Useful crystal-ball-related stuff.
Project description
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']
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