Prior Version (ms) Ratio to Prior

were incorrect (GH10278 and GH9760 ) • Bug in .sample() where returned object, if set, gives unnecessary SettingWithCopyWarning (GH10738) • Bug in .sample() where weights passed as Series were not aligned including graphical examples to make it easier to un- derstand each operations, see here • New method sample for drawing random samples from Series, DataFrames and Panels. See here • The default Index printing make string operations easier, see here What’s new in v0.16.1 • Enhancements – CategoricalIndex – Sample – String Methods Enhancements – Other Enhancements • API changes – Deprecations • Index Representation

CategoricalIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 String function instead of storing it. Call where directly to get the previous behavior (GH13299). • Calls to .sample() will respect the random seed set via numpy.random.seed(n) (GH13161) • Styler.apply is now more were incorrect (GH10278 and GH9760 ) • Bug in .sample() where returned object, if set, gives unnecessary SettingWithCopyWarning (GH10738) • Bug in .sample() where weights passed as Series were not aligned

CategoricalIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 String function instead of storing it. Call where directly to get the previous behavior (GH13299). • Calls to .sample() will respect the random seed set via numpy.random.seed(n) (GH13161) • Styler.apply is now more were incorrect (GH10278 and GH9760 ) • Bug in .sample() where returned object, if set, gives unnecessary SettingWithCopyWarning (GH10738) • Bug in .sample() where weights passed as Series were not aligned

CategoricalIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 1.12.1.2 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 1.12.1.3 String a concise way by using agg() and transform(). The full documentation is here (GH1623). Here is a sample In [1]: df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'], ...: index=pd.date_range('1/1/2000' function instead of storing it. Call where directly to get the previous behavior (GH13299). • Calls to .sample() will respect the random seed set via numpy.random.seed(n) (GH13161) • Styler.apply is now more