API¶

Identifying (filtering) columns¶

Several class methods and functions in this module allow column and row filtering of the CSV file data. It is necessary to identify columns for both of these operations and how these columns can be identified depends on whether the file has or does not have a header as indicated by the has_header boolean constructor argument:

If has_header is True the first line of the file is taken as the header. Columns can be identified by name (a string that has to match a column value in the file header) or by number (an integer representing the column number with column zero being the leftmost column)
If has_header is False columns can only be identified by number (an integer representing the column number with column zero being the leftmost column)

For example, if a file myfile.csv has the following data:

Ctrl	Ref	Result
1	3	10
1	4	20
2	4	30
2	5	40
3	5	50

Then when the file is loaded with pcsv.CsvFile('myfile.csv', has_header=True) the columns can be referred to as 'Ctrl' or 0, 'Ref' or 1, or 'Result' or 2. However if the file is loaded with pcsv.CsvFile('myfile.csv', has_header=False) the columns can be referred only as 0, 1 or 2.

Filtering rows¶

Several class methods and functions of this module allow row filtering of the CSV file data. The row filter is described in the CsvRowFilter pseudo-type

Swapping or inserting columns¶

The column filter not only filters columns but also determines the order in which the columns are stored internally in an pcsv.CsvFile object. This means that the column filter can be used to reorder and/or duplicate columns. For example:

# pcsv_example_6.py
import pmisc, pcsv


def main():
    ctx = pmisc.TmpFile
    with ctx() as ifname:
        with ctx() as ofname:
            # Create input data file
            data = [
                ["Ctrl", "Ref", "Result"],
                [1, 3, 10],
                [1, 4, 20],
                [2, 4, 30],
                [2, 5, 40],
                [3, 5, 50],
            ]
            pcsv.write(ifname, data, append=False)
            # Swap 'Ctrl' and 'Result' columns, duplicate
            # 'Ref' column at the end
            obj = pcsv.CsvFile(fname=ifname, dfilter=["Result", "Ref", "Ctrl", 1])
            assert obj.header(filtered=False) == ["Ctrl", "Ref", "Result"]
            assert obj.header(filtered=True) == ["Result", "Ref", "Ctrl", "Ref"]
            obj.write(
                ofname,
                header=["Result", "Ref", "Ctrl", "Ref2"],
                filtered=True,
                append=False,
            )
            # Verify that resulting file is correct
            ref_data = [
                [10, 3, 1, 3],
                [20, 4, 1, 4],
                [30, 4, 2, 4],
                [40, 5, 2, 5],
                [50, 5, 3, 5],
            ]
            obj = pcsv.CsvFile(ofname, has_header=True)
            assert obj.header() == ["Result", "Ref", "Ctrl", "Ref2"]
            assert obj.data() == ref_data


if __name__ == "__main__":
    main()

Empty columns¶

When a file has empty columns they are read as None. Conversely any column value that is None is written as an empty column. Empty columns are ones that have either an empty string ('') or literally no information between the column delimiters (,)

For example, if a file myfile2.csv has the following data:

Ctrl	Ref	Result
1	4	20
2		30
2	5
	5	50

The corresponding read array is:

[
    ['Ctrl', 'Ref', 'Result'],
    [1, 4, 20],
    [2, None, 30],
    [2, 5, None],
    [None, 5, 50]
]

Functions¶

pcsv.concatenate(fname1, fname2, dfilter1=None, dfilter2=None, has_header1=True, has_header2=True, frow1=0, frow2=0, ofname=None, ocols=None)¶

Concatenate two comma-separated values file.

Data rows from the second file are appended at the end of the data rows from the first file

Parameters:

fname1 (FileNameExists) – Name of the first comma-separated values file, the file whose data appears first in the output file
fname2 (FileNameExists) – Name of the second comma-separated values file, the file whose data appears last in the output file
dfilter1 (CsvDataFilter or None) – Row and/or column filter for the first file. If None no data filtering is done on the file
dfilter2 (CsvDataFilter or None) – Row and/or column filter for the second file. If None no data filtering is done on the file
has_header1 (boolean) – Flag that indicates whether the first comma-separated values file has column headers in its first line (True) or not (False)
has_header2 (boolean) – Flag that indicates whether the second comma-separated values file has column headers in its first line (True) or not (False)
frow1 (NonNegativeInteger) – First comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
frow2 (NonNegativeInteger) – Second comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
ofname (FileName or None) – Name of the output comma-separated values file, the file that will contain the data from the first and second files. If None the first file is replaced “in place”
ocols (list or None) – Column names of the output comma-separated values file. If None the column names in the first file are used if has_header1 is True or the column names in the second files are used if has_header1 is False and has_header2 is True, otherwise no header is used

