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# table - Working with tabular data¶

This module defines the table class that provides convenient functionality to work with tabular data. It features functions to calculate statistical moments, e.g. mean, standard deviations as well as functionality to plot the data using matplotlib.

## Basic Usage¶

Populate table with data:

from ost.table import *

# create table with two columns, x and y both of float type
tab=Table(['x', 'y'], 'ff')
for x in range(1000):

# create a plot
plt=tab.Plot('x', 'y')

# save resulting plot to png file
plt.savefig('x-vs-y.png')


Iterating over table items:

# load table from file

# get column index for col 'foo'
idx=tab.GetColIndex('foo')

# iterate over all rows
for row in tab.rows:
# print complete row
print row

# print value for column 'foo'
print row[idx]

# iterate over all rows of selected columns
for foo, bar in tab.Zip('foo','bar'):
print foo, bar


## Functions You Might be Interested In¶

 Adding/Removing/Reordering data AddRow() add a row to the table AddCol() add a column to the table RemoveCol() remove a column from the table Extend() append a table to the end of another table Merge() merge two tables together Sort() sort table by column Filter() filter table by values Zip() extract multiple columns at once Input/Output Save() save a table to a file Load() load a table from a file ToString() convert a table to a string for printing Simple Math Min() compute the minimum of a column Max() compute the maximum of a column Sum() compute the sum of a column Mean() compute the mean of a column RowMean() compute the mean for each row Median() compute the median of a column StdDev() compute the standard deviation of a column Count() compute the number of items in a column More Sophisticated Math Correl() compute Pearson’s correlation coefficient SpearmanCorrel() compute Spearman’s rank correlation coefficient ComputeMCC() compute Matthew’s correlation coefficient ComputeROC() compute receiver operating characteristics (ROC) ComputeEnrichment() compute enrichment GetOptimalPrefactors() compute optimal coefficients for linear combination of columns Plot Plot() Plot data in 1, 2 or 3 dimensions PlotHistogram() Plot data as histogram PlotROC() Plot receiver operating characteristics (ROC) PlotEnrichment() Plot enrichment

## The Table class¶

class Table(col_names=None, col_types=None, **kwargs)

The table class provides convenient access to data in tabular form. An empty table can be easily constructed as follows

tab=Table()


If you want to add columns directly when creating the table, column names and column types can be specified as follows

tab=Table(['nameX','nameY','nameZ'], 'sfb')


this will create three columns called nameX, nameY and nameZ of type string, float and bool, respectively. There will be no data in the table and thus, the table will not contain any rows.

The following column types are supported:

name abbrev
string s
float f
int i
bool b

If you want to add data to the table in addition, use the following:

tab=Table(['nameX','nameY','nameZ'],
'sfb',
nameX=['a','b','c'],
nameY=[0.1, 1.2, 3.414],
nameZ=[True, False, False])


if values for one column is left out, they will be filled with NA, but if values are specified, all values must be specified (i.e. same number of values per column)

Add a column to the right of the table.

Parameters: col_name (str) – name of new column col_type (str) – type of new column (long versions: int, float, bool, string or short versions: i, f, b, s) data (scalar or iterable) – data to add to new column.

Example:

tab=Table(['x'], 'f', x=range(5))
print tab

'''
will produce the table

====  ====
x     even
====  ====
0   True
1   False
2   True
3   False
4   True
====  ====
'''


If data is a constant instead of an iterable object, it’s value will be written into each row:

tab=Table(['x'], 'f', x=range(5))
print tab

'''
will produce the table

====  ====
x     num
====  ====
0   1
1   1
2   1
3   1
4   1
====  ====
'''


Warning

AddCol() only adds data to existing rows and does not add new rows. Use AddRow() to do this. Therefore, the following code snippet does not add any data items:

tab=Table()
print tab

'''
will produce the empty table

====
even
====
'''


Add a row to the table.

data may either be a dictionary or a list-like object:

• If data is a dictionary the keys in the dictionary must match the column names. Columns not found in the dict will be initialized to None. If the dict contains list-like objects, multiple rows will be added, if the number of items in all list-like objects is the same, otherwise a ValueError is raised.
• If data is a list-like object, the row is initialized from the values in data. The number of items in data must match the number of columns in the table. A ValuerError is raised otherwise. The values are added in the order specified in the list, thus, the order of the data must match the columns.

