Useful crystal-ball-related stuff.
Project description
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.
CrystalBall Features
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find specific columns and tables you may be interested in, but may have otherwise not known where to look.
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walk through connections between different csvs,
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compare and establish foreign key and primary key relationships by using simple boxplots
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dynamically create a master table of useful information while simultaneously recording your step-by-step process for future reference.
Installation and Usage
pip install crystal-ball</programlisting> You can start using CrystalBall right away by importing it and initializing it with a relative directory containing the CSVs.
import crystalball as cb
ball = cb.run("insert relative directory here")
Methods
Note that all methods that involve searching via keynames are case sensitive.
cb.contains(keywords: list, all_colnames: list=None) → list
Check if keywords exist in all_colnames.
cb.featureSearch(keywords: list, all_colnames: list=None) → list
Find the columns that contain the keywords.
cb.tableSearch(keywords: list, csvname_to_colnames_list=None, mode: str=UNION) → list
Find the tables that contain the keywords.
cb.openTable(rel_dir: str, indices: list=[0]) → DataFrame
Open the csv that is referenced by the given relative directory.
cb.subTable(supertable: DataFrame, chosen_index: list, chosen_columns: list) → DataFrame
Create a subtable from a supertable.
cb.mergeTables(tables: list) → DataFrame
Sequentially merge a list of tables that all share a common index.
cb.analyzeRelationships(to_analyze: list, visualize: bool=True) → DataFrame
Analyze basic stats of one or more different Series.
compareRelationship(to_analyze1: Series, to_analyze2: Series, visualize: bool=False) → DataFrame
Compare and contrast the difference between two Series.
cb.export(df_to_export: DataFrame, write_to: str, export_type: str=CSV) → None
Exports contents of dataframe to relative location specified by write_to arg.
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