# plotnine.geoms.geom_col¶

class plotnine.geoms.geom_col(*args, **kwargs)[source]

Bar plot with base on the x-axis

This is an alternate version of geom_bar that maps the height of bars to an existing variable in your data. If you want the height of the bar to represent a count of cases, use geom_bar.

Usage

geom_col(mapping=None, data=None, stat='identity', position='stack',
na_rm=False, inherit_aes=True, show_legend=None, width=None,
**kwargs)


Only the mapping and data can be positional, the rest must be keyword arguments. **kwargs can be aesthetics (or parameters) used by the stat.

Parameters:
mapping : aes, optional

Aesthetic mappings created with aes(). If specified and inherit.aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic Default value
x
y
alpha 1
color None
fill '#595959'
group
linetype 'solid'
size 0.5

The bold aesthetics are required.

data : dataframe, optional

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str or stat, optional (default: identity)

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str or position, optional (default: stack)

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool, optional (default: False)

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool, optional (default: True)

If False, overrides the default aesthetics.

show_legend : bool or dict, optional (default: None)

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

width : float, (default: None)

Bar width. If None, the width is set to 90% of the resolution of the data.

## Examples¶

In [1]:

import pandas as pd
import numpy as np

from plotnine import *

%matplotlib inline


### Two Variable Bar Plot¶

Visualising on a single plot the values of a variable that has nested (and independent) variables

Create the data

In [2]:

df = pd.DataFrame({
'variable': ['gender', 'gender', 'age', 'age', 'age', 'income', 'income', 'income', 'income'],
'category': ['Female', 'Male', '1-24', '25-54', '55+', 'Lo', 'Lo-Med', 'Med', 'High'],
'value': [60, 40, 50, 30, 20, 10, 25, 25, 40],
})
df['variable'] = pd.Categorical(df['variable'], categories=['gender', 'age', 'income'])

df

Out[2]:

category value variable
0 Female 60 gender
1 Male 40 gender
2 1-24 50 age
3 25-54 30 age
4 55+ 20 age
5 Lo 10 income
6 Lo-Med 25 income
7 Med 25 income
8 High 40 income

We want to visualise this data and at a galance get an idea to how the value breaks down along the categorys for the different variable. Note that each variable has different categorys.

First we make a simple plot with all this information and see what to draw from it.

In [3]:

(ggplot(df, aes(x='variable', y='value', fill='category'))
+ geom_col()
)

Out[3]:

<ggplot: (97654321012345679)>


All the values along each variable add up to 100, but stacked together the difference within and without the groups is not clear. The solution is to dodge the bars.

In [4]:

(ggplot(df, aes(x='variable', y='value', fill='category'))
+ geom_bar(stat='identity', position='dodge'))                     # modified

Out[4]:

<ggplot: (97654321012345679)>


This is good, it gives us the plot we want but the legend is not great. Each variable has a different set of categorys, but the legend has them all clamped together. We cannot easily change the legend, but we can replicate it's purpose by labelling the individual bars.

To do this, we create a geom_text with position_dodge(width=0.9) to match the ratio of the space taken up by each variable. If there was no spacing between the bars of different variables, we would have width=1.

A minor quack, when text extends beyond the limits we have to manually make space or it would get clipped. Therefore we adjust the bottom y limits.

In [5]:

dodge_text = position_dodge(width=0.9)                              # new

(ggplot(df, aes(x='variable', y='value', fill='category'))
+ geom_bar(stat='identity', position='dodge', show_legend=False)   # modified
+ geom_text(aes(y=-.5, label='category'),                          # new
position=dodge_text,
color='gray', size=8, angle=45, va='top')
+ lims(y=(-5, 60))                                                 # new
)


Out[5]:

<ggplot: (97654321012345679)>


Would it look too crowded if we add value labels on top of the bars?

In [6]:

dodge_text = position_dodge(width=0.9)

(ggplot(df, aes(x='variable', y='value', fill='category'))
+ geom_bar(stat='identity', position='dodge', show_legend=False)
+ geom_text(aes(y=-.5, label='category'),
position=dodge_text,
color='gray', size=8, angle=45, va='top')
+ geom_text(aes(label='value'),                                    # new
position=dodge_text,
size=8, va='bottom', format_string='{}%')
+ lims(y=(-5, 60))
)

Out[6]:

<ggplot: (97654321012345679)>


That looks okay. The values line up with the categorys because we used the same dodge parameters. For the final polish, we remove the y-axis, clear out the panel and make the variable and category labels have the same color.

In [7]:

dodge_text = position_dodge(width=0.9)
ccolor = '#555555'

(ggplot(df, aes(x='variable', y='value', fill='category'))
+ geom_bar(stat='identity', position='dodge', show_legend=False)
+ geom_text(aes(y=-.5, label='category'),
position=dodge_text,
color=ccolor, size=8, angle=45, va='top')              # modified
+ geom_text(aes(label='value'),
position=dodge_text,
size=8, va='bottom', format_string='{}%')
+ lims(y=(-5, 60))
+ theme(panel_background=element_rect(fill='white'),               # new
axis_title_y=element_blank(),
axis_line_x=element_line(color='black'),
axis_line_y=element_blank(),
axis_text_y=element_blank(),
axis_text_x=element_text(color=ccolor),
axis_ticks_major_y=element_blank(),
panel_grid=element_blank(),
panel_border=element_blank())
)

Out[7]:

<ggplot: (97654321012345679)>


Credit: I saved a plot this example is based on a while ago and forgot/misplaced the link to the source. The user considered it a minor coup.