scatter

Dimensionality-reduction, scatter, and correlation. 
df = sns.load_dataset('penguins').dropna().reset_index(drop=True)
df2 = df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]
print(df.shape)
print(df2.shape)
(333, 7)
(333, 4)
df.head()
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
0 Adelie Torgersen 39.1 18.7 181.0 3750.0 Male
1 Adelie Torgersen 39.5 17.4 186.0 3800.0 Female
2 Adelie Torgersen 40.3 18.0 195.0 3250.0 Female
3 Adelie Torgersen 36.7 19.3 193.0 3450.0 Female
4 Adelie Torgersen 39.3 20.6 190.0 3650.0 Male

Dimensionality Reduction

reduce_feature


def reduce_feature(
    df:DataFrame, # numeric feature matrix
    method:str='pca', # one of pca, tsne, umap
    complexity:int=20, # perplexity for tsne or neighbors for umap
    n:int=2, # number of output dimensions
    load:str | pathlib.Path | None=None, # path to a previously fitted reducer
    save:str | pathlib.Path | None=None, # optional path for persisting the reducer
    seed:int=123, # random_state used by reducers that support it
    kwargs:VAR_KEYWORD
)->DataFrame: # forwarded reducer kwargs

Reduce a feature matrix to a lower-dimensional embedding dataframe.

reduce_feature(df2, method='pca', n=2)
PCA1 PCA2
0 -457.325073 -13.351587
1 -407.252205 -9.179113
2 -957.044676 8.160444
3 -757.115802 1.867653
4 -557.177302 -3.389158
... ... ...
328 718.068699 2.338199
329 643.090909 4.280699
330 1543.098355 -2.232010
331 992.994900 -4.605154
332 1193.002584 -5.417312

333 rows × 2 columns

Scatter Plots

plot_2d


def plot_2d(
    embedding_df:DataFrame, # dataframe with at least two numeric columns
    hue:str | None=None, # column name used for color when present in embedding_df
    palette:str='tab20', # seaborn palette name
    legend:bool=False, # whether to draw a legend
    name_list:list[str] | None=None, # labels used to annotate points
    s:int=20, # marker size
    legend_title:str | None=None, # optional legend title override
    kwargs:VAR_KEYWORD
):

Plot the first two columns of an embedding dataframe.

df2 = reduce_feature(df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']], method='pca', n=2)
df2['species'] = df['species'].values
plot_2d(df2, hue='species', legend=True)

plot_cluster


def plot_cluster(
    df:DataFrame, # numeric feature matrix, optionally including a hue column
    method:str='pca', # one of pca, tsne, umap
    hue:str | pandas.Series | list | None=None, # hue column name or per-row hue values
    complexity:int=30, # perplexity for tsne or neighbors for umap
    palette:str='tab20', # seaborn palette name
    legend:bool=False, # whether to draw a legend
    name_list:list[str] | None=None, # point annotations
    seed:int=123, # random seed passed to the reducer
    s:int=50, # marker size
    legend_title:str | None=None, # optional legend title override
    kwargs:VAR_KEYWORD
):

Reduce features and immediately plot the first two embedding dimensions.

plot_cluster(df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'species']], method='pca', hue='species', legend=True)

Correlation Plot

plot_rel


def plot_rel(
    df:DataFrame, # dataframe that contains the x and y columns
    x:str, # x-axis column name
    y:str, # y-axis column name
    text_location:tuple=(0.8, 0.1), # annotation location in axes coordinates
    method:str | None='spearman', # one of spearman, pearson, or None
    index_list:list[str] | None=None, # row labels to annotate
    hue:str | None=None, # optional categorical hue column
    reg_line:bool=True, # whether to draw a regression line when hue is used
    data:NoneType=None, x_estimator:NoneType=None, x_bins:NoneType=None, x_ci:str='ci', scatter:bool=True,
    fit_reg:bool=True, ci:int=95, n_boot:int=1000, units:NoneType=None, seed:NoneType=None, order:int=1,
    logistic:bool=False, lowess:bool=False, robust:bool=False, logx:bool=False, x_partial:NoneType=None,
    y_partial:NoneType=None, truncate:bool=True, dropna:bool=True, x_jitter:NoneType=None, y_jitter:NoneType=None,
    label:NoneType=None, color:NoneType=None, marker:str='o', scatter_kws:NoneType=None, line_kws:NoneType=None,
    ax:NoneType=None
):

Plot a pairwise relationship with an optional correlation annotation.

df2 = df[['bill_length_mm', 'flipper_length_mm', 'species']].head(12).copy()
df2.index = [f'pt{i}' for i in range(len(df2))]
plot_rel(df2, x='bill_length_mm', y='flipper_length_mm', hue='species', index_list=['pt0', 'pt11'])