quickshow 0.1.3

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Quick-Show

• Quick-Show is a package that allows you to easily and quickly draw 2D or 3D t-SNE and PCA plots using specific columns of a refined dataframe.
• Quick Show is an abstraction using popular libraries such as sklearn and matplotlib, so it is very light and convenient.
• Note: quich show is sub-modules of other packages to manage quickshow more lightly and use more widly.
• 추가 업데이트 계획이 있으므로, 간단한 함수로 관리하며, 추가 배포 예정 레포의 서브 모듈로 사용함.

Quick Start

$ pip install quickshow

• Create a scatter plot very quickly and easily by inputting a clean dataframe and column names that do not have missing data.

• If the label column does not exist, simply enter None as an argument.

  from quickshow import vis_tsne2d, vis_tsne3d, vis_pca
  
  # Make sample df
  df = pd.DataFrame([3,2,3,2,3,3,1,1])
  df['val'] = [np.array([np.random.randint(0,10000),np.random.randint(0,10000),np.random.randint(0,10000)]) for x in df[0]]
  df.columns = ['labels', 'values']
  
  # Use matplotlib rcparams or returned dataframe for customize your plot.
  return_df = vis_tsne2d(df, 'values', 'labels', False, 'fig1.png')
  return_df = vis_tsne3d(df, 'values', 'labels', False, 'fig2.png')
  return_df = vis_pca(df, 'values', 'labels', 2, False, 'fig3.png')
  return_df = vis_pca(df, 'values', 'labels', 3, False, 'fig4.png')
  

  

  • For more details, please check doc string.

Functions

It contains 3 functions: vis_tsne2d, vis_tsne3d, vis_pca

• (1) vis_tsne2d function: Simple visuallization of 2-dimensional t-distributed stochastic neighbor embedding
• (2) vis_tsne3d function: Simple visuallization of 3-dimensional t-distributed stochastic neighbor embedding
• (3) vis_pca function: Simple visuallization of Principal Component Analysis (PCA)

All function returns the dataframe which used to plot. Thus, use the returned dataframe object to customize your plot. Or use matplotlib's rcparam methods.

References

[1] sklearn.manifold.TSNE https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
[2] sklearn.decomposition.PCA https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
[3] matplotlib https://matplotlib.org/