Here's a brief overview of the functionalities provided by this class:
Data Initialization:
The start method is a convenient way
to load multiple data files with fallback options
for manual path input, accommodating the common scenario
where data not in expected locations.
Row Name Correction:
Through row_names_fix, it offers a
flexible approach to rectify row names
based on either a typo correction or
column-based search, enhancing data
consistency for downstream analysis.
Sublist Generation:
The sublists_ method provides a way
to chunk a list into sublists of
approximately equal size,
which can be useful in batch processing
or when dividing data for cross-validation purposes.
Statistical Summary:
With stats_, you can quickly obtain
a comprehensive statistical summary
of a data subset, including quantiles,
which are crucial for understanding
data distribution and variance.
Array Conversion:
The num_toarray method transforms dataframe columns
into numpy arrays, catering to scenarios
where numerical operations are more efficiently handled
with NumPy's capabilities.
Data Extraction:
Through simple_extract_, you facilitate the extraction of
specific columns from a dataframe, supporting scenarios
where only a subset of the data is needed for analysis.
Data Validation:
The validation_ method provides a mechanism to filter data
based on a differential condition, allowing for
the identification of significant changes or
anomalies within a dataset.
Data Expansion:
With extract_, lists within dataframe cells can be
expanded into separate columns, aiding in
the normalization of data structures for analysis.
PPG Signal Column Detection:
Lastly, simple_PPG_iloc_detector helps in
identifying columns related to photoplethysmogram (PPG) signals,
demonstrating the class's applicability to
specific biomedical signal processing tasks.
Each method is designed with static accessibility, ensuring that they can be utilized without the need to instantiate the class, thereby providing a straightforward interface for users to interact with their data.
import main
import pandas as pd
maria = main.Maria()
data_frame = pd.read_excel("path_to_your_file.xlsx")
fixed_data_frame = maria.row_names_fix(data_frame)
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