Skip to main content
Python Software Foundation 20th Year Anniversary Fundraiser  Donate today!

Data Science Library

Project description

polar

polar is a Python module that contains simple to use data science functions. It is built on top of SciPy, scikit-learn, seaborn and pandas.

Installation

If you already have a working installation of numpy and scipy, the easiest way to install parkitny is using pip:

pip install polar seaborn pandas scikit-learn scipy matplotlib numpy nltk -U

Dependencies

polar requires:

  • Python (>= 3.5)
  • NumPy (>= 1.11.0)
  • SciPy (>= 0.17.0)
  • Seaborn (>= 0.9.0)
  • scikit-learn (>= 0.21.3)
  • nltk (>= 3.4.5)
  • python-pptx (>= 0.6.18)
  • cryptography (> 2.8)
  • imblearn

Jupyter Notebook Examples

Here is the link to the jupyter notebook with all the exmples that are described below Polar-Examples

ACA (Automated Cohort Analysis) Example

The ACA creates three heatmaps for each feature in the data set.

  • Conversion heatmap - conversion per feature value
  • Distribution heatmap - distribution per feature value
  • Size heatmap - total samples per feature value

Data File: ACA_date.csv

Final Result Power Point: ACA.pptx

import pandas as pd
import polar as pl
from pptx import Presentation
%matplotlib inline

url = "https://raw.githubusercontent.com/pparkitn/imagehost/master/ACA_date.csv"
data_df=pd.read_csv(url)

prs = Presentation()    
pl.create_title(prs,'ACA')
for chart in pl.ACA_create_graphs(data_df,'date','label'):
    pl.add_chart_slide(prs,chart[0],chart[1])
pl.save_presentation(prs,filename = 'ACA')

Conversion: Image

Distribution: Image

Samples: Image

EDA Example

import pandas as pd
import openml
import polar as pl

dataset = openml.datasets.get_dataset(31)
X, y, categorical_indicator, attribute_names = \
dataset.get_data(target=dataset.default_target_attribute,dataset_format='dataframe')

openml_df = pd.DataFrame(X)
openml_df['target'] = y

data_df = pl.analyze_correlation(openml_df,'target')
pl.get_heatmap(data_df,'correlation_heat_map.png',1.1,14,'0.1f',0,100,5,5)

Image

data_df = pl.analyze_association(openml_df,'target',verbose=0)
pl.get_heatmap(data_df,'association_heat_map.png',1.1,12,'0.1f',0,100,10,10)

Image

print(pl.analyze_df(openml_df, 'target',10))

Image

data_df = pl.get_important_features(openml_df,'target')
pl.get_bar(data_df,'bar.png','Importance','Feature_Name')

Image

NLP Example

import nltk
nltk.download('wordnet')
import pandas as pd
import polar as pl
from cryptography.fernet import Fernet

url = "https://raw.githubusercontent.com/pparkitn/imagehost/master/test_real_or_not_from_kaggle.csv"
data_df=pd.read_csv(url)

data_df.drop(columns=['id','keyword','location'], inplace=True)
data_df.head(3)

Image

key = Fernet.generate_key()
data_df['text_encrypted'] =  data_df['text'].apply(pl.encrypt_df,args=(key,))
data_df['text_decrypted'] =  data_df['text_encrypted'].apply(pl.decrypt_df,args=(key,))

data_df['text_stem'] = data_df['text_decrypted'].apply(pl.nlp_text_process,args=('stem',))
data_df['text_stem_lem'] = data_df['text_stem'].apply(pl.nlp_text_process,args=('lem',))

data_df.head(3)

Image

cluster_df = pl.nlp_cluster(data_df, 'text_stem_lem',  10, 'text_cluster',1.0,1,100,1,'KMeans',(1,2))[0]
cluster_df.groupby(['text_cluster']).count()

Image

cluster_df[cluster_df['text_cluster']==9]['text_stem_lem']

Image

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for polar, version 0.0.118
Filename, size File type Python version Upload date Hashes
Filename, size polar-0.0.118-py3-none-any.whl (10.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size polar-0.0.118.tar.gz (11.7 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page