# bar
``` python
df = sns.load_dataset('tips').dropna()
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 |
## Distribution Plots
------------------------------------------------------------------------
### plot_hist
``` python
def plot_hist(
df:DataFrame, # dataframe containing the values to plot
x:str, # numeric column name
figsize:tuple=(6, 2), # figure size in inches
data:NoneType=None, y:NoneType=None, hue:NoneType=None, weights:NoneType=None, # Vector variables
stat:str='count', bins:str='auto', binwidth:NoneType=None,
binrange:NoneType=None, # Histogram computation parameters
discrete:NoneType=None, cumulative:bool=False, common_bins:bool=True, common_norm:bool=True,
multiple:str='layer', element:str='bars', fill:bool=True, shrink:int=1, # Histogram appearance parameters
kde:bool=False, kde_kws:NoneType=None,
line_kws:NoneType=None, # Histogram smoothing with a kernel density estimate
thresh:int=0, pthresh:NoneType=None, pmax:NoneType=None, cbar:bool=False, cbar_ax:NoneType=None,
cbar_kws:NoneType=None, # Bivariate histogram parameters
palette:NoneType=None, hue_order:NoneType=None, hue_norm:NoneType=None,
color:NoneType=None, # Hue mapping parameters
log_scale:NoneType=None, legend:bool=True, ax:NoneType=None, # Axes information
):
```
*Plot a histogram with a KDE overlay and polygon bins.*
``` python
plot_hist(df, 'total_bill')
```
## Count And Bar Plots
------------------------------------------------------------------------
### plot_count
``` python
def plot_count(
cnt:Series, # output of df[col].value_counts()
tick_spacing:float | None=None, # optional major tick interval
palette:str='tab20', # seaborn palette name
):
```
*Plot horizontal counts from a value-count series.*
``` python
plot_count(df['day'].value_counts())
```
------------------------------------------------------------------------
### plot_bar
``` python
def plot_bar(
df:DataFrame, # long-form dataframe
value:str, # numeric column name
group:str, # grouping column name
title:str | None=None, # optional plot title
figsize:tuple=(12, 5), # figure size in inches
fontsize:int=14, # axis label and tick size
dots:bool=True, # whether to overlay strip dots
rotation:float=90, # x tick rotation angle
ascending:bool=False, # sort group means ascending when True
ymin:float | None=None, # optional lower y-axis bound
data:NoneType=None, x:NoneType=None, y:NoneType=None, hue:NoneType=None, order:NoneType=None,
hue_order:NoneType=None, estimator:str='mean', errorbar:tuple=('ci', 95), n_boot:int=1000, seed:NoneType=None,
units:NoneType=None, weights:NoneType=None, orient:NoneType=None, color:NoneType=None, palette:NoneType=None,
saturation:float=0.75, fill:bool=True, hue_norm:NoneType=None, width:float=0.8, dodge:str='auto', gap:int=0,
log_scale:NoneType=None, native_scale:bool=False, formatter:NoneType=None, legend:str='auto', capsize:int=0,
err_kws:NoneType=None, ci:Deprecated=, errcolor:Deprecated=,
errwidth:Deprecated=, ax:NoneType=None
):
```
*Plot a bar chart from an unstacked dataframe.*
``` python
plot_bar(df, value='total_bill', group='day')
```
------------------------------------------------------------------------
### plot_group_bar
``` python
def plot_group_bar(
df:DataFrame, # wide-form dataframe
value_cols:list, # numeric columns to melt into grouped bars
group:str, # grouping column preserved during melt
figsize:tuple=(12, 5), # figure size in inches
order:NoneType=None, # optional x order passed to seaborn
title:str | None=None, # optional plot title
fontsize:int=14, # axis label and tick size
rotation:float=90, # x tick rotation angle
data:NoneType=None, x:NoneType=None, y:NoneType=None, hue:NoneType=None, hue_order:NoneType=None,
estimator:str='mean', errorbar:tuple=('ci', 95), n_boot:int=1000, seed:NoneType=None, units:NoneType=None,
weights:NoneType=None, orient:NoneType=None, color:NoneType=None, palette:NoneType=None, saturation:float=0.75,
fill:bool=True, hue_norm:NoneType=None, width:float=0.8, dodge:str='auto', gap:int=0, log_scale:NoneType=None,
native_scale:bool=False, formatter:NoneType=None, legend:str='auto', capsize:int=0, err_kws:NoneType=None,
ci:Deprecated=, errcolor:Deprecated=, errwidth:Deprecated=, ax:NoneType=None
):
```
*Plot grouped bars after melting multiple value columns.*
``` python
plot_group_bar(df, value_cols=['total_bill', 'tip'], group='day')
```
------------------------------------------------------------------------