Raises:

OSError (File [fname] could not be found)
RuntimeError (Argument `dfilter1` is not valid)
RuntimeError (Argument `dfilter2` is not valid)
RuntimeError (Argument `fname1` is not valid)
RuntimeError (Argument `fname2` is not valid)
RuntimeError (Argument `frow1` is not valid)
RuntimeError (Argument `frow2` is not valid)
RuntimeError (Argument `ocols` is not valid)
RuntimeError (Argument `ofname` is not valid)
RuntimeError (Column headers are not unique in file [fname])
RuntimeError (File [fname] has no valid data)
RuntimeError (File [fname] is empty)
RuntimeError (Files have different number of columns)
RuntimeError (Invalid column specification)
RuntimeError (Number of columns in data files and output columns are different)
ValueError (Column [column_identifier] not found)

pcsv.dsort(fname, order, has_header=True, frow=0, ofname=None)¶

Sort file data.

Parameters:

fname (FileNameExists) – Name of the comma-separated values file to sort
order (CsvColFilter) – Sort order
has_header (boolean) – Flag that indicates whether the comma-separated values file to sort has column headers in its first line (True) or not (False)
frow (NonNegativeInteger) – First data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
ofname (FileName or None) – Name of the output comma-separated values file, the file that will contain the sorted data. If None the sorting is done “in place”

Raises:

OSError (File [fname] could not be found)
RuntimeError (Argument `fname` is not valid)
RuntimeError (Argument `frow` is not valid)
RuntimeError (Argument `has_header` is not valid)
RuntimeError (Argument `ofname` is not valid)
RuntimeError (Argument `order` is not valid)
RuntimeError (Column headers are not unique in file [fname])
RuntimeError (File [fname] has no valid data)
RuntimeError (File [fname] is empty)
RuntimeError (Invalid column specification)
ValueError (Column [column_identifier] not found)

pcsv.merge(fname1, fname2, dfilter1=None, dfilter2=None, has_header1=True, has_header2=True, frow1=0, frow2=0, ofname=None, ocols=None)¶

Merge two comma-separated values files.

Data columns from the second file are appended after data columns from the first file. Empty values in columns are used if the files have different number of rows

Parameters:

fname1 (FileNameExists) – Name of the first comma-separated values file, the file whose columns appear first in the output file
fname2 (FileNameExists) – Name of the second comma-separated values file, the file whose columns appear last in the output file
dfilter1 (CsvDataFilter or None) – Row and/or column filter for the first file. If None no data filtering is done on the file
dfilter2 (CsvDataFilter or None) – Row and/or column filter for the second file. If None no data filtering is done on the file
has_header1 (boolean) – Flag that indicates whether the first comma-separated values file has column headers in its first line (True) or not (False)
has_header2 (boolean) – Flag that indicates whether the second comma-separated values file has column headers in its first line (True) or not (False)
frow1 (NonNegativeInteger) – First comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
frow2 (NonNegativeInteger) – Second comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
ofname (FileName or None) – Name of the output comma-separated values file, the file that will contain the data from the first and second files. If None the first file is replaced “in place”
ocols (list or None) – Column names of the output comma-separated values file. If None the column names in the first and second files are used if has_header1 and/or has_header2 are True. The column labels 'Column [column_number]' are used when one of the two files does not have a header, where [column_number] is an integer representing the column number (column 0 is the leftmost column). No header is used if has_header1 and has_header2 are False

Raises:

OSError (File [fname] could not be found)
RuntimeError (Argument `dfilter1` is not valid)
RuntimeError (Argument `dfilter2` is not valid)
RuntimeError (Argument `fname1` is not valid)
RuntimeError (Argument `fname2` is not valid)
RuntimeError (Argument `frow1` is not valid)
RuntimeError (Argument `frow2` is not valid)
RuntimeError (Argument `ocols` is not valid)
RuntimeError (Argument `ofname` is not valid)
RuntimeError (Column headers are not unique in file [fname])
RuntimeError (Combined columns in data files and output columns are different)
RuntimeError (File [fname] has no valid data)
RuntimeError (File [fname] is empty)
RuntimeError (Invalid column specification)
ValueError (Column [column_identifier] not found)

pcsv.replace(fname1, fname2, dfilter1, dfilter2, has_header1=True, has_header2=True, frow1=0, frow2=0, ofname=None, ocols=None)¶

Replace data in one file with data from another file.

