DataComparerLibrary 0.855

For comparing csv-files, 2d-array with a csv-file or 2d-arrays. For comparing text-files, text variable with a text-file or text variables. Including a sorting module.

• Preparation

  • Installation

  • Import statement for the DataComparerLibrary in Python

• DataComparer

  • Introduction

  • Comparing Data

    • Examples of using the DataComparerLibrary for comparing data in Python

    • Examples of using the DataComparerLibrary keywords for comparing data in Robot Framework

    • Examples comparing Actual Data with Expected Data

  • Comparing Text

    • Examples of using the DataComparerLibrary for comparing text in Python

    • Examples of using the DataComparerLibrary keywords for comparing text in Robot Framework

• DataSorter

  • Introduction

  • Default sorting

  • Special sorting

  • Examples of using the DataComparerLibrary keywords for sorting data in Robot Framework

• FileConverter

  • Introduction

  • DataComparerLibrary keywords for preparing data in Robot Framework

  • Examples of using the DataComparerLibrary keywords for preparing data in Robot Framework

Preparation

Installation

If you already have Python with pip installed, you can simply run:

pip install DataComparerLibrary
pip install --upgrade DataComparerLibrary

Also the following pip package is needed:

pip install python-dateutil

Import statement for the DataComparerLibrary in Python

from DataComparerLibrary.datacomparer import DataComparer

DataComparer

Introduction

The DataComparerLibrary can be used for:

• comparing csv-files or text-files

• comparing a 2d-matrix with a csv-file or text-file

• comparing a csv-file or text-file with a 2d-matrix

• comparing 2d-matrices

In case a difference between actual and expected data is found an exception wil be given. In Robot Framework the result will be set to failed.

A strait comparison can be made, but the DataComparerLibrary offers also some special comparison options described beneath.

{PRESENT}:

With {PRESENT} in the expected data file you can make clear that data of a field/cell of the actual data should be present. This can be helpful for fields that have constant changing values. For example generated id’s.

{EMPTY}:

With {EMPTY} in the expected data file you can make clear that data of a field/cell of the actual data should be absent.

{SKIP}:

With {SKIP} in the expected data file you can make clear that the comparison of data of a field/cell or part of a field/cell of the actual data should be skipped. This can be helpful for fields or parts of fields that have constant changing values. For example time or generated id’s.

{INTEGER}:

With {INTEGER} in the expected data file you can make clear that the data of a field/cell of the actual data should be an integer. This can be helpful for fields that have constant changing integer values. For example integer id’s.

{NOT(…)}:

With {NOT(…)} in the expected data file you can make clear that data of a field/cell should not match the unexpected data.

At “Examples comparing Actual Data with Expected Data” you can find some examples how to use it.

{NOW()…:….}:

With {NOW()} in the expected data file you can make clear that the data of a field/cell or part of a field/cell of the actual data should be (a part of) a date. You can let calculate the current or a date in the past or future. Calculation is based on the “relativedelta” method from Python. Also you can style the date in the format you want. This can be helpful for fields that have constant changing date values, but which date values have a fixed offset linked to the current date. At “Examples comparing Actual Data with Expected Data” you can find some examples how to use it.

{DATETIME_FORMAT():YYYYMMDDHHMMSSFF6}:

With {DATETIME_FORMAT():YYYYMMDDHHMMSSFF6} in the expected data file you can make clear that the data of a field/cell or part of a field/cell of the actual data should be (a part of) a date. At this moment it is processed as {SKIP}. In the future it will be changed into a check on date format, but not a specific date. For check on a specific expected date you can use {NOW()…:….}.

Delimiter:

Default delimiter is “,” in case of an input file. You can use the option “delimiter_actual_data” and “delimiter_expected_data” to set another delimiter like “;” or “t” for tab. It is also possible to use a multi-character delimiter like “@#@”.

Quotechar:

Default quotechar is ‘”’ in case of an input file. You can use the option “quotechar_actual_data” and/or “quotechar_expected_data” to set another quotechar.

Comparing Data

Examples of using the DataComparerLibrary for comparing data in Python

Below there are some examples how to call the methods of the DataComparerLibrary in Python:

a = DataComparer
a.compare_data_files(self, actual_file, expected_file)
a.compare_data_files(self, actual_file, expected_file, delimiter_actual_data=';', delimiter_expected_data=';')
a.compare_data_files(self, actual_file, expected_file, delimiter_actual_data='@#@', delimiter_expected_data='@#@')
a.compare_data_2d_array_with_file(self, actual_2d_matrix_data_input, expected_file, delimiter_expected_data='\t')
a.compare_data_file_with_2d_array(self, actual_file, expected_2d_matrix_data_input, delimiter_actual_data=';')
a.compare_data_2d_arrays(self, actual_2d_matrix_data_input, expected_2d_matrix_data_input)

Examples of using the DataComparerLibrary keywords for comparing data in Robot Framework

