cuoco 0.0.1

Cuoco is a tool for automatic data preprocessing. Cuoco comes from Italy, means chef.

DataChef

DataChef is a tool for automatic processing of data.

Documentation

For installing do: pip install datachef

How it works: DataChef uses a json created by the user to automatically apply data-processing functions to the desired dataset. The Json has the next values:

• input_format: format of the input dataset. Can be csv, parquet, orc and txt
• output_format: format of the resulted dataset. Can be csv, parquet, orc and txt
• new_fileName: name of the new dataset the DataChef will write
• new_file_route: route where to store the new data file
• header: if yor datasets has a header. Can be yes or none
• separator: the separator of your dataset. Only applies if its csv o txt format.
• num_nans: method you want to use against possible numerical nans (include empties). Can be:
  • drop: drop rows that contains nans
  • yes: dont do anything with rows that contains nans
  • mean: fill nans with the mean value of the column
  • median: fill nans with the median value of the column
  • mode: fill nans with the mode value of the column
• str_nans: method you want to use against possible string nans (include empties). Can be:
  • yes: keep nans columns
  • no: drop nans columns
• caps: method you want to use with strings that contains Upper and Lower case letters:
  • no: dont do anything
  • upper: put all strings of string columns to uppercase
  • lower: put all strings of string columns to lowercase
• normalize_method: method to use to normalize numerical columns. Can be:
  • no: dont normalize
  • max_abs: uses max absolute value to normalize
  • min_max: uses min - max value method to normalize
  • z_score: uses z-score value method to normalize
• normalize:
  • write the name of the columns you want to normalize
  • Note: if yor dataset does not have a header, you must write the columns's names you want to normalize in number format, if it has a header you must write the columns's names between ""