# utils
``` python
df = sns.load_dataset('tips')
df.shape
```
(244, 7)
``` python
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
``` python
def set_sns(
dpi:int=300
)->None:
```
*Set seaborn defaults for notebook display and saved figures.*
``` python
set_sns(dpi=50)
```
------------------------------------------------------------------------
### save_svg
``` python
def save_svg(
path:str | pathlib.Path
)->None:
```
*Save the current matplotlib figure as SVG with editable text.*
``` python
plt.figure()
plt.plot([0, 1], [0, 1])
# save_svg(Path('nbs') / '_tmp_utils.svg')
```
------------------------------------------------------------------------
### save_pdf
``` python
def save_pdf(
path:str | pathlib.Path
)->None:
```
*Save the current matplotlib figure as PDF with TrueType fonts.*
``` python
plt.figure()
plt.plot([0, 1], [1, 0])
# save_pdf(Path('nbs') / '_tmp_utils.pdf')
```
------------------------------------------------------------------------
### save_show
``` python
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.*
``` python
plt.figure()
plt.plot([0, 1], [0.5, 0.5])
# save_show(path=Path('nbs') / '_tmp_utils_show.png')
```
## Palette Helpers
------------------------------------------------------------------------
### get_color_dict
``` python
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.*
``` python
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
``` python
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.*
``` python
get_plt_color('Set2', ['a', 'b'])
```
------------------------------------------------------------------------
### get_hue_big
``` python
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.*
``` python
# get_hue_big(df, 'day', cnt_thr=40).tolist()
```
## Statistical Annotations
------------------------------------------------------------------------
### add_stats
``` python
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’)
``` python
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')
```
