Metadata-Version: 2.1
Name: yasDataPrepKit
Version: 0.4.0
Author: Yaseen Mohammed
Author-email: yasien202020@gmail.com
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6

how to use this package:

#after you download the package from PyPi

#to import this package
import YasinDataPrepKit as dtk

#your can read a csv, json and Excel files and in order to do
#that your need to do the next:

#first you need to make an object and call the ReadingData class
obj = dtk.ReadingData(r"Your file absolute path")

#make sure to always use (r"")when reading your data for 
#correctly read your file path

#after this your call the read() function like this
obj.read()

#after you have done these steps correctly then the rest is easy 
#you can do many function for data summary for instance:

#to find mean of int and float columns you use
print(obj.calculate_mean())

#to find the maximum
print(obj.max_value())

#for handling missing values you can use either the remove or 
#impute methods
print(obj.handle_missing_values('remove'))
print(obj.handle_missing_values('impute'))

#the 'remove' and 'impute' is you specifying the strategy you
#want to use to handle missing values


#there is also a function for encoding using one hot encoding
print(obj.encode_categorical_data())


#here is a list for all functions other than the ones above that you can use from this package

#to calculate sum
print(obj.calculate_sum())

#to calculate minimum
print(obj.min_value())

#to calculate median
print(obj.median_value())

#to calculate variance
print(obj.var_value())

#to calculate standard deviation
print(obj.std_deviation())

#to calculate correlation coefficient
print(obj.cor_coefficient())