Parameters:

fname1 (FileNameExists) – Name of the input comma-separated values file, the file that contains the columns to be replaced
fname2 (FileNameExists) – Name of the replacement comma-separated values file, the file that contains the replacement data
dfilter1 (CsvDataFilter) – Row and/or column filter for the input file
dfilter2 (CsvDataFilter) – Row and/or column filter for the replacement file
has_header1 (boolean) – Flag that indicates whether the input comma-separated values file has column headers in its first line (True) or not (False)
has_header2 (boolean) – Flag that indicates whether the replacement comma-separated values file has column headers in its first line (True) or not (False)
frow1 (NonNegativeInteger) – Input comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
frow2 (NonNegativeInteger) – Replacement comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns
ofname (FileName) – Name of the output comma-separated values file, the file that will contain the input file data but with some columns replaced with data from the replacement file. If None the input file is replaced “in place”
ocols (list or None) – Names of the replaced columns in the output comma-separated values file. If None the column names in the input file are used if has_header1 is True, otherwise no header is used

Raises:

OSError (File [fname] could not be found)
RuntimeError (Argument `dfilter1` is not valid)
RuntimeError (Argument `dfilter2` is not valid)
RuntimeError (Argument `fname1` is not valid)
RuntimeError (Argument `fname2` is not valid)
RuntimeError (Argument `frow1` is not valid)
RuntimeError (Argument `frow2` is not valid)
RuntimeError (Argument `ocols` is not valid)
RuntimeError (Argument `ofname` is not valid)
RuntimeError (Column headers are not unique in file [fname])
RuntimeError (File [fname] has no valid data)
RuntimeError (File [fname] is empty)
RuntimeError (Invalid column specification)
RuntimeError (Number of input and output columns are different)
RuntimeError (Number of input and replacement columns are different)
ValueError (Column [column_identifier] not found)
ValueError (Number of rows mismatch between input and replacement data)

pcsv.write(fname, data, append=True)¶

Write data to a specified comma-separated values (CSV) file.

Parameters:

fname (FileName) – Name of the comma-separated values file to be written
data (list) – Data to write to the file. Each item in this argument should contain a sub-list corresponding to a row of data; each item in the sub-lists should contain data corresponding to a particular column
append (boolean) – Flag that indicates whether data is added to an existing file (or a new file is created if it does not exist) (True), or whether data overwrites the file contents (if the file exists) or creates a new file if the file does not exists (False)

Raises:	OSError (File [fname] could not be created: [reason]) RuntimeError (Argument `append` is not valid) RuntimeError (Argument `data` is not valid) RuntimeError (Argument `fname` is not valid) ValueError (There is no data to save to file)

Class¶

class pcsv.CsvFile(fname, dfilter=None, has_header=True, frow=0)¶

Bases: object

Process comma-separated values (CSV) files.

Parameters:

fname (FileNameExists) – Name of the comma-separated values file to read
dfilter (CsvDataFilter or None) – Row and/or column filter. If None no data filtering is done
has_header (boolean) – Flag that indicates whether the comma-separated values file has column headers in its first line (True) o not (False)
frow (NonNegativeInteger) – First data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns

Return type:

pcsv.CsvFile object

Raises:

OSError (File [fname] could not be found)
RuntimeError (Argument `dfilter` is not valid)
RuntimeError (Argument `fname` is not valid)
RuntimeError (Argument `frow` is not valid)
RuntimeError (Argument `has_header` is not valid)
RuntimeError (Column headers are not unique in file [fname])
RuntimeError (File [fname] has no valid data)
RuntimeError (File [fname] is empty)
RuntimeError (Invalid column specification)
ValueError (Column [column_identifier] not found)

__eq__(other)¶

Test object equality.

