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Tools for Repetitive Data Analysis

Project description

TORDA

  • TOols for Repetitive Data Analysis
  • 분석 업무 진행 시, 반복해서 수행하게 되는 작업들을 모듈화 하기 위한 패키지입니다.

현재 존재하는 함수

visualization class

  • Parameters:
    • dataset : list of pandas series or numpy ndarray
      • pandas series, 혹은 numpy ndarray를 원소로 가지는 리스트를 할당.
  • 사용 예시:
from torda.visualization import distribution as vd
vd = vd(dataset=[np.random.randn(1000), np.random.randn(1000)+2])

plot_histogram_kde(names, title, height, width, kernel = 'gaussian', bins = 10, opacity = 0.75, colors = None, display_quantiles = False, display_maxinum_likelihood = False, display_mean = False)

  • Parameters:
    • names : list of string
      • dataset 리스트 내 각 데이터의 이름.
    • title : string
      • 플롯의 제목.
    • height : int
      • 플롯의 높이.
    • width : int
      • 플롯의 폭.
    • kernel : {'gaussian', 'epanechnikov'}, default = 'gaussian'
      • KDE 진행 시 어떤 kernel function을 사용할 지 선택.
    • bins : int, default = 10
      • 히스토그램의 bin 숫자.
    • opacity : float, default = 0.75
      • 히스토그램의 투명도.
    • colors : list of string, default = None
      • 데이터별 색.
    • display_quantiles : list of int, default = None
      • 정수 리스트를 할당하면, 해당하는 백분위수를 점선으로 표시한다.
    • display_maxinum_peak_density : boolean, default = False
      • True일 경우, KDE 결과에서 가장 밀도가 높은 peak를 점선으로 표시.
    • display_mean : boolean, default = False
      • True일 경우, 평균 값을 점선으로 표시.

plot_box(names, title, height, width, colors = None)

  • Parameters:
    • names : list of string
      • dataset 리스트 내 각 데이터의 이름.
    • title : string
      • 플롯의 제목.
    • height : int
      • 플롯의 높이.
    • width : int
      • 플롯의 폭.
    • colors : list of string, default = None
      • 데이터별 색.

사용 예시

import pandas as pd
import numpy as np
from torda.visualization import distribution as vd

vd = vd(dataset=[np.random.randn(1000), np.random.randn(1000)+2])
vd.plot_histogram_kde(
    names=['Sample A', 'Sample B']
  , title='test_title'
  , height=600, width=1200, bins=60, opacity=0.5
  , display_quantiles=[50], display_maxinum_likelihood=True, display_mean=True
  , kernel = 'gaussian'
)
vd.plot_box(
    names=['Sample A', 'Sample B']
  , title='test_title'
  , height=600, width=1200
)

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