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 tremendous power without using 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-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

• Ability to graph relative frequency and normalize over any number of variables
• No need for multiple functions to do the same thing (far fewer public functions)
• Ability to make grids with a single function instead of having to use a higher level function like catplot
• Pandas groupby methods available as strings
• Ability to sort by values
• Ability to sort x/y labels lexicographically
• Ability to select most/least frequent groups
• x/y labels are wrapped so that they don't overlap
• Figure size (plus several other options) and available to change without using matplotlib
• A matplotlib figure object is 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
import pandas as pd
airbnb = dxp.load_dataset('airbnb')
airbnb.head()

<div> <table border="1" class="dataframe"> <thead style="border-bottom:1px solid black; vertical-align:bottom;"> <tr style="text-align: right;"> <th></th> <th style="color:red;">neighborhood</th> <th>property_type</th> <th>accommodates</th> <th>bathrooms</th> <th>bedrooms</th> <th>price</th> <th>cleaning_fee</th> <th>rating</th> <th>superhost</th> <th>response_time</th> <th>latitude</th> <th>longitude</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>Shaw</td> <td>Townhouse</td> <td>16</td> <td>3.5</td> <td>4</td> <td>433</td> <td>250</td> <td>95.0</td> <td>No</td> <td>within an hour</td> <td>38.90982</td> <td>-77.02016</td> </tr> <tr> <th>1</th> <td>Brightwood Park</td> <td>Townhouse</td> <td>4</td> <td>3.5</td> <td>4</td> <td>154</td> <td>50</td> <td>97.0</td> <td>No</td> <td>NaN</td> <td>38.95888</td> <td>-77.02554</td> </tr> <tr> <th>2</th> <td>Capitol Hill</td> <td>House</td> <td>2</td> <td>1.5</td> <td>1</td> <td>83</td> <td>35</td> <td>97.0</td> <td>Yes</td> <td>within an hour</td> <td>38.88791</td> <td>-76.99668</td> </tr> <tr> <th>3</th> <td>Shaw</td> <td>House</td> <td>2</td> <td>2.5</td> <td>1</td> <td>475</td> <td>0</td> <td>98.0</td> <td>No</td> <td>NaN</td> <td>38.91331</td> <td>-77.02436</td> </tr> <tr> <th>4</th> <td>Kalorama Heights</td> <td>Apartment</td> <td>3</td> <td>1.0</td> <td>1</td> <td>118</td> <td>15</td> <td>91.0</td> <td>No</td> <td>within an hour</td> <td>38.91933</td> <td>-77.04124</td> </tr> </tbody> </table> </div>

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')

Line and scatter plots can be created with the same command, just substituting the name of the function. They both are not good choices for the visualization since the grouping variable (neighborhood) has no meaningful order.

dxp.line(x='neighborhood', y='price', data=airbnb, aggfunc='median')

dxp.scatter(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'})

<div> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>price</th> </tr> <tr> <th>neighborhood</th> <th></th> </tr> </thead> <tbody> <tr> <th>Brightwood Park</th> <td>87.0</td> </tr> <tr> <th>Capitol Hill</th> <td>129.5</td> </tr> <tr> <th>Columbia Heights</th> <td>95.0</td> </tr> <tr> <th>Dupont Circle</th> <td>125.0</td> </tr> <tr> <th>Edgewood</th> <td>100.0</td> </tr> <tr> <th>Kalorama Heights</th> <td>118.0</td> </tr> <tr> <th>Shaw</th> <td>133.5</td> </tr> <tr> <th>Union Station</th> <td>120.0</td> </tr> </tbody> </table> </div>

Number and percent of missing values with 'countna' and 'percna'

In addition to all the common aggregating functions, you can use the strings 'countna' and 'percna' to get the number and percentage of missing values per group.

dxp.bar(x='neighborhood', y='response_time', data=airbnb, aggfunc='countna')

Sorting the bars by values

By default, the bars will be sorted by the grouping column (x-axis here) in alphabetical order. Use the sort_values parameter to sort the bars by value.

• None - sort x/y axis labels alphabetically (default)
• asc - sort values from least to greatest
• desc - sort values from greatest to least

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

Here, we sort the values from greatest to least.

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

Specify order with x_order

Specify a specific order of the labels 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