If overwrite is not None and set to an existing column name, the specified column in the table is searched for the first occurrence of a value matching the value of the column with the same name in the dictionary. If a matching value is found, the row is overwritten with the dictionary. If no matching row is found, a new row is appended to the table.

Parameters: data (dict or list-like object) – data to add overwrite (str) – column name to overwrite existing row if value in column overwrite matches ValueError if list-like object is used and number of items does not match number of columns in table. ValueError if dict is used and multiple rows are added but the number of data items is different for different columns.

Example: add multiple data rows to a subset of columns using a dictionary

# create table with three float columns
tab = Table(['x','y','z'], 'fff')

data = {'x': [1.2, 1.6], 'z': [1.6, 5.3]}
print tab

'''
will produce the table

====  ====  ====
x     y     z
====  ====  ====
1.20  NA    1.60
1.60  NA    5.30
====  ====  ====
'''

# overwrite the row with x=1.2 and add row with x=1.9
data = {'x': [1.2, 1.9], 'z': [7.9, 3.5]}
print tab

'''
will produce the table

====  ====  ====
x     y     z
====  ====  ====
1.20  NA    7.90
1.60  NA    5.30
1.90  NA    3.50
====  ====  ====
'''

ComputeEnrichment(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0)

Computes the enrichment of column score_col classified according to class_col.

For this it is necessary, that the datapoints are classified into positive and negative points. This can be done in two ways:

• by using one ‘bool’ type column (class_col) which contains True for positives and False for negatives
• by specifying a classification column (class_col), a cutoff value (class_cutoff) and the classification columns direction (class_dir). This will generate the classification on the fly
• if class_dir=='-': values in the classification column that are less than or equal to class_cutoff will be counted as positives
• if class_dir=='+': values in the classification column that are larger than or equal to class_cutoff will be counted as positives

During the calculation, the table will be sorted according to score_dir, where a ‘-‘ values means smallest values first and therefore, the smaller the value, the better.

ComputeEnrichmentAUC(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0)

Computes the area under the curve of the enrichment using the trapezoidal rule.

Warning : The function depends on numpy
ComputeMCC(score_col, class_col, score_dir='-', class_dir='-', score_cutoff=2.0, class_cutoff=2.0)

Compute Matthews correlation coefficient (MCC) for one column (score_col) with the points classified into true positives, false positives, true negatives and false negatives according to a specified classification column (class_col).

The datapoints in score_col and class_col are classified into positive and negative points. This can be done in two ways:

• by using ‘bool’ columns which contains True for positives and False for negatives
• by using ‘float’ or ‘int’ columns and specifying a cutoff value and the columns direction. This will generate the classification on the fly
• if class_dir/score_dir=='-': values in the classification column that are less than or equal to class_cutoff/score_cutoff will be counted as positives
• if class_dir/score_dir=='+': values in the classification column that are larger than or equal to class_cutoff/score_cutoff will be counted as positives

The two possibilities can be used together, i.e. ‘bool’ type for one column and ‘float’/’int’ type and cutoff/direction for the other column.

ComputeROC(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0)

Computes the receiver operating characteristics (ROC) of column score_col classified according to class_col.

For this it is necessary, that the datapoints are classified into positive and negative points. This can be done in two ways:

• by using one ‘bool’ column (class_col) which contains True for positives and False for negatives
• by using a non-bool column (class_col), a cutoff value (class_cutoff) and the classification columns direction (class_dir). This will generate the classification on the fly
• if class_dir=='-': values in the classification column that are less than or equal to class_cutoff will be counted as positives
• if class_dir=='+': values in the classification column that are larger than or equal to class_cutoff will be counted as positives

During the calculation, the table will be sorted according to score_dir, where a ‘-‘ values means smallest values first and therefore, the smaller the value, the better.

If class_col does not contain any positives (i.e. value is True (if column is of type bool) or evaluated to True (if column is of type int or float (depending on class_dir and class_cutoff))) the ROC is not defined and the function will return None.

ComputeROCAUC(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0)

Computes the area under the curve of the receiver operating characteristics using the trapezoidal rule.

Warning : The function depends on numpy
Correl(col1, col2)

Calculate the Pearson correlation coefficient between col1 and col2, only taking rows into account where both of the values are not equal to None. If there are not enough data points to calculate a correlation coefficient, None is returned.