For example:

>>> import pmisc, pcsv
>>> with pmisc.TmpFile() as fname:
...     pcsv.write(fname, [['a'], [1]], append=False)
...     obj1 = pcsv.CsvFile(fname, dfilter='a')
...     obj2 = pcsv.CsvFile(fname, dfilter='a')
...
>>> with pmisc.TmpFile() as fname:
...     pcsv.write(fname, [['a'], [2]], append=False)
...     obj3 = pcsv.CsvFile(fname, dfilter='a')
...
>>> obj1 == obj2
True
>>> obj1 == obj3
False
>>> 5 == obj3
False

__repr__()¶

Return a string with the expression needed to re-create the object.

For example:

>>> import pmisc, pcsv
>>> with pmisc.TmpFile() as fname:
...     pcsv.write(fname, [['a'], [1]], append=False)
...     obj1 = pcsv.CsvFile(fname, dfilter='a')
...     exec("obj2="+repr(obj1))
>>> obj1 == obj2
True
>>> repr(obj1)
"pcsv.CsvFile(fname=r'...', dfilter=['a'])"

add_dfilter(dfilter)¶

Add more row(s) or column(s) to the existing data filter.

Duplicate filter values are eliminated

Parameters:	dfilter (CsvDataFilter) – Row and/or column filter

Raises:	RuntimeError (Argument `dfilter` is not valid) RuntimeError (Invalid column specification) ValueError (Column [column_identifier] not found)

cols(filtered=False)¶

Return the number of data columns.

Parameters:	filtered (boolean) – Flag that indicates whether the raw (input) data should be used (False) or whether filtered data should be used (True)

Raises:	RuntimeError (Argument `filtered` is not valid)

data(filtered=False, no_empty=False)¶

Return (filtered) file data.

The returned object is a list, each item is a sub-list corresponding to a row of data; each item in the sub-lists contains data corresponding to a particular column

Parameters:	filtered (CsvFiltered) – Filtering type no_empty (bool) – Flag that indicates whether rows with empty columns should be filtered out (True) or not (False)
Return type:	list

Raises:	RuntimeError (Argument `filtered` is not valid) RuntimeError (Argument `no_empty` is not valid)

dsort(order)¶

Sort rows.

Parameters:	order (CsvColFilter) – Sort order

Raises:	RuntimeError (Argument `order` is not valid) RuntimeError (Invalid column specification) ValueError (Column [column_identifier] not found)

header(filtered=False)¶

Return data header.

When the raw (input) data is used the data header is a list of the comma-separated values file header if the file is loaded with header (each list item is a column header) or a list of column numbers if the file is loaded without header (column zero is the leftmost column). When filtered data is used the data header is the active column filter, if any, otherwise it is the same as the raw (input) data header

Parameters:	filtered (boolean) – Flag that indicates whether the raw (input) data should be used (False) or whether filtered data should be used (True)
Return type:	list of strings or integers

Raises:	RuntimeError (Argument `filtered` is not valid)

replace(rdata, filtered=False)¶

Replace data.

Parameters:	rdata (list of lists) – Replacement data filtered (CsvFiltered) – Filtering type

Raises:	RuntimeError (Argument `filtered` is not valid) RuntimeError (Argument `rdata` is not valid) ValueError (Number of columns mismatch between input and replacement data) ValueError (Number of rows mismatch between input and replacement data)

reset_dfilter(ftype=True)¶

Reset (clears) the data filter.

Parameters:	ftype (CsvFiltered) – Filter type

Raises:	RuntimeError (Argument `ftype` is not valid)

rows(filtered=False)¶

Return the number of data rows.

Parameters:	filtered (boolean) – Flag that indicates whether the raw (input) data should be used (False) or whether filtered data should be used (True)

Raises:	RuntimeError (Argument `filtered` is not valid)

write(fname=None, filtered=False, header=True, append=False)¶

Write (processed) data to a specified comma-separated values (CSV) file.

