Visualize Amounts

To get started in visualizing, we’ll look at one of the simplest ideas, single quantaties. Let’s grab some data too!

import pandas as pa

df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/iris.csv')

Bar Charts

I’ll give a small bar chart of the means of the different Classes of flowers.

df.groupby('Class').SepalLength.agg('mean')

Class
Iris-setosa        5.006
Iris-versicolor    5.936
Iris-virginica     6.588
Name: SepalLength, dtype: float64

df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar')

<AxesSubplot:xlabel='Class'>

df.groupby('Class').SepalLength.agg('mean').plot.bar()

<AxesSubplot:xlabel='Class'>

There are lots of options some of which we should be using reguarly. A title is always nice

df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar', title = 'Mean by Class')

<AxesSubplot:title={'center':'Mean by Class'}, xlabel='Class'>

A vertical description on what the \(y\) axis represents should not be forgotten!

df.groupby('Class').SepalLength.agg('mean').plot(kind = 'bar',
                                                 title = 'Mean by Class', 
                                                 ylabel= 'Mean of Sepal Length')

<AxesSubplot:title={'center':'Mean by Class'}, xlabel='Class', ylabel='Mean of Sepal Length'>

One of the complaints about a graphic like this is the length of the class titles. It takes up a lot of vertical space. With a barh you can change the orientation of the bars.

df.groupby('Class').SepalLength.agg('mean').plot(kind = 'barh',
                                                 title = 'Mean by Class', 
                                                 ylabel= 'Mean of Sepal Length')

<AxesSubplot:title={'center':'Mean by Class'}, ylabel='Class'>

I couldn’t get the label of the values to appear, maybe you can?

If there are lots of values, don’t use bars! Let’s see this with a different dataset.

df2 = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Activity_Dataset_V1.csv')

In the following graph it is very difficult to follow the data points across.

df2.groupby('workout_type').calories.agg('mean').sort_values(ascending = True).plot(kind = 'barh')

<AxesSubplot:ylabel='workout_type'>

To clear this up you could use a point instead of a bar!

Dot Plots Work Well Too

df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type')

<AxesSubplot:xlabel='mean', ylabel='workout_type'>

This creates other issues in that the origin of the figure is not zero. To fix that, we simply require that the x limits go from 0 to 310.

df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type', xlim = [0,310])

<AxesSubplot:xlabel='mean', ylabel='workout_type'>

All the workout types are giving us about the same bang for our buck, at least in mean calories.

Adding Labels

It might also be nice to see the numbers presented with the data. This is esspecially nice for a small number of quantities.

ax = df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index().plot.scatter(x = 'mean', y = 'workout_type')#this made the same graph as above.

for i,k in enumerate(df2.groupby('workout_type').calories.agg(['mean']).sort_values(by = 'mean',ascending = True).reset_index()['mean']): #here I loop through the values, k, and indicies ,i.
  ax.annotate(str(int(k)),[k+.2,i+.2])

Bar Charts with Multiple Data

df.groupby('Class').agg('mean').plot(kind = 'bar')

<AxesSubplot:xlabel='Class'>

df.groupby('Class').agg('mean').plot(kind = 'bar', stacked = True)

<AxesSubplot:xlabel='Class'>

If you want to add labels, it should be simple but the version on colab is out of date… I update here.

!pip install --upgrade matplotlib

Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.5.1)
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Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)

import matplotlib
matplotlib.__version__

'3.5.1'

Now with the correct version it is acually really easy.

ax = df.groupby('Class').agg('mean').plot(kind = 'bar', ylim =[0,8])

for container in ax.containers:
    ax.bar_label(container)

ax = df.groupby('Class').agg('mean').plot(kind = 'bar', stacked = True)

for container in ax.containers:
    ax.bar_label(container)

Be careful with the stacked as it is giving a cummulative total. This doesn’t really make any sense here…

Your Turn

Using the Air B&B NYC data complete the following tasks.

1. Create a bar graph of the maximum ‘price’ by ‘neighbourhood_group’. Include the ‘price’ in your graph

2. Create a multiple bar graph with ‘neighbourhood_group’ and ‘room_type’ by looking at the average ‘price’.