Plotting time series box and whisker plot with missing date values for origin destination pairs - pandas

Question

I have the following data set:
df.head(7)
Origin Dest Date Quantity
0 Atlanta LA 2021-09-09 1
1 Atlanta LA 2021-09-11 4
2 Atlanta Chicago 2021-09-16 1
3 Atlanta Seattle 2021-09-27 12
4 Seattle LA 2021-09-29 2
5 Seattle Atlanta 2021-09-13 2
6 Seattle Newark 2021-09-17 7
In short, this table represents the number of items (Quantity) that were sent from a given origin to a given destination on a given date. The table contains 1 month of data. This table was read with:
shipments = pd.read_csv('shipments.csv', parse_dates=['Date'])
Note that this is a sparse table: if Quantity=0 for a particular (Origin,Dest,Date) pair then this row is not included in the table. As per example, on 2021-09-10 no items were sent from Atlanta to LA this row is not included in the data.
I would like to visualize this data using time series box and whisker plots. The x-axis of my graph should show the day, and Quantity should be on the y-axis. A boxplot should represent the various percentiles aggregated over all (origin-destination) pairs.
Similarly, would it be possible to create a graph which, instead of every day, only shows Monday-Sunday on the x-axis (and hence shows the results per day of the week)?
To generate the rows with missing data I used the following code:
table = pd.pivot_table(data=shipments, index='Date', columns=['Origin','Dest'], values='Quantity', fill_value=0)
idx = pd.date_range('2021-09-06','2021-10-10')
table = table.reindex(idx,fill_value=0)

Answer 1

You could transpose the table dataframe, and use that as input for a sns.boxplot. And you could create a similar table for the day of the week. Note that with many zeros, the boxplot might look a bit strange.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# first create some test data, somewhat similar to the given data
N = 1000
cities = ['Atlanta', 'LA', 'Chicago', 'Seattle', 'Newark']
shipments = pd.DataFrame({'Origin': np.random.choice(cities, N),
'Dest': np.random.choice(cities, N),
'Date': np.random.choice(pd.date_range('2021-09-06', '2021-10-10'), N),
'Quantity': (np.random.uniform(1, 4, N) ** 3).astype(int)})
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 5), gridspec_kw={'width_ratios': [3, 1]})
# create boxplots for each day
table_month = pd.pivot_table(data=shipments, index='Date', columns=['Origin', 'Dest'], values='Quantity', fill_value=0)
idx = pd.date_range('2021-09-06', '2021-10-10')
table_month = table_month.reindex(idx, fill_value=0)
sns.boxplot(data=table_month.T, ax=ax1)
labels = [day.strftime('%d\n%b %Y') if i == 0 or day.day == 1 else day.strftime('%d')
for i, day in enumerate(table_month.index)]
ax1.set_xticklabels(labels)
# create boxplots for each day of the week
table_dow = pd.pivot_table(data=shipments, index=shipments['Date'].dt.dayofweek, columns=['Origin', 'Dest'],
values='Quantity', fill_value=0)
table_dow = table_dow.reindex(range(7), fill_value=0)
sns.boxplot(data=table_dow.T, ax=ax2)
labels = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']
ax2.set_xticklabels(labels)
ax2.set_xlabel('') # remove superfluous x label
fig.tight_layout()
plt.show()