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.
- dataset : list of pandas series or numpy ndarray
- 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.
- names : list of string
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.
- names : list of string
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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