# scatter
``` python
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)
``` python
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
``` python
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.*
``` python
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
``` python
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.*
``` python
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
``` python
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.*
``` python
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
``` python
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.*
``` python
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'])
```
