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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.

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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).

png

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-axis
  • y - Column name the y-axis
  • data - Pandas DataFrame
  • aggfunc - String of pandas aggregation function, 'min', 'max', 'mean', etc...
  • split - Column name to split data into distinct groups
  • row - Column name to split data into distinct subplots row-wise
  • col - Column name to split data into distinct subplots column-wise
  • orientation - 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')

png

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 A
  • asc - sort values from least to greatest
  • desc - 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

png

dxp.bar(x='neighborhood', y='price', data=airbnb, aggfunc='median', sort='asc')

png

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'])

png

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')

png

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')

png

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'])

png

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)

png

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')

png

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)

png

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'])

png

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])

png

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)

png

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)

png

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)

png

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