Data Visualisation

Estimated time: 30 minutes

Overview

Questions

• How can I use Python tools like Pandas and Plotly to visualize library circulation data?

Objectives

• Generate plots using Python to interpret and present data on library circulation.
• Apply data manipulation techniques with pandas to prepare and transform library circulation data into a suitable format for visualization.
• Analyze and interpret time-series data by identifying key trends and outliers in library circulation data.

For this module, we will use the tidy (long) version of our circulation data, where each variable forms a column, each observation forms a row, and each type of observation unit forms a row. If your workshop included the Tidy Data episode, you should be set and have an object called df_long in your Jupyter environment. If not, we’ll read that dataset in now, as it was provided for this lesson.

PYTHON

#import if it is already not
import pandas as pd
df_long = pd.read_pickle('data/df_long.pkl')

Let’s look at the data:

PYTHON

df_long.head()

	branch	address	city	zip code	ytd	year	month	circulation
date
2011-01-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	january	8427
2011-01-01	Altgeld	13281 S. Corliss Ave.	Chicago	60827.0	9611	2011	january	1258
2011-01-01	Archer Heights	5055 S. Archer Ave.	Chicago	60632.0	101951	2011	january	8104
2011-01-01	Austin	5615 W. Race Ave.	Chicago	60644.0	25527	2011	january	1755
2011-01-01	Austin-Irving	6100 W. Irving Park Rd.	Chicago	60634.0	165634	2011	january	12593

Plotting with Pandas

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Ok! We are now ready to plot our data. Since this data is monthly data, we can plot the circulation data over time.

At first, let’s focus on a specific branch. We can select the rows for the Albany Park branch and then use .sort_index() to be explicit that we want our data to be sorted in the order of the date index.

PYTHON

albany = df_long[df_long['branch'] == 'Albany Park'].sort_index()

PYTHON

albany.head()

	branch	address	city	zip code	ytd	year	month	circulation
date
2011-01-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	january	8427
2011-02-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	february	7023
2011-03-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	march	9702
2011-04-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	april	9344
2011-05-01	Albany Park	5150 N. Kimball Ave.	Chicago	60625.0	120059	2011	may	8865

Now we can use the plot() function that is built in to pandas. Let’s try it:

PYTHON

albany.plot()

That’s interesting, but by default .plot() will use a line plot for all numeric variables of the DataFrame. This isn’t exactly what we want, so let’s tell .plot() what variable to use by selecting the circulation column.

PYTHON

albany['circulation'].plot()

Analyze the Circulation Trends

Examine the line graph depicting library circulation data. You will notice two significant periods where the circulation drops to zero: first in March 2020 and then a two-year zero circulation period starting in 2012. Evaluate the graph and identify any trends, unusual patterns, or notable changes in the data.

Show me the solution

The significant drop in circulation in March 2020 is likely due to the COVID-19 pandemic, which caused widespread temporary closures of public spaces, including libraries.

The drop from 2012 through part of 2014 corresponds to the reconstruction period of the Albany Park Branch. The original building at 5150 N. Kimball Avenue was demolished in 2012, and a new, modern building was constructed at the same site. The new Albany Park Branch opened on September 13, 2014, at 3104 W. Foster Avenue in the North Park neighborhood of Chicago. More details about this renovation can be found on the Chicago Public Library webpage: Chicago Public Library - Albany Park.

Use Pandas for More Detailed Charts

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What if we want to alter the axis labels and the title of the graph? Pandas’ built-in plotting functions, which are backended by Matplotlib, allow us to customize various aspects of a plot without needing to import Matplotlib directly.

• We can pass parameters to Pandas’ .plot() function to add a plot title, specify a figure size, and change the color of the line.
• Additionally, we can directly set the x and y axis labels within the .plot() function.

PYTHON

albany['circulation'].plot(title='Circulation Count Over Time', 
                                figsize=(10, 5), 
                                color='blue', 
                                xlabel='Date',
                                ylabel='Circulation Count')

Changing plot types

What if we want to use a different plot type for this graphic? To do so, we can change the kind parameters in our .plot() function.

PYTHON

albany['circulation'].plot(kind='area', 
                            title='Circulation Count Area Plot at Albany Park', alpha=0.5, 
                            xlabel='Date',
                            ylabel='Circulation Count')

We can also look at our circulation data as a histogram.

PYTHON

albany['circulation'].plot(kind='hist', bins=20, 
                            title='Distribution of Circulation Counts at Albany Park',
                            xlabel='Circulation Count')

Use Plotly for interactive plots

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Let’s switch back to the full DataFrame in df_long and use another plotting package in Python called Plotly. First let’s install and then use the package.

