Frame bin_column_equal_width

---

bin_column_equal_width(self, column_name, num_bins=None, bin_column_name=None)

Classify column into same-width groups.

Parameters:

column_name : unicode

The column whose values are to be binned.

num_bins : int32 (default=None)

The maximum number of bins. Default is the Square-root choice , where is the number of rows.

bin_column_name : unicode (default=None)

The name for the new column holding the grouping labels. Default is <column_name>_binned.

Returns:

: dict

A list of the edges of each bin.

Group rows of data based on the value in a single column and add a label to identify grouping.

Equal width binning places column values into groups such that the values in each group fall within the same interval and the interval width for each group is equal.

Notes

1. Unicode in column names is not supported and will likely cause the drop_frames() method (and others) to fail!
2. The num_bins parameter is considered to be the maximum permissible number of bins because the data may dictate fewer bins. For example, if the column to be binned has 10 elements with only 2 distinct values and the num_bins parameter is greater than 2, then the number of actual number of bins will only be 2. This is due to a restriction that elements with an identical value must belong to the same bin.

Examples

Given a frame with column a accessed by a Frame object my_frame:

>>> my_frame.inspect( n=11 )
[##]  a
========
[0]    1
[1]    1
[2]    2
[3]    3
[4]    5
[5]    8
[6]   13
[7]   21
[8]   34
[9]   55
[10]  89

Modify the frame, adding a column showing what bin the data is in. The data should be separated into a maximum of five bins and the bin cutoffs should be evenly spaced. Note that there may be bins with no members:

>>> cutoffs = my_frame.bin_column_equal_width('a', 5, 'aEWBinned')
[===Job Progress===]
>>> my_frame.inspect( n=11 )
[##]  a   aEWBinned
===================
[0]    1          0
[1]    1          0
[2]    2          0
[3]    3          0
[4]    5          0
[5]    8          0
[6]   13          0
[7]   21          1
[8]   34          1
[9]   55          3
[10]  89          4

The method returns a list of 6 cutoff values that define the edges of each bin. Note that difference between the cutoff values is constant:

>>> print cutoffs
[1.0, 18.6, 36.2, 53.8, 71.4, 89.0]