|
USA-2721-Publications 회사 디렉토리
|
회사 뉴스 :
- seaborn. heatmap — seaborn 0. 11. 2 documentation
Plot a heatmap for data centered on 0 with a diverging colormap: >>> normal_data = np random randn ( 10 , 12 ) >>> ax = sns heatmap ( normal_data , center = 0 ) Plot a dataframe with meaningful row and column labels:
- Annotated heatmaps — seaborn 0. 13. 2 documentation
Annotated heatmaps# seaborn components used: set_theme(), load_dataset(), heatmap()
- Plotting a diagonal correlation matrix — seaborn 0. 13. 2 documentation
seaborn components used: set_theme(), diverging_palette(), heatmap() from string import ascii_letters import numpy as np import pandas as pd import seaborn as sns import matplotlib pyplot as plt sns set_theme ( style = "white" ) # Generate a large random dataset rs = np random
- Discovering structure in heatmap data — seaborn 0. 13. 2 documentation
Discovering structure in heatmap data# seaborn components used: set_theme(), load_dataset(), husl_palette(), clustermap()
- Annotated heatmaps — seaborn 0. 10. 1 documentation
Annotated heatmaps¶ Python source code: [download source: heatmap_annotation py]
- Scatterplot heatmap — seaborn 0. 13. 2 documentation
Scatterplot heatmap# seaborn components used: set_theme(), load_dataset(), relplot()
- seaborn. clustermap — seaborn 0. 13. 2 documentation
Plot a heatmap with row and column clustering: iris = sns load_dataset ( "iris" ) species = iris pop ( "species" ) sns clustermap ( iris ) Change the size and layout of the figure:
- seaborn: statistical data visualization — seaborn 0. 13. 2 documentation
Seaborn is a Python data visualization library based on matplotlib It provides a high-level interface for drawing attractive and informative statistical graphics For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper
|
|