09:10:12-05:00 dtype: object In [52]: stz.dt.tz Out[52]: STD> You can also chain these types of operations: In [53]: s.dt.tz_localize(’UTC’).dt.tz_convert(’US/Eastern’) float64 • rolling_max(), rolling_min(), rolling_sum(), rolling_mean(), rolling_median(), rolling_std(), rolling_var(), rolling_skew(), rolling_kurt(), rolling_quantile(), rolling_cov(), rolling_corr() backwards-compatible. (GH8279) • Documented the ddof argument to expanding_var(), expanding_std(), rolling_var(), and rolling_std(). These functions’ support of a ddof argument (with a default value of 1) was previously

09:10:12-05:00 dtype: object In [52]: stz.dt.tz Out[52]: STD> You can also chain these types of operations: In [53]: s.dt.tz_localize(’UTC’).dt.tz_convert(’US/Eastern’) float64 • rolling_max(), rolling_min(), rolling_sum(), rolling_mean(), rolling_median(), rolling_std(), rolling_var(), rolling_skew(), rolling_kurt(), rolling_quantile(), rolling_cov(), rolling_corr() backwards-compatible. (GH8279) • Documented the ddof argument to expanding_var(), expanding_std(), rolling_var(), and rolling_std(). These functions’ support of a ddof argument (with a default value of 1) was previously

the z-score across the major_axis In [46]: result = panel.apply( ....: lambda x: (x-x.mean())/x.std(), ....: axis=’major_axis’) ....: In [47]: result Out[47]: Dimensions: Panel apply() operating on cross-sectional slabs. (GH1148) In [49]: f = lambda x: ((x.T-x.mean(1))/x.std(1)).T In [50]: result = panel.apply(f, axis = [’items’,’major_axis’]) In [51]: result Out[51]: std, var, skew, kurt, corr, and cov 36 Chapter 1. What’s New pandas: powerful Python data analysis toolkit

can accept database URI as con parameter (GH10214) • read_sql_table will now allow reading from views (GH10750). • Enable writing complex values to HDFStores when using the table format (GH10447) • 6.774e-136 =============================================================================== coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------- categorical type Series with dropna=True (GH9443) • Fixed mising numeric_only option for DataFrame.std/var/sem (GH9201) • Support constructing Panel or Panel4D with scalar data (GH8285) • Series text

describe() Out[38]: B A 1 count 1.000000 mean 4.000000 std NaN min 4.000000 25% 4.000000 50% 4.000000 75% 4.000000 ... ... 5 mean 7.000000 std 1.414214 min 6.000000 25% 6.500000 50% 7.000000 75% Out[42]: A B 0 count 2 1.000000 mean 1 4.000000 std 0 NaN min 1 4.000000 25% 1 4.000000 50% 1 4.000000 75% 1 4.000000 ... .. ... 1 mean 5 7.000000 std 0 1.414214 min 5 6.000000 25% 5 6.500000 50% coherence with chained indexing and slicing; add _is_view property to NDFrame to correctly predict views; mark is_copy on xs only if its an actual copy (and not a view) (GH7084) • Bug in DatetimeIndex creation

single DataFrame . . . . . . . . . . . . . . . . . . . . . . 1021 24.1.23 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021 24.1.24 Specifying the parser . . . . . . . . . . . . . . . . . . . . . . . . 1767 xxxix 34.12.1.6 pandas.core.window.Rolling.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1767 34.12.1.7 pandas.core.window.Rolling.min var . . . . . . . . . . . . . . . . . . . . . . . . . . 1772 34.12.2.6 pandas.core.window.Expanding.std . . . . . . . . . . . . . . . . . . . . . . . . . . 1772 34.12.2.7 pandas.core.window.Expanding.min

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 921 25.1.21 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 922 25.1.22 Specifying the parser engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1706 pandas.core.window.Rolling.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1707 pandas.core.window.Rolling.min var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Expanding.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Expanding.min

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 923 25.1.21 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924 25.1.22 Specifying the parser engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1712 pandas.core.window.Rolling.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1712 pandas.core.window.Rolling.min var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1716 pandas.core.window.Expanding.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1717 pandas.core.window.Expanding.min

single DataFrame . . . . . . . . . . . . . . . . . . . . . . 1056 24.1.23 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056 24.1.24 Specifying the parser . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1905 34.15.1.6 pandas.core.window.Rolling.std . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1905 34.15.1.7 pandas.core.window.Rolling.min var . . . . . . . . . . . . . . . . . . . . . . . . . . 1909 34.15.2.6 pandas.core.window.Expanding.std . . . . . . . . . . . . . . . . . . . . . . . . . . 1909 34.15.2.7 pandas.core.window.Expanding.min

to performing these operations on, for example, a DataFrame of slice objects: – sum, prod, mean, std, var, skew, kurt, corr, and cov • read_html now defaults to None when reading, and falls back on bs4 stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to sup- port iteration on select and select_as_multiple (GH3076) • You can now select A bar count 3.000000 mean 0.454566 std 0.129985 min 0.359373 25% 0.380519 50% 0.401666 75% 0.502163 max 0.602661 foo count 5.000000 mean -0.450546 std 0.318867 min -0.904623 25% -0.463909