Parameters: col1 (str) – column name for first column col2 (str) – column name for second column
Count(col, ignore_nan=True)

Count the number of cells in column that are not equal to None.

Parameters: col (str) – column name ignore_nan (bool) – ignore all None values
Extend(tab, overwrite=None)

Append each row of tab to the current table. The data is appended based on the column names, thus the order of the table columns is not relevant, only the header names.

If there is a column in tab that is not present in the current table, it is added to the current table and filled with None for all the rows present in the current table.

If the type of any column in tab is not the same as in the current table a TypeError is raised.

If overwrite is not None and set to an existing column name, the specified column in the table is searched for the first occurrence of a value matching the value of the column with the same name in the dictionary. If a matching value is found, the row is overwritten with the dictionary. If no matching row is found, a new row is appended to the table.

Filter(*args, **kwargs)

Returns a filtered table only containing rows matching all the predicates in kwargs and args For example,

tab.Filter(town='Basel')


will return all the rows where the value of the column “town” is equal to “Basel”. Several predicates may be combined, i.e.

tab.Filter(town='Basel', male=True)


will return the rows with “town” equal to “Basel” and “male” equal to true. args are unary callables returning true if the row should be included in the result and false if not.

GetColIndex(col)

Returns the column index for the column with the given name.

Raises : ValueError if no column with the name is found
GetColNames()

Returns a list containing all column names.

GetName()

Get name of table

GetNumpyMatrix(*args)

Returns a numpy matrix containing the selected columns from the table as columns in the matrix. Only columns of type int or float are supported. NA values in the table will be converted to None values.

Parameters: *args – column names to include in numpy matrix The function depends on numpy
GetOptimalPrefactors(ref_col, *args, **kwargs)

This returns the optimal prefactor values (i.e. a, b, c, ...) for the following equation

(1)a*u + b*v + c*w + ... = z

where u, v, w and z are vectors. In matrix notation

(2)A*p = z

where A contains the data from the table (u,v,w,...), p are the prefactors to optimize (a,b,c,...) and z is the vector containing the result of equation (1).

The parameter ref_col equals to z in both equations, and *args are columns u, v and w (or A in (2)). All columns must be specified by their names.

Example:

tab.GetOptimalPrefactors('colC', 'colA', 'colB')


The function returns a list of containing the prefactors a, b, c, ... in the correct order (i.e. same as columns were specified in *args).

Weighting: If the kwarg weights=”columX” is specified, the equations are weighted by the values in that column. Each row is multiplied by the weight in that row, which leads to (3):

(3)weight*a*u + weight*b*v + weight*c*w + ... = weight*z

Weights must be float or int and can have any value. A value of 0 ignores this equation, a value of 1 means the same as no weight. If all weights are the same for each row, the same result will be obtained as with no weights.

Example:

tab.GetOptimalPrefactors('colC', 'colA', 'colB', weights='colD')

GetUnique(col, ignore_nan=True)

Extract a list of all unique values from one column

Parameters: col (str) – column name ignore_nan (bool) – ignore all None values
HasCol(col)

Checks if the column with a given name is present in the table.

IsEmpty(col_name=None, ignore_nan=True)

Checks if a table is empty.

If no column name is specified, the whole table is checked for being empty, whereas if a column name is specified, only this column is checked.

By default, all NAN (or None) values are ignored, and thus, a table containing only NAN values is considered as empty. By specifying the option ignore_nan=False, NAN values are counted as ‘normal’ values.

Load table from stream or file with given name.

By default, the file format is set to auto, which tries to guess the file format from the file extension. The following file extensions are recognized:

extension recognized format
.csv comma separated values
.pickle pickled byte stream
<all others> ost-specific format

Thus, format must be specified for reading file with different filename extensions.

The following file formats are understood:

• ost

col_name1[type1] <col_name2[type2]>...

The types given in brackets must be one of the data types the Table class understands. Each following line in the file then must contains exactly the same number of data items as listed in the header. The data items are automatically converted to the column format. Lines starting with a ‘#’ and empty lines are ignored.

