crystal-ball 0.1.1

Useful crystal-ball-related stuff.

Crystal-Ball Documentation

If you have hundreds of csvs you need to quickly digest and understand, you can use crystal-ball to help with the onboarding and data exploration phase of your project. You will be able to immediately dive into an expansive data set without getting lost.

Crystal-Ball Features:

• find specific columns and tables you may be interested in, but may have otherwise not known where to look.
• walk through connections between different csvs,
• compare and establish foreign key and primary key relationships by using simple boxplots
• dynamically create a master table of useful information while simultaneously recording your step-by-step process for future reference.

Methods

cb.contains(keywords, all_colnames) -> list

Purpose:

• Determine whether a keyword (substring) exists in a given list of column names (strings).
• Note: This search is case sensitive!

Parameters:

• keywords (list of strings)
  • A key word
• all_colnames (list of strings)
  • List of column names of a table, or for many tables.
  • If no argument is provided, this function will use the column names generated when the run method was called.

Returns:

• list (a list of bools):
  • For each index corresponding to a keyword, True if substring exists in list of strings, otherwise False.

Examples:

colnames = ['id', 'name', 'title']
cb.contains(['name'], colnames) # returns [True]
cb.contains(['Name'], colnames) # returns [False]
cb.contains(['name', 'Name'], colnames) # returns [True, False]

cb.featureSearch(keywords, all_colnames) -> list

Purpose:

• Find features (column names) that contain the substrings specified in keywords.
• Note: This search is case sensitive!

Parameters:

• keywords (list of strings)
  • List of key words that the user is interested in
• colnames (list of strings)
  • List of column names of a table, or for many tables.
  • If no argument is provided, this function will use the column names generated when the run method was called.

Returns:

• list (list of features):
  • List will contain all features (column names) that contains one/all substrings found in keywords.
  • List will be sorted in alphabetical order.

Examples:

colnames = ['id', 'name', 'nameType', 'subSpeciesName', 'title']
cb.featureSearch(['name'], colnames) # returns ['name', 'nameType']
cb.featureSearch(['Name'], colnames) # returns ['subSpeciesName']
cb.featureSearch(['name', 'Name'], colnames) # returns ['name', 'nameType', 'subSpeciesName']