datapreprep 0.0.7

A Data Pre Processing Package

This is a Data pre processing package where you can Treat 1) Missing Values using Traditional method - Mean , Median, Mode,Knn Method 2) Outlier Treatment using - IQR , Zscore 3) Feature Scaling using - Standard Scalar , Min Max Scalar, Robust Scalar, Max absolute scalar.

——————Missing Value Treatment———————————

Mean - treat_mean(dataframe) Median - treat_median(dataframe) Mode - treat_mode(dataframe) KNN - treat_knn(dataframe,int) # int specify nearest neighbour by default 1

———Get information of a dataframe ———————————

info(dataframe)

————————–Outlier Treatment—————————————————–

IQR — ot_iqr(dataframe,column_name)

Zscore– ot_zscore(dataframe,column_name)

————————————–Feature Scaling—————————————

Standard Scalar — f_standardscalar(dataframe)

Min Max Scalar — f_minmax(dataframe)

Robust Scalar —- f_robustscalar(dataframe)

Max absolute Scalar — f_maxabs(dataframe)

——————Data Visualization———————————————————————-

bar(df) heatmap(df) matrix(df) dendrogram(df) geoplot(df)

——————————You can also use the GUI version of our package—————————————

———–We’ll love it if give it a try——————-

https://share.streamlit.io/mohammed-muzzammil/data_pre_processing/main/st1.py

————————-More Information on our Website—————————————– https://mohammed-muzzammil.github.io/dataprepreps

Change Log

0.0.1 (22/11/2020)

• First Release

0.0.5 (29/11/2020)

-Fifth Realease

Added Data Visualization

0.07 (9/12/2020)

Advance Missing value Treatment Method