• pickle

Deserializes the table from a pickled byte stream

• csv

Reads the table from comma separated values stream. Since there is no explicit type information in the csv file, the column types are guessed, using the following simple rules:

• if all values are either NA/NULL/NONE the type is set to string
• if all non-null values are convertible to float/int the type is set to float/int
• if all non-null values are true/false/yes/no, the value is set to bool
• for all other cases, the column type is set to string
Returns: A new Table instance
Max(col)

Returns the maximum value in col. If several rows have the highest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
MaxIdx(col)

Returns the row index of the cell with the maximal value in col. If several rows have the highest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
MaxRow(col)

Returns the row containing the cell with the maximal value in col. If several rows have the highest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
Mean(col)

Returns the mean of the given column. Cells with None are ignored. Returns None, if the column doesn’t contain any elements. Col must be of numeric (‘float’, ‘int’) or boolean column type.

If column type is bool, the function returns the ratio of number of ‘Trues’ by total number of elements.

Parameters: col (str) – column name TypeError if column type is string
Median(col)

Returns the median of the given column. Cells with None are ignored. Returns None, if the column doesn’t contain any elements. Col must be of numeric column type (‘float’, ‘int’) or boolean column type.

Parameters: col (str) – column name TypeError if column type is string
Min(col)

Returns the minimal value in col. If several rows have the lowest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
MinIdx(col)

Returns the row index of the cell with the minimal value in col. If several rows have the lowest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
MinRow(col)

Returns the row containing the cell with the minimal value in col. If several rows have the lowest value, only the first one is returned. None values are ignored.

Parameters: col (str) – column name
Plot(x, y=None, z=None, style='.', x_title=None, y_title=None, z_title=None, x_range=None, y_range=None, z_range=None, color=None, plot_if=None, legend=None, num_z_levels=10, diag_line=False, labels=None, max_num_labels=None, title=None, clear=True, save=False, **kwargs)

Function to plot values from your table in 1, 2 or 3 dimensions using Matplotlib

Parameters: x (str) – column name for first dimension y (str) – column name for second dimension z (str) – column name for third dimension style (str) – symbol style (e.g. ., -, x, o, +, *). For a complete list check (matplotlib docu). x_title (str) – title for first dimension, if not specified it is automatically derived from column name y_title (str) – title for second dimension, if not specified it is automatically derived from column name z_title (str) – title for third dimension, if not specified it is automatically derived from column name x_range (list of length two) – start and end value for first dimension (e.g. [start_x, end_x]) y_range (list of length two) – start and end value for second dimension (e.g. [start_y, end_y]) z_range (list of length two) – start and end value for third dimension (e.g. [start_z, end_z]) color (str) – color for data (e.g. b, g, r). For a complete list check (matplotlib docu). plot_if (callable) – callable which returnes True if row should be plotted. Is invoked like plot_if(self, row) legend (str) – legend label for data series num_z_levels (int) – number of levels for third dimension diag_line (bool) – draw diagonal line labels (str) – column name containing labels to put on x-axis for one dimensional plot max_num_labels (int) – limit maximum number of labels title (str) – plot title, if not specified it is automatically derived from plotted column names clear (bool) – clear old data from plot save (str) – filename for saving plot **kwargs – additional arguments passed to matplotlib the matplotlib.pyplot module

Examples: simple plotting functions

tab=Table(['a','b','c','d'],'iffi', a=range(5,0,-1),
b=[x/2.0 for x in range(1,6)],
c=[math.cos(x) for x in range(0,5)],
d=range(3,8))

# one dimensional plot of column 'd' vs. index
plt=tab.Plot('d')
plt.show()

# two dimensional plot of 'a' vs. 'c'
plt=tab.Plot('a', y='c', style='o-')
plt.show()

# three dimensional plot of 'a' vs. 'c' with values 'b'
plt=tab.Plot('a', y='c', z='b')
# manually save plot to file
plt.savefig("plot.png")

PlotEnrichment(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0, style='-', title=None, x_title=None, y_title=None, clear=True, save=None)

Plot an enrichment curve using matplotlib of column score_col classified according to class_col.

Warning : The function depends on matplotlib
PlotHistogram(col, x_range=None, num_bins=10, normed=False, histtype='stepfilled', align='mid', x_title=None, y_title=None, title=None, clear=True, save=False)

Create a histogram of the data in col for the range x_range, split into num_bins bins and plot it using Matplotlib.