PYTHON

# uncomment below to install plotly if the import fails. 
# !pip install plotly

# if you didn't install Jupyter via Anaconda, you may also need to install ipywidgets.
# !pip install ipywidgets

import plotly.express as px

Now we can visualize how circulation counts have changed over time for selected branches. This can be especially useful for identifying trends, seasonality, or data anomalies. We willfirst create a subset of our data to look at branches starting with the letter ‘A’. Feel free to select different branches. After subsetting, we will sort our new DataFrame by date and then plot our data by date and circulation count.

PYTHON

# Creating a line plot for a few selected branches to avoid clutter
selected_branches = df_long[df_long['branch'].isin(['Altgeld',
 'Archer Heights',
 'Austin',
 'Austin-Irving',
 'Avalon'])]
selected_branches = selected_branches.sort_values(by='date')

PYTHON

fig = px.line(selected_branches, x=selected_branches.index, y='circulation', color='branch', title='Circulation Over Time for Selected Branches')
fig.show()

Here is a view of the interactive output of the Plotly line chart.

One advantage that Plotly provides over Matplotlib is that it has some interactive features out of the box. Hover your cursor over the lines in the output to find out more granular data about specific branches over time.

Bar plots with Plotly

Let’s use a barplot to compare the distribution of circulation counts among branches. We first need to group our data by branch and sum up the circulation counts. Then we can use the bar plot to show the distribution of total circulation over branches.

PYTHON

# Aggregate circulation by branch
total_circulation_by_branch = df_long.groupby('branch')['circulation'].sum().reset_index()

# Create a bar plot
fig = px.bar(total_circulation_by_branch, x='branch', y='circulation', title='Total Circulation by Branch')
fig.show()

Here is a view of the interactive output of the Plotly bar chart.

Plotting with Pandas

1. Load the dataset df_long.pkl using Pandas.
2. Create a new DataFrame that only includes the data for the “Chinatown” branch. (Don’t forget to sort by the index)
3. Use the Pandas plotting function to plot the “circulation” column over time.

Show me the solution

PYTHON

import pandas as pd
df_long = pd.read_pickle('data/df_long.pkl')
chinatown = df_long[df_long['branch'] == 'Chinatown'].sort_index()
chinatown['circulation'].plot()

Chinatown plot

Modify a plot display

Add a line to the code below to plot the Uptown branch circulation including the following plot elements:

• A title, “Uptown Circulation”
• “Year” and “Circulation Count” labels for the x and y axes
• A green plot line

PYTHON

import pandas as pd
df_long = pd.read_pickle('data/df_long.pkl')
uptown = df_long[df_long['branch'] == 'Uptown'].sort_index()

Show me the solution

PYTHON

uptown['circulation'].plot(title='Uptown Circulation', 
                                color='green', 
                                xlabel='Year',
                                ylabel='Circulation Count')

Uptown plot

Plot the top five branches

Modify the code below to only plot the five Chicago Public Library branches with the highest circulation.

PYTHON

import plotly.express as px
import pandas as pd
df_long = pd.read_pickle('data/df_long.pkl')
total_circulation_by_branch = df_long.groupby('branch')['circulation'].sum().reset_index()

top_five = total_circulation_by_branch.___________________

# Create a bar plot
fig = px.bar(top_five._______, x='branch', y='circulation', width=600, height=600, title='Total Circulation by Branch')
fig.show()

Show me the solution

PYTHON

total_circulation_by_branch.sort_values(by='circulation', ascending=False)
df_long = pd.read_pickle('data/df_long.pkl')
total_circulation_by_branch = df_long.groupby('branch')['circulation'].sum().reset_index()

top_five = total_circulation_by_branch.sort_values(by='circulation', ascending=False)

# Create a bar plot
fig = px.bar(top_five.head(), x='branch', y='circulation', width=600, height=600, title='Total Circulation by Branch')
fig.show()

Top five circulation branches

Key Points

• Explored the use of pandas for basic data manipulation, ensuring correct indexing with DatetimeIndex to enable time-series operations like resampling.
• Used pandas’ built-in plot() for initial visualizations and faced issues with overplotting, leading to adjustments like data filtering and resampling to simplify plots.
• Introduced Plotly for advanced interactive visualizations, enhancing user engagement through dynamic plots such as line graphs, area charts, and bar plots with capabilities like dropdown selections.