{
    "info": {
        "author": "Nahom Tamerat",
        "author_email": "nahomt@amestsantim.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# pyCSPro\nPython library for parsing CSPro dictionaries and cases.\n\n## Install\npip:\n```bash\npip install pycspro\n```\n\ngit:\n```bash\ngit clone https://github.com/amestsantim/pycspro\ncd pycspro\npython setup.py install\n```\n\n## Usage\nThe library is simple to use and has only two classes. DictionaryParser and CaseParser.\nThere is also a medium article that explains, in considerable detail, how it works. You can find it here:\n\n[Working With CSPro Data Using Python (Pandas)](https://medium.com/@nahomt/working-with-cspro-data-using-python-pandas-9a6161b84ffa?sk=5e19e932f9090a21432c716aac0e7401)\n\n### DictionaryParser\nThis class receives a raw dictionary text and parses it into a Python dictionary which we can then manipulate to acomplish various tasks.\n\n#### parse()\nThis method accepts the contents of a dictionary file (.dcf) and returns a dictionary object, which is basically a nested Python dictionary.\n```python\nfrom pycspro import DictionaryParser\n\nraw_dictionary = open('CensusDictionary.dcf', 'r').read()\ndictionary_parser = DictionaryParser(raw_dict)\nparsed_dictionary = dictionary_parser.parse()\nprint(json.dumps(parsed_dict, indent=4))\n```\n\n```json\n{\n    \"Dictionary\": {\n        \"Name\": \"CEN2000\",\n        \"Label\": \"Popstan Census\",\n        \"Note\": \"\",\n        \"Version\": \"CSPro 7.2\",\n        \"RecordTypeStart\": 1,\n        \"RecordTypeLen\": 1,\n        \"Positions\": \"Relative\",\n        \"ZeroFill\": true,\n        \"DecimalChar\": false,\n        \"Languages\": [],\n        \"Relation\": [],\n        \"Level\": {\n            \"Name\": \"QUEST\",\n            \"Label\": \"Questionnaire\",\n            \"Note\": \"\",\n            \"IdItems\": [\n                {\n                    \"Name\": \"PROVINCE\",\n                    \"Label\": \"Province\",\n                    \"Note\": \"\",\n                    \"Len\": 2,\n                    \"ItemType\": \"Item\",\n                    \"DataType\": \"Numeric\",\n                    \"Occurrences\": 1,\n                    \"Decimal\": 0,\n                    \"DecimalChar\": false,\n                    \"ZeroFill\": true,\n                    \"OccurrenceLabel\": [],\n                    \"Start\": 2,\n                    \"ValueSets\": [\n                        {\n                            \"Name\": \"PROV_VS1\",\n                            \"Label\": \"Province\",\n                            \"Note\": \"\",\n                            \"Value\": [\n                                \"1;Artesia\",\n                                \"2;Copal\",\n                                \"3;Dari\",\n                                \"4;Eris\",\n                                \"5;Girda\",\n                                \"6;Hali\",\n                                \"7;Kerac\",\n                                \"8;Lacuna\",\n                                \"9;Laya\",\n                                \"10;Lira\",\n                                \"11;Matanga\",\n                                \"12;Patan\",\n                                \"13;Rift\",\n                                \"14;Terra\",\n                                \"15;Tumar\"\n                            ]\n                        }\n                    ]\n                },\n                ...\n```\n\n#### get_column_labels()\nThis method accepts a record name and returns a dictionary where keys are the item names and the values are the item labels for all the items within the given record.\n```json\n{\n    'H01_TYPE': 'Type of housing',\n    'H02_WALL': 'Wall type',\n    'H03_ROOF': 'Roof type',\n    'H04_FLOOR': 'Floor type',\n    'H05_ROOMS': 'Number of rooms',\n    'H06_TENURE': 'Tenure'\n}\n```\nThis is useful for replacing the column labels in the Data Frame.\n```python\nhousing = dfs['HOUSING']\nhousing.rename(columns = dictionary_parser.get_column_labels('HOUSING'))\n```\n\n#### get_value_labels()\nThis method accepts a record name and returns a dictionary where keys are the item names and the values are yet another dictionary. This dictionaries key-value paris are all the possible values and their respective labels.\n```json\n{\n    'P02_REL': {1: 'Head', 2: 'Spouse', 3: 'Child', 4: 'Parent', 5: 'Other', 6: 'Nonrelative', 9: 'Not Reported'},\n    'P03_SEX': {1: 'Male', 2: 'Female'}\n}\n```\n\nThis can be used to replace values by their more meaningful labels in a Data Frame.\n```python\nperson = dfs['PERSON']\nperson.replace(dictionary_parser.get_value_labels('PERSON'))\n```\n\n### CaseParser\nThe CaseParser class is responsible for cutting up raw CSPro cases into tables by using a parsed dictionary (CaseParser). It produces a nested dictionary where each record is yet another dictionary. The resulting format is well suited to be converted into a Pandas Data Frame by using the from_dict method of pandas DataFrame class.\n\nDuring instantiation, you can also pass in a cutting_mask to the CaseParser class to specify only the columns (items) you are interested in. This can be useful when there are a large number of items in a record.\n```python\ncutting_mask = {\n    'QUEST': ['PROVINCE', 'DISTRICT'],\n    'PERSON': ['P03_SEX', 'P04_AGE', 'P11_LITERACY', 'P15_OCC'],\n    'HOUSING': ['H01_TYPE', 'H05_ROOMS', 'H07_RENT', 'H08_TOILET', 'H13_PERSONS']\n}\ncase_parser = CaseParser(parsed_dictionary, cutting_mask)\n```\n\n#### parse()\nThe parse method receives a list of cases and returns a nested dictionary of records.\n```python\nimport pandas as pd\nfrom pycspro import CaseParser\n\ncase_parser = CSProCaseParser(parsed_dictionary)\nparsed_cases = case_parser.parse(cases) # where cases is a list of CSPro cases\n# parsed_cases will be Python dictionary where the keys are the record names\n# and values would be a dictionary with columns as keys and column values as a Python list\nfor table_name, table in parsed_cases.items():\n    pd.DataFrame.from_dict(table)\n```\n\n## Live Demo\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/amestsantim/pycspro-example/master?filepath=Using%20pyCSPro.ipynb)\n\nThere is a Jupyter Notebook on Binder (great project!) that you can play with, in a live environment (in your browser) and see how easy it is to use this library. Please, take it for a spin!\n\n## Syntax Checking\nThis library uses a finite state machine to check the syntax of dictionaries. However, in the current version, some simplifying assumptions were made.\nThese are:\nDictionaries are assumed to have only a single Level\nSubItems are not considered and will cause an error if present\n\n## Performance\nWhen reading and loading cases directly from CSWeb's MySQL database, you should be passing in to the CaseParser about 50,000 cases at a time and then converting the result into a Pandas DataFrame. In the next iteration, send in another 50,000 cases and on return, convert to a DataFrame and append to the previous DataFrame. That way, you can grow your DataFrame to a large size without consuming a lot of memory.\n\n## To Dos\nSome edge cases such as SPECIAL values etc might not have been handled. If you run in to such edge cases, please submit an issue (or even better, a pull request) and hopefully, we will have them ironed out soon enough!\n\n## License\nMIT\n\n",
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