(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • pandas: powerful Python data analysis toolkit, Release 0.14.0 df.plot(kind=’barh’, stacked=True) • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (GH563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • pandas: powerful Python data analysis toolkit, Release 0.15.2 df.plot(kind=’barh’, stacked=True) • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (GH563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • pandas: powerful Python data analysis toolkit, Release 0.15.1 df.plot(kind=’barh’, stacked=True) • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (GH563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • pandas: powerful Python data analysis toolkit, Release 0.13.1 df.plot(kind=’barh’, stacked=True) • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (GH563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames

DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin, cos, exp, log, expm1, log1p, sqrt, sinh, cosh, tanh, arcsin, arccos, arctan, arccosh, arcsinh, arctanh, abs and arctan2 # sm.poisson takes (formula, data) In [3]: (bb.query('h > 0') ...: .assign(ln_h = lambda df: np.log(df.h)) ...: .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)') ...: .fit() ...: .summary() Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:59:49 Log-Likelihood: -143.91 converged: True LL-Null: -460.91 LLR p-value: 6.774e-136 ==================

stacked=True) 4 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.7.3 • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (PR563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames Release 0.7.3 5.2.11 DataFrame interoperability with NumPy functions Elementwise NumPy ufuncs (log, exp, sqrt, ...) and various other NumPy functions can be used with no issues on DataFrame, assuming

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • v.0.7.3 (April 12, 2012) 51 pandas: powerful Python data analysis toolkit, Release 0.12.0 • Add log x and y scaling options to DataFrame.plot and Series.plot • Add kurt methods to Series and DataFrame attribute-based item access to Panel and add IPython completion (GH563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames

DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin, cos, exp, log, expm1, log1p, sqrt, sinh, cosh, tanh, arcsin, arccos, arctan, arccosh, arcsinh, arctanh, abs and arctan2 # sm.poisson takes (formula, data) In [3]: (bb.query('h > 0') ...: .assign(ln_h = lambda df: np.log(df.h)) ...: .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)') ...: .fit() ...: .summary() Residuals: 63 Method: MLE Df Model: 4 Date: Don, 03 Nov 2016 Pseudo R-squ.: 0.6878 Time: 17:08:14 Log-Likelihood: -143.91 converged: True LL-Null: -460.91 LLR p-value: 6.774e-136 ==================

DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin, cos, exp, log, expm1, log1p, sqrt, sinh, cosh, tanh, arcsin, arccos, arctan, arccosh, arcsinh, arctanh, abs and arctan2 # sm.poisson takes (formula, data) In [3]: (bb.query('h > 0') ...: .assign(ln_h = lambda df: np.log(df.h)) ...: .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)') ...: .fit() ...: .summary() Residuals: 63 Method: MLE Df Model: 4 Date: Son, 02 Okt 2016 Pseudo R-squ.: 0.6878 Time: 17:15:45 Log-Likelihood: -143.91 converged: True LL-Null: -460.91 LLR p-value: 6.774e-136 ==================

attribute-based item access to Panel and add IPython completion (PR563) • Add logy option to Series.plot for log-scaling on the Y axis • Add index and header options to DataFrame.to_string • Can pass multiple DataFrames Release 0.7.1 5.2.11 DataFrame interoperability with NumPy functions Elementwise NumPy ufuncs (log, exp, sqrt, ...) and various other NumPy functions can be used with no issues on DataFrame, assuming plot 167 pandas: powerful Python data analysis toolkit, Release 0.7.1 You may pass logy to get a log-scale Y axis. In [881]: plt.figure(); In [881]: ts = Series(randn(1000), index=DateRange(’1/1/2000’