M/Phm 6ddlmZmZmZmZmZmZddlZddl m Z ddl m Z ee Z dede defdZdede defd Zdede deeee fe ffd Zdede deeee fe ffd Zdede deeee e ffd Zd ejde defdZdS))DictListSequenceTupleTypeUnionN)VisionsBaseType)VisionsTypesetdatatypesetreturnc,||S)a3Casts a DataFrame into a typeset by first performing column wise type detection against a provided typeset Args: data: the DataFrame to cast typeset: the Typeset in which we cast Returns: A tuple of the casted DataFrame and the types to which the columns were cast )cast_to_detectedr r s R/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/visions/functional.pyrr   # #D ) ))c,||S)a3Casts a DataFrame into a typeset by first performing column wise type inference against a provided typeset Args: data: the DataFrame to cast typeset: the Typeset in which we cast Returns: A tuple of the casted DataFrame and the types to which the columns were cast )cast_to_inferredrs rrrrrc,||S)aInfer the current types of each column in the DataFrame given the typeset. Args: data: the DataFrame to infer types on typeset: the Typeset that provides the type context Returns: A dictionary with a mapping from column name to type ) infer_typers rrr's   d # ##rc,||S)zDetect the type in the base graph Args: data: the DataFrame to detect types on typeset: the Typeset that provides the type context Returns: A dictionary with a mapping from column name to type ) detect_typers rrr4s   t $ $$rcBg}t||}t||}t|trt|tsJ||zD]&}||||||f'|S)a&Compare the types given by inference on the base graph and the relational graph Args: data: the sequence to detect types on typeset: the Typeset that provides the type context Examples: >>> for column, type_before, type_after in compare_detect_inference_frame(data, typeset): >>> print(f"{column} was {type_before} is {type_after}") See Also: :doc:`type_inference_report_frame `: Formatted report of the output of this function )rr isinstancedictkeysappend)r r comparisonsdetected_typesinferred_typeskeys rcompare_detect_inference_framer#As"K w//Ng..N nd + + 11""$$~':':'<'<<LLC!4nS6IJKKKK rdfc <d}td|jD|z}d}d}d}t||D]5\}}} || k} | rd} |dz }nd} ||d |d |d |d | d | d |d z }6|d |t |j z }|S)aqReturn formatted report of the output of `compare_detect_inference_frame`. Args: df: the DataFrame to detect types on typeset: the Typeset that provides the type context Returns: Text-based comparative type inference report Examples: >>> import pandas as pd >>> from visions.functional import type_inference_report_frame >>> from visions.typesets import StandardSet >>> >>> typeset = StandardSet() >>> df = pd.read_csv('dataset.csv') >>> >>> report = type_inference_report_frame(df, typeset) >>> print(report) c34K|]}t|VdS)N)len).0columns r z.type_inference_report_frame..vs(AAFCKKAAAAAArrz!=z==z < z z@In total {change_count} out of {type_count} types were changed. ) change_count type_count)maxcolumnsr#formatr() r$r paddingmax_column_lengthmax_type_lengthreportr0r* type_before type_afterchangedfills rtype_inference_report_framer=`s0*GAAbjAAAAAGKO FL+I"g+V+V   ' Z+  D A LLD 3* 3 P 3 3k 3_ 3 P 3 3 3 3 3, 3 2 3 3 3   KRR%#bj// S  F Mr)typingrrrrrrpandaspdvisions.types.typer visions.typesets.typesetr Trrstrrrr# DataFramer=rrrGs;;;;;;;;;;;;;;;;......333333 *8 *n * * * * * *8 *n * * * * * $X $ $5c1fq;Q $ $ $ $ %h % %E$sAv,PQ/,BL,>,c,,,,,,r