utils

General plotting helpers, save utilities, and palette/color tools.

df = sns.load_dataset('tips')
df.shape

(244, 7)

df.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Setup Helpers

---

set_sns

def set_sns(
    dpi:int=300
)->None:

Set seaborn defaults for notebook display and saved figures.

set_sns(dpi=50)

---

save_svg

def save_svg(
    path:str | pathlib.Path
)->None:

Save the current matplotlib figure as SVG with editable text.

plt.figure()
plt.plot([0, 1], [0, 1])
# save_svg(Path('nbs') / '_tmp_utils.svg')

---

save_pdf

def save_pdf(
    path:str | pathlib.Path
)->None:

Save the current matplotlib figure as PDF with TrueType fonts.

plt.figure()
plt.plot([0, 1], [1, 0])
# save_pdf(Path('nbs') / '_tmp_utils.pdf')

---

save_show

def save_show(
    path:str | pathlib.Path | None=None, # output path when saving instead of showing
    show_only:bool=False, # force plt.show even when no path is provided
)->None:

Show the current figure or save it, then close open figures.

plt.figure()
plt.plot([0, 1], [0.5, 0.5])
# save_show(path=Path('nbs') / '_tmp_utils_show.png')

Palette Helpers

---

get_color_dict

def get_color_dict(
    categories:list, # labels that need colors
    palette:str='tab20', # seaborn palette name
)->dict:

Assign colors to labels while tolerating duplicate category names.

get_color_dict(['A', 'B', 'C'], palette='Set2')

{'A': (0.4, 0.7607843137254902, 0.6470588235294118),
 'B': (0.9882352941176471, 0.5529411764705883, 0.3843137254901961),
 'C': (0.5529411764705883, 0.6274509803921569, 0.796078431372549)}

---

get_plt_color

def get_plt_color(
    palette:dict | list | str, # dict lookup, explicit list, or palette name
    columns:list, # plotted column names in output order
)->list:

Return colors in plotting order for a dict, list, or named palette.

get_plt_color('Set2', ['a', 'b'])

---

get_hue_big

def get_hue_big(
    df:DataFrame, # source dataframe
    hue_col:str, # categorical column used for hue
    cnt_thr:int=10, # minimum count retained in the filtered hue series
)->Series:

Filter a hue column down to categories that meet a count threshold.

# get_hue_big(df, 'day', cnt_thr=40).tolist()

Statistical Annotations

---

add_stats

def add_stats(
    ax, df, value, group, test:str='t-test_ind', loc:str='inside', text_format:str='star', min_n:int=3,
    kwargs:VAR_KEYWORD
):

If value is str: compare between groups (x=group, y=value) If value is list/tuple: compare among values within each group (x=group, hue=‘variable’)

fig, ax = plt.subplots(figsize=(5, 4))
sns.boxplot(data=df, x='sex', y='total_bill', ax=ax)
add_stats(ax, df, value='total_bill', group='sex')