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A Pandas Enhancement

Project description

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

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)


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.Save/x Frame.write/x = Frame.Save/x
Frame.peek = Frame.Print Frame.Peek = Frame.Print = 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.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
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)

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.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.

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