Data-Purifier

A Python library for Automated Exploratory Data Analysis, Automated Data Cleaning and Automated Data Preprocessing For Machine Learning and Natural Language Processing Applications in Python.

Features

• It gives shape, number of categorical and numerical features, description of the dataset, and also the information about the number of null values and their respective percentage.

• For understanding the distribution of datasets and getting useful insights, there are many interactive plots generated where the user can select his desired column and the system will automatically plot it. Plot includes

  1. Count plot
  2. Correlation plot
  3. Joint plot
  4. Pair plot
  5. Pie plot

Demo Output of Auto EDA

Get Started

Install the packages

pip install data-purifier

python -m spacy download en_core_web_sm

Load the module

from datapurifier import Mleda, Nleda, Nlpurifier

Load the dataset and let the magic of automated EDA begin

df = pd.read_csv("./datasets/iris.csv")
ae = MLeda(df)
ae

For Automated EDA and Automated Data Cleaning of NL dataset, load the dataset and pass the dataframe along with the targeted column containing textual data.

nlp_df = pd.read_csv("./datasets/twitter16m.csv", header=None, encoding='latin-1')
nlp_df.columns = ["tweets","sentiment"]

Automated EDA

For Basic EDA, pass the argument basic as argument in constructor

%%time
eda = Nlpeda(nlp_df, "tweets", analyse="basic")
eda.df

For Word based EDA, pass the argument word as argument in constructor

%%time
eda = Nlpeda(nlp_df, "tweets", analyse="word")
eda.unigram_df # for seeing unigram datfarame

Automated Data Cleaning

pure = Nlpurifier(nlp_df, "tweets")

View the processed and purified dataframe

pure.df

Example: https://colab.research.google.com/drive/1J932G1uzqxUHCMwk2gtbuMQohYZsze8U?usp=sharing

Python Package: https://pypi.org/project/data-purifier/