Below there are some examples how to call the keywords of the DataComparerLibrary in Robot Framework:

*** Settings ***
Library     DataComparerLibrary

*** Test Cases ***
Testcase_DataComparer
    Examples

*** Keywords ***
Examples
    Run Keyword And Continue On Failure  DataComparerLibrary.Compare Data Files  C:\\Users\\actual.csv   C:\\Users\\expected.csv
    DataComparerLibrary.Compare Data Files  C:\\Users\\actual.csv   C:\\Users\\expected.csv  delimiter_actual_data=;  delimiter_expected_data=;
    DataComparerLibrary.Compare Data Files  C:\\Users\\actual.csv   C:\\Users\\expected.csv  delimiter_actual_data=@#@  delimiter_expected_data=@#@
    DataComparerLibrary.Compare Data Files  C:\\Users\\actual.csv   C:\\Users\\expected.csv
    DataComparerLibrary.Compare Data 2d Array With File  ${actual_2d_matrix_data_input}  C:\\Users\\expected.csv  delimiter_expected_data=\t

    Set Variable  ${postcode}  52091
    ${expected_2d_matrix_data_input}=  Evaluate  [['NAME', 'STREET', 'NUMBER', 'CITY', 'POSTCODE'], ['JOHN', 'Lund gatan', 10, 'STOCKHOLM', '${postcode}']]

    DataComparerLibrary.Compare Data File With 2d Array  C:\\Users\\actual.csv  ${expected_2d_matrix_data_input}  delimiter_actual_data=;
    DataComparerLibrary.Compare Data 2d Arrays  ${actual_2d_matrix_data_input}  ${expected_2d_matrix_data_input}

    &{literal_dict}=  Create Dictionary  {lit_1}=${some_variable}
    ...                                  {lit_2}='Text with space'
    ...                                  {version}=${version}
    ...                                  {build_number}=${build_number}
    ...                                  {env}=${env}
    ...                                  {firstname}=${name}
    ...                                  {capital}='Stockholm'
    DataComparerLibrary.Compare Data Files  ${actual_input_file_template_literal}  ${expected_input_file_template_literal}  delimiter_actual_data=;  delimiter_expected_data=;  template_literals_dict=${literal_dict}

Examples comparing Actual Data with Expected Data

Below there is an example of actual and expected data with some different cases.

Based on current datetime = 2023-09-06 19:04:00 (example):

Actual csv file or 2d-array
id	name	city	start datetime	code	password
87	John	London	2019-09-01 10:00:15	abc1	xxxxxxxx
88	Bert	Amsterdam	2023/09/06 19:02:00		xxxxxxxx
89	Klaas	Brussel	23-8-6 12:04:17	5ghi	xxxxxxxx
90	Joe	Helsinki	08062025 12:04:17	99fg	xxxxxxxx
91	Mike	Berlin	2023/09/06 19:02:00	123	xxxxxxxx
92	Kurt	Paris	2023/09/06 19:02:00	123	xxxxxxxx

Expected csv file or 2d-array
id	name	city	start datetime	code	password
{INTEGER}	John	London	{NOW()-4Y5D:YYYY-MM-DD}	abc1	{PRESENT}
{INTEGER}	Bert	Amsterdam	{NOW():YYYY/MM/DD} {SKIP}	{EMPTY}	{PRESENT}
{INTEGER}	Klaas	Brussel	{NOW()-1M:YY-M-D} {SKIP}	5ghi	{PRESENT}
{INTEGER}	Joe	Helsinki	{NOW()+1Y9M2D:DDMMYYYY} {SKIP}	{SKIP}	{PRESENT}
{INTEGER}	{NOT(“Jack”)}	Berlin	{NOW():YYYY/MM/DD} {SKIP}	{NOT(456)}	{PRESENT}
{INTEGER}	{firstname}	{capital}	{NOW():YYYY/MM/DD} {SKIP}	123	{PRESENT}

Comparing Text

Examples of using the DataComparerLibrary for comparing text in Python

Below there are some examples how to call the methods of the DataComparerLibrary in Python:

a = DataComparer
a.compare_text_files(self, actual_file, expected_file)
a.compare_text_variable_with_text_file(self, actual_text_input, expected_file)
a.compare_text_file_with_text_variable(self, actual_file, expected_text_input)
a.compare_text_variables(self, actual_text_input, expected_text_input)

Examples of using the DataComparerLibrary keywords for comparing text in Robot Framework

Below there are some examples how to call the keywords of the DataComparerLibrary in Robot Framework:

*** Settings ***
Library     DataComparerLibrary

*** Test Cases ***
Testcase_DataComparer
    Examples

*** Keywords ***
Examples
    Run Keyword And Continue On Failure  DataComparerLibrary.Compare Text Files  C:\\Users\\actual.txt   C:\\Users\\expected.txt
    DataComparerLibrary.Compare Text Files  C:\\Users\\actual.txt   C:\\Users\\expected.txt
    DataComparerLibrary.Compare Text Files  C:\\Users\\actual.txt   C:\\Users\\expected.txt
    DataComparerLibrary.Compare Text Files  C:\\Users\\actual.txt   C:\\Users\\expected.txt
    DataComparerLibrary.Compare Text Variable With File  ${actual_text_input}  C:\\Users\\expected.txt
    DataComparerLibrary.Compare Text File With Text Variable  C:\\Users\\actual.txt  ${expected_text_input}
    DataComparerLibrary.Compare Text Variables  ${actual_text_input}  ${expected_text_input}

DataSorter

Introduction

The DataSorter can be used for sorting records in a csv file or text file.