Parameters:

fname (FileName) – Name of the comma-separated values file to be written. If None the file from which the data originated is overwritten
filtered (CsvFiltered) – Filtering type
header (string, list of strings or boolean) – If a list, column headers to use in the file. If boolean, flag that indicates whether the input column headers should be written (True) or not (False)
append (boolean) – Flag that indicates whether data is added to an existing file (or a new file is created if it does not exist) (True), or whether data overwrites the file contents (if the file exists) or creates a new file if the file does not exists (False)

Raises:	OSError (File [fname] could not be created: [reason]) RuntimeError (Argument `append` is not valid) RuntimeError (Argument `filtered` is not valid) RuntimeError (Argument `fname` is not valid) RuntimeError (Argument `header` is not valid) RuntimeError (Argument `no_empty` is not valid) ValueError (There is no data to save to file)

cfilter¶

Set or return the column filter.

Type:	CsvColFilter or None. If None no column filtering is done
Return type:	CsvColFilter or None

Raises:

(when assigned)

RuntimeError (Argument `cfilter` is not valid)
RuntimeError (Invalid column specification)
ValueError (Column [column_identifier] not found)

dfilter¶

Set or return the data (row and/or column) filter.

The first tuple item is the row filter and the second tuple item is the column filter

Type:	CsvDataFilter or None. If None no data filtering is done
Return type:	CsvDataFilter or None

Raises:

(when assigned)

RuntimeError (Argument `dfilter` is not valid)
RuntimeError (Invalid column specification)
ValueError (Column [column_identifier] not found)

rfilter¶

Set or return the row filter.

Type:	CsvRowFilter or None. If None no row filtering is done
Return type:	CsvRowFilter or None

Raises:

(when assigned)

RuntimeError (Argument `rfilter` is not valid)
RuntimeError (Invalid column specification)
ValueError (Argument `rfilter` is empty)
ValueError (Column [column_identifier] not found)

Contracts pseudo-types¶

Introduction¶

The pseudo-types defined below can be used in contracts of the PyContracts or Pexdoc libraries. As an example, with the latter:

>>> from __future__ import print_function
>>> import pexdoc
>>> from pcsv.ptypes import csv_col_filter
>>> @pexdoc.pcontracts.contract(cfilter='csv_col_filter')
... def myfunc(cfilter):
...     print('CSV filter received: '+cfilter)
...
>>> myfunc('m')
CSV filter received: m
>>> myfunc(35+3j)
Traceback (most recent call last):
    ...
RuntimeError: Argument `cfilter` is not valid

Alternatively each pseudo-type has a checker function associated with it that can be used to verify membership. For example:

>>> import pcsv.ptypes
>>> # None is returned if object belongs to pseudo-type
>>> pcsv.ptypes.csv_col_filter('m')
>>> # ValueError is raised if object does not belong to pseudo-type
>>> pcsv.ptypes.csv_col_filter(35+3j) 
Traceback (most recent call last):
    ...
ValueError: [START CONTRACT MSG: csv_col_filter]...

Description¶

CsvColFilter¶

Import as csv_col_filter. String, integer, a list of strings or a list of integers that identify a column or columns within a comma-separated values (CSV) file.

Integers identify a column by position (column 0 is the leftmost column) whereas strings identify the column by name. Columns can be identified either by position or by name when the file has a header (first row of file containing column labels) but only by position when the file does not have a header.

None indicates that no column filtering should be done

CsvColSort¶

Import as csv_col_sort. Integer, string, dictionary or list of integers, strings or dictionaries that specify the sort direction of a column or columns in a comma-separated values (CSV) file.

The sort direction can be either ascending, specified by the string 'A', or descending, specified by the string 'B' (case insensitive). The default sort direction is ascending.

The column can be specified numerically or with labels depending on whether the CSV file was loaded with or without a header.

The full specification is a dictionary (or list of dictionaries if multiple columns are to be used for sorting) where the key is the column and the value is the sort order, thus valid examples are {'MyCol':'A'} and [{'MyCol':'A'}, {3:'d'}].

When the default direction suffices it can be omitted; for example in [{'MyCol':'D'}, 3], the data is sorted first by MyCol in descending order and then by the 4th column (column 0 is the leftmost column in a CSV file) in ascending order

CsvDataFilter¶

Import as csv_data_filter. In its most general form a two-item tuple, where one item is of CsvColFilter pseudo-type and the other item is of CsvRowFilter pseudo-type (the order of the items is not mandated, i.e. the first item could be of pseudo-type CsvRowFilter and the second item could be of pseudo-type CsvColFilter or vice-versa).

