Csv::Import::Analyzer

CsvImportAnalyzer is intended to help perform data analysis on csv (comma seperated), tsv (tab seperated) or ssv (semi-colon seperated) files. It can be used to process large datasets in desired chunk sizes (defaults to 200 rows), gives you a comprehensive analysis on each column with possible datatype, minimum and manimum bounds, if the column can be set to nullable for each column.

Note: This gem expects the first line to be definitve header, as in like column names if the csv file has to be imported to database.

Add this line to your application's Gemfile:

gem 'csv-import-analyzer'

And then execute:

$ bundle

Or install it yourself as:

$ gem install csv-import-analyzer

Calling process on a filename would generate metadata for the sample file and return it as a json object. This metadata would have the following

• High level stats for the given file (E.g. filename, file size, number of rows, number of columns).
• Data manipulation done for pre-processing the file.
• Data analysis on each column as key value pairs.
• By default you would also have MySQL queries that you need to import the file to database.

Below is a sample test.csv file

Year ID,Make ID,Model ID,Description ID,Price ID
1997,Ford,E350,"ac, abs, moon","3000.00"
1999,Chevy,"Venture ""Extended Edition""",,4900.00
1999,"Chevy","Venture ""Extended Edition, Very Large""","",5000.00
1996,Jeep,Grand Che'rokee,"MUST SELL!air, moon roof, loaded",4799.00

To get the data analysis of above file, you can use CsvImportAnalyzer to process the file.

metadata = CsvImportAnalyzer.process("test.csv", {:distinct => 2})

Now the metadata would hold the json object of the comprehensive analysis. Below is what the metadata would be for the sample csv file

puts metadata

{
  "csv_file": {
    "filename": "sampleTab.csv",
    "file_size": 276,
    "record_delimiter": ",",
    "rows": 6,
    "columns": 5,
    "processed_filename": "processed_sampleTab.csv",
    "processed_file_path": "/tmp/processed_sampleTab.csv",
    "processed_file_size": 279,
    "error_report": "/tmp/error_report_sampleTab.csv"
  },
  "data_manipulations": {
    "replace_nulls": true,
    "replace_quotes": true
  },
  "csv_headers": {
    "year_id": {
      "datatype": "int",
      "datatype_analysis": {
        "int": 4
      },
      "distinct_values": "2+"
    },
    "make_id": {
      "datatype": "string",
      "datatype_analysis": {
        "string": 4
      },
      "distinct_values": "2+"
    },
    "model_id": {
      "datatype": "string",
      "datatype_analysis": {
        "string": 4
      },
      "distinct_values": "2+"
    },
    "description_id": {
      "datatype": "string",
      "datatype_analysis": {
        "string": 2
      },
      "distinct_values": [
        "ac, abs, moon",
        "MUST SELL!air, moon roof, loaded"
      ],
      "nullable": true
    },
    "price_id": {
      "datatype": "float",
      "datatype_analysis": {
        "float": 4
      },
      "distinct_values": "2+"
    }
  },
  "sql": {
    "mysql": {
      "create_query": "create table processed_sampletab.csv ( year_id int not null, make_id varchar(255) not null, model_id varchar(255) not null, description_id varchar(255), price_id float not null);",
      "import_query": "COPY processed_sampletab.csv FROM '/tmp/processed_sampleTab.csv' HEADER DELIMITER ',' CSV NULL AS 'NULL';"
    }
  }
}

• Better - Structuring the analysis outputted to csv
• Add support to convert and import xlsx files to csv
• Handle control of processed input file to user

• smarter_csv - For processing the csv in chunks

1. Fork it ( https://github.com/avinash-vllbh/csv-import-analyzer/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request