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 for computing 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 to DataFrame DataFrame with a two-level index to a Panel. In [336]: midx = MultiIndex(levels=[[’one’, ’two’], [’x’,’y’]], labels=[[1,1,0,0],[1,0,1,0]]) In [337]: df = DataFrame({’A’ : [1, 2, 3, 4], ’B’: [5, 6, 7, 8]}

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 to DataFrame DataFrame with a two-level index to a Panel. In [329]: midx = MultiIndex(levels=[[’one’, ’two’], [’x’,’y’]], labels=[[1,1,0,0],[1,0,1,0]]) In [330]: df = DataFrame({’A’ : [1, 2, 3, 4], ’B’: [5, 6, 7, 8]} Dimensions: 2 (items) x 2 (major) x 2 (minor) Items: A to B Major axis: one to two Minor axis: x to y 5.3. Panel 41 pandas: powerful Python data analysis toolkit, Release 0.7.1 5.3.4 Item selection /

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 to DataFrame DataFrame with a two-level index to a Panel. In [329]: midx = MultiIndex(levels=[[’one’, ’two’], [’x’,’y’]], labels=[[1,1,0,0],[1,0,1,0]]) In [330]: df = DataFrame({’A’ : [1, 2, 3, 4], ’B’: [5, 6, 7, 8]} Dimensions: 2 (items) x 2 (major) x 2 (minor) Items: A to B Major axis: one to two Minor axis: x to y 5.3. Panel 41 pandas: powerful Python data analysis toolkit, Release 0.7.2 5.3.4 Item selection /

powerful Python data analysis toolkit, Release 0.25.3 In [76]: def combiner(x, y): ....: return np.where(pd.isna(x), y, x) ....: 3.3.5 Descriptive statistics There exists a large number of methods 4, 5, 6], .....: 'y': [10, 20, 30, 40, 50, 60]}, .....: index=pd.MultiIndex.from_product([['a', 'b', 'c'], �→ [1, 2]], .....: names=['let', 'num'])) .....: In [242]: df Out[242]: x y let num a 1 1 10 2 2 20 b 1 3 30 2 4 40 c 1 5 50 2 6 60 In [243]: df.rename_axis(index={'let': 'abc'}) Out[243]: x y 78 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.3 abc num

values will all have this formatting applied. (GH4313) • DataFrame.plot will scatter plot x versus y by passing kind=’scatter’ (GH2215) • Added support for Google Analytics v3 API segment IDs that also Radviz plot (GH1566) • Parallel coordinates plot • Bootstrap plot • Per column styles and secondary y-axis plotting (GH1559) • New datetime converters millisecond plotting (GH1599) • Add option to disable any and all method to DataFrame 1.10.5 New plotting methods Series.plot now supports a secondary_y option: In [1]: plt.figure() Out[1]: In [2]: fx[’FR’].plot(style=’g’)

.: ’joe’:[’x’, ’x’, ’z’, ’y’], ...: ’jolie’:np.random.rand(4)}).set_index([’jim’, ’joe’]) ...: In [2]: df Out[2]: jolie jim joe 0 x 0.179356 x 0.908835 1 z 0.571981 y 0.851401 In [3]: df.index sorting In [5]: df2 = df.sortlevel() In [6]: df2 Out[6]: jolie jim joe 0 x 0.179356 x 0.908835 1 y 0.851401 z 0.571981 In [7]: df2.index.lexsort_depth Out[7]: 2 In [8]: df2.loc[(1,’z’)] Out[8]: jolie getting and setting. Previously, when both a column and attribute named y existed, data.y would return the attribute, while data.y = z would update the column (GH8994) 4 Chapter 1. What’s New pandas:

(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 NotImplementedError: operator values will all have this formatting applied. (GH4313) • DataFrame.plot will scatter plot x versus y by passing kind=’scatter’ (GH2215) • Added support for Google Analytics v3 API segment IDs that also Radviz plot (GH1566) • Parallel coordinates plot • Bootstrap plot • Per column styles and secondary y-axis plotting (GH1559) • New datetime converters millisecond plotting (GH1599) • Add option to disable

43 pandas: powerful Python data analysis toolkit, Release 0.12.0 • Per column styles and secondary y-axis plotting (GH1559) • New datetime converters millisecond plotting (GH1599) • Add option to disable Add any and all method to DataFrame 1.8.5 New plotting methods Series.plot now supports a secondary_y option: In [1]: plt.figure() In [2]: fx[’FR’].plot(style=’g’) powerful Python data analysis toolkit, Release 0.12.0 In [3]: fx[’IT’].plot(style=’k--’, secondary_y=True) Vytautas Jancauskas, the 2012 GSOC participant, has

and columns would break groupby operations (GH7115, GH8112) • Fixed a bug where plotting a column y and specifying a label would mutate the index name of the original DataFrame (GH8494) • Fix regression preserving UTC timezone info when selecting a DatetimeIndex (GH7777) • Bug in to_datetime when format=’%Y%m%d’ and coerce=True are specified, where previously an object array was returned (rather than a coerced Float64Index where iat and at were not testing and were failing (GH8092). • Bug in DataFrame.boxplot() where y-limits were not set correctly when producing multiple axes (GH7528, GH5517). • Bug in read_csv where

2013-01-01 1 2013-01-02 2 2013-01-03 3 2013-01-04 dtype: datetime64[ns] In [15]: s.dt.strftime('%Y/%m/%d') Out[15]: 0 2013/01/01 1 2013/01/02 2 2013/01/03 3 2013/01/04 dtype: object 1.1. v0.17.0 Out[17]: 0 2013-01-01 1 2013-01-02 2 2013-01-03 3 2013-01-04 dtype: object In [18]: s.dt.strftime('%Y/%m/%d') Out[18]: 0 2013/01/01 1 2013/01/02 2 2013/01/03 3 2013/01/04 dtype: object The string format caused variance to be inaccurate for small float values (GH10448) • Bug in Series.plot(kind=’hist’) Y Label not informative (GH10485) • Bug in read_csv when using a converter which generates a uint8 type