Metadata-Version: 1.1
Name: samoy
Version: 0.4
Summary: samoy is a Python package for machine learning and data science, built on top of Pandas inbuilt libraries. This package will be useful for data pre-processing before starting off any machine learning or data science project as it will ease your entire process of data cleaning without much input from the user
Home-page: https://github.com/samoy-pckg/samoy
Author: Abhishek Pailwan,Priyanka Singh
Author-email: samoyapi@gmail.com
License: MIT
Download-URL: https://github.com/samoy-pckg/samoy/archive/v_04.zip
Description: Auto Data cleaner - samoy samoy is a Python package for machine learning and data science, built on top of Pandas inbuilt libraries. This package will be useful for data pre-processing before starting off any machine learning or data science project as it will ease your entire process of data cleaning without much input from the user. This project is developed in July,2020 by a team of post graduate students and since then many have also contributed. It is currently maintained by the same team.InstallationDependenciessamoy requires:Python (>= 3.6)NumPy (>= 1.13.3)pandas (>=1.1.0)User installationIf you already have a working installation of numpy and pandas, the easiest way to install samoy is using pippip install samoyPackage UtilitiesIn current version of this package it only handles missing,null values and duplicates along with case conversionUtilities supported:Null values handeling : Dropping and imputing(with mean,median as well as custom values)Imputing null values with the threshold as mentioned by the user in terms of percentageMissing values handeling : Dropping and imputing(with mean,median and LRU)Duplicates removalCase conversion like if use wants to convert specific columns or entire column values into either lower or upper caseName of functions provided in this package:Handeling Nulldropnull : This function will drop null in three ways that is dropping all null in the entire dataframe,dropping columns or rows having all nulls and dropping the rows or columns having any of the value as null and return the dataframe after removing null by the method as mentioned by user. By default it drops all the null if no method is mentioned explicitely.dropnull_th : This function will drop the nulls in those columns where the number of nulls is greater than or equal to the percentage specified by the user and returns dataframe having nulls dropped in those columns where number of null is greater than the percentage(mentioned by user while calling function) of total number of records in that column.swapnull : This function will replace all the null values with the three different methods like custom method,mean and median and it will return the dataframe having all null values replaced by the method as chosen by the user.By default if nothing is mentioned explicitely,it will impute nulls with the mean value.swapnull_subset : This function will replace all the null values in the columns as specified by the user with the three different methods like custom method,mean and median and it will return the dataframe having all null values replaced in the selected columns by the method as chosen by the user.By default if nothing is mentioned explicitely,it will impute nulls with the mean value.Handeling Missingswapmissing : This function replace NaN values with mean or median of the specific column. If user mention method as mean or median it will pick it as it is and if user dont want to mention any method then by default mean value to be replaced.swapmissing_subset : This function replace NaN values with mean or median in the specific numeric columns only. If user mentions any method like mean or median it will pick it as it is and if user dont want to mention any method then by default missing value will be replaced to mean value.swapmissing_lru : This function will replace the NaN value with last and next value of the same column and if there are many NaN values with start of the column then it will start replacing same with mean of the same column.dropmissing : This function drops missing values from the given data.dropmissing_rows : This function drops rows whose all values are missing.dropmissing_subset : This function drops missing values as well as NaN values from the selcted columns of the dataframeHandeling Duplicatesdrop_replicatecols : This function will drop duplicate columns and return the dataframe after dropping the columns having same name(names are also typographically matched with respect to upper or lower case).drop_replicates : This function drops duplicate rows that are present in enitre column of dataframe or within the subset of columns as specifies by users.Case conversionaltercase : This function is usually to convert the content of dataframe either in lower case or upper case.altercase_subset : This function is usually to convert the content of only selected columns either in lower case or upper case and returns dataframe having the content of mentioned columns either in lower or upper case.Details of each functionsTo know the detailed summary of each function along with its parameters and a demo example to use it,please use command help(name_of_function)Contact Us:Email: samoyapi@gmail.com
Keywords: DATA SCIENCE,MACHINE LEARNING,DATA CLEANING,DATA PREPROCESSING,FEATURE ENGINEERING,DESCRIPTIVE ANALYSIS,PREDICTIVE ANALYSIS,STATISTICAL MODELING,PYTHON,PYSPARK
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
