Data Visualization library using matplotlib for both long and wide data
Project description
Dexplot
Dexplot is a Python library for delivering beautiful data visualizations with a simple and intuitive user experience.
Goals
The primary goals for dexplot are:
- Maintain a very consistent API with as few functions as necessary to make the desired statistical plots
- Allow the user to tweak the plots without digging into matplotlib
Installation
pip install dexplot
Built for long and wide data
Dexplot is primarily built for long data, which is a form of data where each row represents a single observation and each column represents a distinct quantity. It is often referred to as "tidy" data. Here, we have some long data.
Dexplot also has the ability to handle wide data, where multiple columns may contain values that represent the same kind of quantity. The same data above has been aggregated to show the mean for each combination of neighborhood and property type. It is now wide data as each column contains the same quantity (price).
Usage
Dexplot provides a small number of powerful functions that all work similarly. Most plotting functions have the following signature:
dxp.plotting_func(x, y, data, aggfunc, split, row, col, orientation, ...)
x
- Column name along the x-axisy
- Column name the y-axisdata
- Pandas DataFrameaggfunc
- String of pandas aggregation function, 'min', 'max', 'mean', etc...split
- Column name to split data into distinct groupsrow
- Column name to split data into distinct subplots row-wisecol
- Column name to split data into distinct subplots column-wiseorientation
- Either vertical ('v'
) or horizontal ('h'
). Default for most plots is vertical.
When aggfunc
is provided, x
will be the grouping variable and y
will be aggregated when vertical and vice-versa when horizontal. The best way to learn how to use dexplot is with the examples below.
Families of plots
There are two primary families of plots, aggregation and distribution. Aggregation plots take a sequence of values and return a single value using the function provided to aggfunc
to do so. Distribution plots take a sequence of values and depict the shape of the distribution in some manner.
- Aggregation
- bar
- line
- scatter
- count
- Distribution
- box
- violin
- hist
- kde
Comparison with Seaborn
If you have used the seaborn library, then you should notice a lot of similarities. Much of dexplot was inspired by Seaborn. Below is a list of the extra features in dexplot not found in seaborn
- The ability to graph relative frequency percentage and normalize over any number of variables
- Far fewer public functions
- No need for multiple functions to do the same thing
- Ability to make grids with a single function instead of having to use a higher level function like
catplot
- Pandas
groupby
methods are available as strings - Ability to sort by values
- Ability to sort x/y labels lexicographically
- Both x/y-labels and titles are automatically wrapped so that they don't overlap
- The figure size (plus several other options) and available to change without using matplotlib
- Only matplotlib objects are returned
Examples
Most of the examples below use long data.
Aggregating plots - bar, line and scatter
We'll begin by covering the plots that aggregate. An aggregation is defined as a function that summarizes a sequence of numbers with a single value.
The examples come from the Airbnb dataset, which contains many property rental listings from the Washington D.C. area.
import dexplot as dxp
airbnb = dxp.load_dataset('airbnb')
airbnb.head()
neighborhood | property_type | accommodates | bathrooms | bedrooms | price | cleaning_fee | rating | superhost | response_time | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Shaw | Townhouse | 16 | 3.5 | 4 | 433 | 250 | 95.0 | No | within an hour | 38.90982 | -77.02016 |
1 | Brightwood Park | Townhouse | 4 | 3.5 | 4 | 154 | 50 | 97.0 | No | NaN | 38.95888 | -77.02554 |
2 | Capitol Hill | House | 2 | 1.5 | 1 | 83 | 35 | 97.0 | Yes | within an hour | 38.88791 | -76.99668 |
3 | Shaw | House | 2 | 2.5 | 1 | 475 | 0 | 98.0 | No | NaN | 38.91331 | -77.02436 |
4 | Kalorama Heights | Apartment | 3 | 1.0 | 1 | 118 | 15 | 91.0 | No | within an hour | 38.91933 | -77.04124 |
There are more than 4,000 listings in our dataset. We will use bar charts to aggregate the data.
