This package can read data from a couple of different file types, can summarize data, handle missing values and can encode your data using one hot encoding.
Project description
how to use this package:
after you download the package from PyPi
import this package like the following
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
df = obj.read()
to print your data
print(df)
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())
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