Default sorting

The default sorting is alphabetically based in ascending order on column 0 on all records.

Special sorting

With the DataSorter it is possible to tune the sorting of records.

number_of_header_lines:

Optional argument “number_of_header_lines” can be used to set the number of header records. These records will be excluded from sorting. In case this optional argument is not present the default value is set to 0.

number_of_trailer_lines:

Optional argument “number_of_trailer_lines” can be used to set the number of trailer records. These records will be excluded from sorting. In case this optional argument is not present the default value is set to 0.

sort_on_columns_list:

Optional argument “sort_on_columns_list” can be used to specify one or more columns on which should be sorted and in which order of columns. Sorting of a column can be done in an alphabetic or numeric way.

delimiter:

Optional argument “delimiter” can be used to set the delimiter. Default delimiter is “,” in case of an input file. You can use the option “delimiter” to set another delimiter like “;” or “t” for tab. It is also possible to use a multi-character delimiter like “@#@”.

Examples of using the DataComparerLibrary keywords for sorting data in Robot Framework

Below there are some examples how to call the keywords of the DataComparerLibrary in Robot Framework:

*** Settings ***
Library     DataComparerLibrary

*** Test Cases ***
Testcase_DataSorter
    Examples

*** Keywords ***
Examples
    # sorting examples
    #
    # Sorting alphabetic on column 0, 5 and 4
    ${sorting_column_0_5_4} =  create list   0  5  4
    # Sorting alphabetic on column 4 and 1 and numeric on colum 3
    ${sorting_column_4_3i_1} =  create list   4  ${3}  1


    Run Keyword And Continue On Failure  DataComparerLibrary.Sort Csv Files  C:\\Users\\unsorted.csv   C:\\Users\\sorted.csv
    DataComparerLibrary.Sort Csv Files  C:\\Users\\unsorted.csv   C:\\Users\\sorted.csv  number_of_header_lines=5  sort_on_columns_list=${sorting_column_0_5_4}  number_of_trailer_lines=5  delimiter=;
    DataComparerLibrary.Sort Csv Files  C:\\Users\\unsorted.csv   C:\\Users\\sorted.csv  number_of_header_lines=4  sort_on_columns_list=${sorting_column_4_3i_1}  delimiter=@#@
    DataComparerLibrary.Sort Csv Files  C:\\Users\\unsorted.csv   C:\\Users\\sorted.csv  number_of_trailer_lines=2  delimiter=\t
    DataComparerLibrary.Sort Csv Files  C:\\Users\\unsorted.csv   C:\\Users\\sorted.csv

FileConverter

Introduction

Records in files can be ended by carriage return line feed (CRLF). In some situations separate line feeds (LF) are present within records. For example for an easy way of sorting records this can be a problem.

DataComparerLibrary keywords for preparing data in Robot Framework

The keywords “Remove Separate Lf” and “Replace Separate Lf” support removing/replacing a separate Lf in the data from the input file. The result will be written to an output file.

Examples of using the DataComparerLibrary keywords for preparing data in Robot Framework

Below there are some examples how to call the keywords of the DataComparerLibrary in Robot Framework:

*** Settings ***
Library     DataComparerLibrary

*** Test Cases ***
Testcase_FileConverter
    Remove Separate LF From Data In File
    Replace Separated LF With Character Or String From Data In File

*** Keywords ***
Remove Separate LF From Data In File
    DataComparerLibrary.Remove Separate Lf  ${path_actual_input_files}\\input_file_with_lf.txt  ${path_actual_output_files}\\output_file_without_lf.txt


Replace Separated LF With Character Or String From Data In File
    DataComparerLibrary.Replace Separate Lf  ${input_file_with_separate_lf}   ${output_file_lf_replaced_by_character_or_string}   ${replacement_string}
    DataComparerLibrary.Replace Separate Lf  input_file_with_separate_lf.txt  output_file_lf_replaced_by_character_or_string.txt  abc
    DataComparerLibrary.Replace Separate Lf  input_file_with_separate_lf.txt  output_file_lf_replaced_by_character_or_string.txt  x
    DataComparerLibrary.Replace Separate Lf  input_file_with_separate_lf.txt  output_file_lf_replaced_by_character_or_string.txt  ${SPACE}