### plot_stacked
``` python
def plot_stacked(
df:DataFrame, # dataframe containing the stacked categories
group:str, # x-axis categorical column
hue:str, # stacked hue column
figsize:tuple=(5, 4), # figure size in inches
xlabel:str | None=None, # x-axis label override
ylabel:str | None=None, # y-axis label override
add_value:bool=True, # whether to annotate total counts
kwargs:VAR_KEYWORD
):
```
*Plot stacked counts for a categorical column.*
``` python
plot_stacked(df, group='day', hue='sex')
```
## Distribution Comparisons
------------------------------------------------------------------------
### plot_violin
``` python
def plot_violin(
df:DataFrame, # long-form dataframe with value and group columns
value:str='value', # numeric column name
group:str='variable', # grouping column name
ylabel:str | None=None, # optional y-axis label override
dots:bool=True, # whether to overlay strip dots
figsize:tuple=(5, 3), # figure size in inches
kwargs:VAR_KEYWORD
):
```
*Plot violin plots with optional strip dots.*
``` python
df2 = df[['time', 'total_bill']].rename(columns={'time': 'variable', 'total_bill': 'value'})
plot_violin(df2)
```
------------------------------------------------------------------------
### plot_box
``` python
def plot_box(
df:DataFrame, # long-form dataframe
value:str, # numeric column name
group:str, # grouping column name
title:str | None=None, # optional plot title
figsize:tuple=(6, 3), # figure size in inches
fontsize:int=14, # axis label and tick size
dots:bool=True, # whether to overlay strip dots
rotation:float=90, # x tick rotation angle
data:NoneType=None, x:NoneType=None, y:NoneType=None, hue:NoneType=None, order:NoneType=None,
hue_order:NoneType=None, orient:NoneType=None, color:NoneType=None, palette:NoneType=None, saturation:float=0.75,
fill:bool=True, dodge:str='auto', width:float=0.8, gap:int=0, whis:float=1.5, linecolor:str='auto',
linewidth:NoneType=None, fliersize:NoneType=None, hue_norm:NoneType=None, native_scale:bool=False,
log_scale:NoneType=None, formatter:NoneType=None, legend:str='auto', ax:NoneType=None
):
```
*Plot a box plot ordered by the group median.*
``` python
plot_box(df, value='total_bill', group='day')
```
## Pie And Count Labels
------------------------------------------------------------------------
### plot_pie
``` python
def plot_pie(
value_counts:Series, # categorical counts
hue_order:list[str] | None=None, # explicit slice order
labeldistance:float=0.8, # distance of labels from the center
fontsize:int=12, # label font size
font_color:str='black', # label font color
palette:str='tab20', # seaborn palette name
figsize:tuple=(4, 3), # figure size in inches
):
```
*Plot a pie chart from a value-count series.*
``` python
plot_pie(df['day'].value_counts())
```
------------------------------------------------------------------------
### plot_cnt
``` python
def plot_cnt(
cnt:Series, # output of df[col].value_counts()
xlabel:str | None=None, # x-axis label override
ylabel:str='Count', # y-axis label override
figsize:tuple=(6, 3), # figure size in inches
):
```
*Plot vertical counts with labels above the bars.*
``` python
plot_cnt(df['day'].value_counts())
```
## Composition
------------------------------------------------------------------------
### calculate_pct
``` python
def calculate_pct(
df:DataFrame, # source dataframe
bin_col:str, # binned x-axis column
hue_col:str, # stacked hue column
)->DataFrame:
```
*Calculate within-bin percentages for a stacked composition chart.*
``` python
df2 = sns.load_dataset('titanic').dropna(subset=['class', 'sex']).reset_index(drop=True)
calculate_pct(df2, 'class', 'sex')
```
| sex |
female |
male |
| class |
|
|
| First |
43.518519 |
56.481481 |
| Second |
41.304348 |
58.695652 |
| Third |
29.327902 |
70.672098 |
------------------------------------------------------------------------
### plot_composition
``` python
def plot_composition(
df:DataFrame, # source dataframe
bin_col:str, # binned x-axis column
hue_col:str, # stacked hue column
palette:dict | list | str='tab20', # colors passed to get_plt_color
legend_title:str | None=None, # legend title override
rotation:float=45, # x tick rotation angle
xlabel:str | None=None, # x-axis label override
ylabel:str='Percentage', # y-axis label override
figsize:tuple=(5, 3), # figure size in inches
):
```
*Plot stacked percentages for a bin-by-category composition.*
``` python
plot_composition(df2, 'class', 'sex')
```