airbnb.shape
(4581, 12)
Vertical bar charts
In order to performa an aggregation, you must supply a value for aggfunc
. Here, we find the median price per neighborhood. Notice that the column names automatically wrap.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median')
Components of the groupby aggregation
Anytime the aggfunc
parameter is set, you have performed a groupby aggregation, which always consists of three components:
- Grouping column - unique values of this column form independent groups (neighborhood)
- Aggregating column - the column that will get summarized with a single value (price)
- Aggregating function - a function that returns a single value (median)
The general format for doing this in pandas is:
df.groupby('grouping column').agg({'aggregating column': 'aggregating function'})
Specifically, the following code is executed within dexplot.
airbnb.groupby('neighborhood').agg({'price': 'median'})
price | |
---|---|
neighborhood | |
Brightwood Park | 87.0 |
Capitol Hill | 129.5 |
Columbia Heights | 95.0 |
Dupont Circle | 125.0 |
Edgewood | 100.0 |
Kalorama Heights | 118.0 |
Shaw | 133.5 |
Union Station | 120.0 |
Sorting the bars
By default, the grouping column (x-axis here) will be sorted in alphabetical order. Use the sort
parameter to specify how its sorted.
lex_asc
- sort lexicographically A to Z (default)lex_desc
- sort lexicographically Z to Aasc
- sort values from least to greatestdesc
- sort values from greatest to least- None - Use order of appearance in DataFrame
fig = dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median', sort='lex_desc')
fig
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median', sort='asc')
Specify order with x_order
Specify a specific order of the values on the x-axis by passing a list of values to x_order
. This can also act as a filter to limit the number of bars.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
x_order=['Dupont Circle', 'Edgewood', 'Union Station'])
Horizontal bars
Set orientation
to 'h'
for horizontal bars. When you do this, you'll need to switch x
and y
since the grouping column (neighborhood) will be along the y-axis and the aggregating column (price) will be along the x-axis.
dxp.bar(x='price', y='neighborhood', data=airbnb, aggfunc='median', orientation='h')
Split bars into groups
You can split each bar into further groups by setting the split
parameter to another column.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median', split='superhost')
We can use the pivot_table
method to replicate the results in pandas.
airbnb.pivot_table(index='neighborhood', columns='superhost',
values='price', aggfunc='median')
superhost | No | Yes |
---|---|---|
neighborhood | ||
Brightwood Park | 85.0 | 90.0 |
Capitol Hill | 129.0 | 130.0 |
Columbia Heights | 90.5 | 103.0 |
Dupont Circle | 120.0 | 135.0 |
Edgewood | 100.0 | 100.0 |
Kalorama Heights | 110.0 | 124.0 |
Shaw | 130.0 | 135.0 |
Union Station | 120.0 | 125.0 |
Set the order of the unique split values with split_order
, which can also act as a filter.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', split_order=['Yes', 'No'])
Stacked bar charts
Stack all the split groups one on top of the other by setting stacked
to True
.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', split_order=['Yes', 'No'], stacked=True)
Split into multiple plots
It's possible to split the data further into separate plots by the unique values in a different column with the row
or col
parameter. Here, each kind of property_type
has its own plot.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', col='property_type')
If there isn't room for all of the plots, set the wrap
parameter to an integer to set the maximum number of plots per row/col.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', col='property_type', wrap=2)
Use col_order
to both filter and set a specific order for the plots.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', col='property_type', col_order=['House', 'Condominium'])
Splits can be made simultaneously along row and columns.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', col='property_type', col_order=['House', 'Condominium', 'Apartment'],
row='bedrooms', row_order=[0, 1, 2, 3])
By default, all axis limits are shared. Allow each plot to set its own limits with the sharex
and sharey
parameters.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median',
split='superhost', col='property_type', col_order=['House', 'Condominium', 'Apartment'],
row='bedrooms', row_order=[0, 1, 2, 3], sharey=False)
Set the width of each bar with size
The width of the bars is set with the size
parameter.
dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median', split='property_type',
split_order=['Apartment', 'House'], x_order=['Dupont Circle', 'Capitol Hill', 'Union Station'], size=.5)
Distribution plots - box, violin, histogram, kde
Distribution plots work similarly, but do not have an aggfunc
since they do not aggregate.
dxp.box(x='price', y='neighborhood', data=airbnb)
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