The two-item tuple can be reduced to a one-item tuple when only a row or column filter needs to be specified, or simply to an object of either CsvRowFilter or CsvColFilter pseudo-type.

For example, all of the following are valid CsvDataFilter objects: ('MyCol', {'MyCol':2.5}), ({'MyCol':2.5}, 'MyCol') (filter in the column labeled MyCol and rows where the column labeled MyCol has the value 2.5), ('MyCol', ) (filter in column labeled MyCol and all rows) and {'MyCol':2.5} (filter in all columns and only rows where the column labeled MyCol has the values 2.5)

None, (None, ) or (None, None) indicate that no row or column filtering should be done

CsvFiltered¶

Import as csv_filtered. String or a boolean that indicates what type of row and column filtering is to be performed in a comma-separated values (CSV) file. If True, 'B' or 'b' it indicates that both row- and column-filtering are to be performed; if False, 'N' or 'n' no filtering is to be performed, if 'R' or 'r' only row-filtering is to be performed, if 'C' or 'c' only column-filtering is to be performed

CsvRowFilter¶

Import as csv_row_filter. Dictionary whose elements are sub-filters with the following structure:

column identifier (CsvColFilter) – Dictionary key. Column to filter (as it appears in the comma-separated values file header when a string is given) or column number (when an integer is given, column zero is the leftmost column)
value (list of strings or numbers, or string or number) – Dictionary value. Column value to filter if a string or number, column values to filter if a list of strings or numbers

If a row filter sub-filter is a column value all rows which contain the specified value in the specified column are kept for that particular individual filter. The overall data set is the intersection of all the data sets specified by each individual sub-filter. For example, if the file to be processed is:

Ctrl	Ref	Result
1	3	10
1	4	20
2	4	30
2	5	40
3	5	50

Then the filter specification rfilter = {'Ctrl':2, 'Ref':5} would result in the following filtered data set:

Ctrl	Ref	Result
2	5	40

However, the filter specification rfilter = {'Ctrl':2, 'Ref':3} would result in an empty list because the data set specified by the Ctrl individual sub-filter does not overlap with the data set specified by the Ref individual sub-filter.

If a row sub-filter is a list, the items of the list represent all the values to be kept for a particular column (strings or numbers). So for example rfilter = {'Ctrl':[2, 3], 'Ref':5} would result in the following filtered data set:

Ctrl	Ref	Result
2	5	40
3	5	50

None indicates that no row filtering should be done

Checker functions¶

pcsv.ptypes.csv_col_filter(obj)¶

Validate if an object is a CsvColFilter pseudo-type object.

Parameters:	obj (any) – Object
Raises:	RuntimeError (Argument `[argument_name]` is not valid). The token [argument_name] is replaced by the name of the argument the contract is attached to
Return type:	None

pcsv.ptypes.csv_col_sort(obj)¶

Validate if an object is a CsvColSort pseudo-type object.

Parameters:	obj (any) – Object
Raises:	RuntimeError (Argument `[argument_name]` is not valid). The token [argument_name] is replaced by the name of the argument the contract is attached to
Return type:	None

pcsv.ptypes.csv_data_filter(obj)¶

Validate if an object is a CsvDataFilter pseudo-type object.

Parameters:	obj (any) – Object
Raises:	RuntimeError (Argument `[argument_name]` is not valid). The token [argument_name] is replaced by the name of the argument the contract is attached to
Return type:	None

pcsv.ptypes.csv_filtered(obj)¶

Validate if an object is a CsvFilter pseudo-type object.

Parameters:	obj (any) – Object
Raises:	RuntimeError (Argument `[argument_name]` is not valid). The token [argument_name] is replaced by the name of the argument the contract is attached to
Return type:	None

pcsv.ptypes.csv_row_filter(obj)¶

Validate if an object is a CsvRowFilter pseudo-type object.

Parameters:	obj (any) – Object
Raises:	RuntimeError (Argument `[argument_name]` is not valid). The token [argument_name] is replaced by the name of the argument the contract is attached to ValueError (Argument `[argument_name]` is empty). The token [argument_name] is replaced by the name of the argument the contract is attached to
Return type:	None