Parameters: col (str) – column name with data x_range (list of length two) – start and end value for first dimension (e.g. [start_x, end_x]) num_bins (int) – number of bins in range normed (bool) – normalize histogram histtype (str) – type of histogram (i.e. bar, barstacked, step, stepfilled). See (matplotlib docu). align (str) – style of histogram (left, mid, right). See (matplotlib docu). x_title (str) – title for first dimension, if not specified it is automatically derived from column name y_title (str) – title for second dimension, if not specified it is automatically derived from column name title (str) – plot title, if not specified it is automatically derived from plotted column names clear (bool) – clear old data from plot save (str) – filename for saving plot

Examples: simple plotting functions

tab=Table(['a'],'f', a=[math.cos(x*0.01) for x in range(100)])

# one dimensional plot of column 'd' vs. index
plt=tab.PlotHistogram('a')
plt.show()

PlotROC(score_col, class_col, score_dir='-', class_dir='-', class_cutoff=2.0, style='-', title=None, x_title=None, y_title=None, clear=True, save=None)

Plot an ROC curve using matplotlib.

Warning : The function depends on matplotlib
RemoveCol(col)

Remove column with the given name from the table

Parameters: col (str) – name of column to remove
RowMean(mean_col_name, cols)

Adds a new column of type ‘float’ with a specified name (mean_col_name), containing the mean of all specified columns for each row.

Cols are specified by their names and must be of numeric column type (‘float’, ‘int’) or boolean column type. Cells with None are ignored. Adds None if the row doesn’t contain any values.

Parameters: mean_col_name (str) – name of new column containing mean values cols (str or list of strings) – name or list of names of columns to include in computation of mean TypeError if column type of columns in col is string

== Example ==

Staring with the following table:

x y u
1 10 100
2 15 None
3 20 400

the code here adds a column with the name ‘mean’ to yield the table below:

x y u mean
1 10 100 50.5
2 15 None 2
3 20 400 201.5
Save(stream_or_filename, format='ost', sep=', ')

Save the table to stream or filename. The following three file formats are supported (for more information on file formats, see Load()):

 ost ost-specific format (human readable) csv comma separated values (human readable) pickle pickled byte stream (binary)
Parameters: stream_or_filename (str or file) – filename or stream for writing output format (str) – output format (i.e. ost, csv, pickle) ValueError if format is unknown
SetName(name)

Set name of the table :param name: name :type name: str

Sort(by, order='+')

Performs an in-place sort of the table, based on column by.

Parameters: by (str) – column name by which to sort order (str (i.e. +, -)) – ascending (-) or descending (+) order
SpearmanCorrel(col1, col2)

Calculate the Spearman correlation coefficient between col1 and col2, only taking rows into account where both of the values are not equal to None. If there are not enough data points to calculate a correlation coefficient, None is returned.

Warning : The function depends on the following module: scipy.stats.mstats col1 (str) – column name for first column col2 (str) – column name for second column
StdDev(col)

Returns the standard deviation of the given column. Cells with None are ignored. Returns None, if the column doesn’t contain any elements. Col must be of numeric column type (‘float’, ‘int’) or boolean column type.

Parameters: col (str) – column name TypeError if column type is string
Sum(col)

Returns the sum of the given column. Cells with None are ignored. Returns 0.0, if the column doesn’t contain any elements. Col must be of numeric column type (‘float’, ‘int’) or boolean column type.

Parameters: col (str) – column name TypeError if column type is string
ToString(float_format='%.3f', int_format='%d', rows=None)

Convert the table into a string representation.

The output format can be modified for int and float type columns by specifying a formatting string for the parameters ‘float_format’ and ‘int_format’.

The option ‘rows’ specify the range of rows to be printed. The parameter must be a type that supports indexing (e.g. a list) containing the start and end row index, e.g. [start_row_idx, end_row_idx].

Parameters: float_format (str) – formatting string for float columns int_format (str) – formatting string for int columns rows (iterable containing ints) – iterable containing start and end row index
Zip(*args)

Allows to conveniently iterate over a selection of columns, e.g.

tab=Table.Load('...')
for col1, col2 in tab.Zip('col1', 'col2'):
print col1, col2


is a shortcut for

tab=Table.Load('...')
for col1, col2 in zip(tab['col1'], tab['col2']):
print col1, col2

Merge(table1, table2, by, only_matching=False)

Returns a new table containing the data from both tables. The rows are combined based on the common values in the column(s) by. The option ‘by’ can be a list of column names. When this is the case, merging is based on multiple columns. For example, the two tables below

x y
1 10
2 15
3 20
x u
1 100
3 200
4 400
x y u
1 10 100
2 15 None
3 20 200
4 None 400

when merged by column x, produce the following output:

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