An enhancement to pandas module. This is kungfu, with monkey-patched common methods to (Data)Frame and Series in pandas. jerryzhujian9_at_gmail.com Tested under python 2.7.

Install: https://pypi.python.org/pypi/kungfu pip install kungfu The above command will auto take care of the following requirements Requires pandas 0.12.0 (tested 0.12.0-2) which will also install python-dateutil(dateutil), numpy, pytz, six Requires openpyxl for writing excel (tested with 1.5.8, version 1.6.1 or higher, but lower than 2.0.0 may also work.) xlrd for reading excel, xlwt for writing .xls (old format) file numpy 1.7.1 is required by pandas 0.12.0; however some other modules require later numpy pandas 0.12.0/kungfu seem to still work fine (?) with newer numpy (pip install pandas==0.12.0; pip install openpyxl==1.5.8; pip install xlrd; pip install xlwt)

Usage: http://pandas.pydata.org/pandas-docs/version/0.12.0/genindex.html

Generally all of the calling (monkey-patched or not) returns something and the original frame or series remains unchanged. If user wants the original frame or series to be changed, assign the returns back.

Visualize a series as a column of a frame with the series name being the column name. Visualize a single list as a series and therefore a column of a frame when converting a series or frame. However, for a list of lists, Visualize each list of the list (i.e. sublist) as a row! Memorization: list=series=column

Frame has column name, row index (index, e.g., 'a','b' is not necessarily number, e.g., row 0, row 1)

Frame.read/x = Frame.Read/x Frame.save/x = Frame.Save/x Frame.write/x = Frame.Save/x Frame.peek = Frame.Print Frame.Peek = Frame.Print Frame.play = Frame.Play Frame.sel = Frame.Sel Frame.selcol = Frame.SelCol Frame.selrow = Frame.SelRow Frame.delete/remove = Frame.Del Frame.groupv = Frame.GroupV Frame.splith = Frame.SplitH Frame.recols = Frame.ReorderCols Frame.rerows = Frame.ReorderRows Frame.rncols = Frame.RenameCols Frame.newcol = Frame.NewCol Frame.findval = Frame.FindVal Frame.countval = Frame.CountVal Frame.cols = Frame.Columns Frame.rows = Frame.Indices Frame.indices = Frame.Indices Frame.cnames = Frame.Columns Frame.names = Frame.Columns Frame.rnames = Frame.Indices Frame.num = Frame.ToNum Frame.maskout = Frame.Maskout # Frame.fillna = Frame.FillNA

Series.play = Series.Play Series.peek = Series.Print Series.Peek = Series.Print Series.sel = Series.Sel Series.countval = Series.CountVal Series.len = Series.Size # Series.size built-in property Series.uniques = Series.Uniques # Series.unique --existing method Series.cols = Series.Indices Series.rows = Series.Indices Series.indices = Series.Indices Series.names = Series.Indices Series.rnames = Series.Indices Series.cames = Series.Indices Series.num = Series.ToNum Series.str = Series.ToStr Series.maskout = Series.Maskout # Series.fillna = Series.FillNA

from pandas import isnull as isna from pandas import isnull as isnull frame.mean(axis=0),frame.median(axis=0),frame.sum(axis=0) series.mean(axis=0),series.median(axis=0),series.sum(axis=0) series.corr(other, method='')

mergelr = MergeLR concatvh = ConcatVH read/x = Read/x, save/x = Save/x, [fr,sr] = play/Play Frame.tolist(), Frame.list()<--homebrew Series.tolist(), Series.list() <--exisiting method in pandas General notes on "join":

when joining along an axis, the index of each frame does not have to in the same order e.g. ["a","b","c","f"] for left frame, ["b","c","a","e"] for right frame join will match them and return the combined frame (in a certain order)

e.g., outputFrame = kf.MergeLR(outputFrame, tempFrame, join="inter", onKeys=[["sbj", "wordpair"]], sort=False)

tempFrame = kf.ConcatVH([tempFrame, immediate, delayed]) tempRow = [sbj, cnd, memoryTesting, memoryImmediate, memoryDelay, memoryImmediateDelay] tempFrame.append(tempRow) tempFrame.extend(immediate + delayed)

for sbj, grp in edatFrame.groupby("Subject"): grp is a Frame groupby([key1, key2]) groupby().groups is a dict whose keys are the computed unique groups

and corresponding values being the axis labels belonging to each group {'bar': [1, 3, 5], 'foo': [0, 2, 4, 6, 7]}

Loop how to: for name, col in Frame.itercols():

# name is column name, col is a series

for index, row in Frame.iterrows():

# index is row index (not necessarily number), row is a series

for index, value in Series.iteritems():

Also consider apply, applymap, map apply works on a row / column basis of a DataFrame, applymap works element-wise on a DataFrame, and map works element-wise on a Series.