569605 K1NRi 0.875906 -2.211372 0.974466 -2.006747 Djq0N -0.410001 -0.078638 0.545952 -1.219217 J9Ha4 -1.226825 0.769804 -1.281247 -0.727707 In [631]: concat([df.ix[:7, [’a’, ’b’]], df.ix[2:-2, [’c’]] axis=1) Out[631]: a b c d 4Kh9a -1.413681 1.607920 1.024180 0.569605 Djq0N NaN NaN NaN -1.219217 J9Ha4 NaN NaN NaN -0.727707 K1NRi NaN NaN 0.974466 -2.006747 Pipp0 0.410835 0.813850 0.132003 -0.827317 413681 1.607920 1.024180 0.569605 K1NRi NaN NaN 0.974466 -2.006747 Djq0N NaN NaN NaN -1.219217 J9Ha4 NaN NaN NaN -0.727707 11.1.2 Concatenating using append A useful shortcut to concat are the append

support pow or mod with non-scalars. (GH3765) • Series and DataFrame now have a mode() method to calculate the statistical mode(s) by axis/Series. (GH5367) • Chained assignment will now by default warn if if the user is assigning to a copy. This can be changed with the option mode.chained_assignment, allowed options are raise/warn/None. See the docs. In [5]: dfc = DataFrame({’A’:[’aaa’,’bbb’,’ccc’],’B’:[1 warn argument from open. Instead a PossibleDataLossError exception will be raised if you try to use mode=’w’ with an OPEN file handle (GH4367) • allow a passed locations array or mask as a where condition

values mad Mean absolute deviation median Arithmetic median of values min Minimum max Maximum mode Mode abs Absolute Value prod Product of values std Bessel-corrected sample standard deviation var 'd' Note: idxmin and idxmax are called argmin and argmax in NumPy. Value counts (histogramming) / mode The value_counts() Series method and top-level function computes a histogram of a 1D array of values frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame: In [126]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) In [127]: s5.mode() Out[127]: 0 3 1 7 dtype: int64 In

values mad Mean absolute deviation median Arithmetic median of values min Minimum max Maximum mode Mode abs Absolute Value prod Product of values std Bessel-corrected sample standard deviation var 'd' Note: idxmin and idxmax are called argmin and argmax in NumPy. Value counts (histogramming) / mode The value_counts() Series method and top-level function computes a histogram of a 1D array of values frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame: In [126]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) In [127]: s5.mode() Out[127]: 0 3 1 7 dtype: int64 In

values mad Mean absolute deviation median Arithmetic median of values min Minimum max Maximum mode Mode abs Absolute Value prod Product of values std Bessel-corrected sample standard deviation var 'd' Note: idxmin and idxmax are called argmin and argmax in NumPy. Value counts (histogramming) / mode The value_counts() Series method and top-level function computes a histogram of a 1D array of values frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame: In [126]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) In [127]: s5.mode() Out[127]: 0 3 1 7 dtype: int64 In

• Bug in Panel indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue: 2 6 3 5 dtype: float64 • rolling_window() now normalizes the weights properly in rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about resulting file will be broken (GH7761) • SettingWithCopy raise/warnings (according to the option mode.chained_assignment) will now be issued when setting a value on a sliced mixed-dtype DataFrame using

1 NaN NaN 2 2 NaN In [28]: df_with_missing.to_hdf('file.h5', 'df_with_missing', format='table', mode='w') pd.read_hdf('file.h5', 'df_with_missing') Out [28]: col1 col2 0 0 1 2 2 NaN New Behavior: In [80]: df_with_missing.to_hdf('file.h5', ....: 'df_with_missing', ....: format='table', ....: mode='w') ....: In [81]: pd.read_hdf('file.h5', 'df_with_missing') Out[81]: col1 col2 0 0 1 1 NaN • Bug in Panel indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue:

• Bug in Panel indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue: 2 6 3 5 dtype: float64 • rolling_window() now normalizes the weights properly in rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about resulting file will be broken (GH7761) • SettingWithCopy raise/warnings (according to the option mode.chained_assignment) will now be issued when setting a value on a sliced mixed-dtype DataFrame using

values mad Mean absolute deviation median Arithmetic median of values min Minimum max Maximum mode Mode abs Absolute Value prod Product of values std Bessel-corrected sample standard deviation var 'd' Note: idxmin and idxmax are called argmin and argmax in NumPy. Value counts (histogramming) / mode The value_counts() Series method and top-level function computes a histogram of a 1D array of values frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame: In [126]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7]) In [127]: s5.mode() Out[127]: 0 3 1 7 dtype: int64 In

exception if labels not in given in level (GH8594) • 1.9.12 I/O • read_csv() now accepts binary mode file buffers when using the Python csv engine (GH23779) • Bug in DataFrame.to_json() where using values mad Mean absolute deviation median Arithmetic median of values min Minimum max Maximum mode Mode abs Absolute Value prod Product of values std Bessel-corrected sample standard deviation var 'd' Note: idxmin and idxmax are called argmin and argmax in NumPy. Value counts (histogramming) / mode The value_counts() Series method and top-level function computes a histogram of a 1D array of values