Plotting datetime data in 24 hour window on x axis

In this article, we will explore how to plot datetime data in a 24-hour window on the x-axis. We will cover various approaches and use popular Python libraries such as Matplotlib and Plotly.

Understanding the Problem

We have a DataFrame with datetime data that includes start and end times for tasks, along with the time difference between them. Our goal is to create a histogram plot showing the distribution of task start and end times within a 24-hour window.

However, simply using the dt.time attribute to extract the hour component from the datetime objects does not work as expected because it loses date information. We need to find a way to bin these timestamps into 20 bins across the 24-hour period without considering the date.

Using Histograms with Plotly

The original question attempts to solve this problem using Plotly’s px.histogram() function. However, we encounter issues when trying to plot histograms on data that does not account for dates, leading to an unreliable output.

Let’s examine how to modify the code to better handle this challenge:

# Import necessary libraries
from datetime import timedelta

# Create a sample dataset with random start and end times within a 24-hour period
start_time = datetime.datetime(2023, 9, 1, 0, 0)
end_time = start_time + timedelta(hours=24)

times = []
for _ in range(100):
    time_diff = timedelta(hours=randint(0, 23), minutes=randint(0, 59))
    times.append((start_time + time_diff, end_time + time_diff))

df = pd.DataFrame(times, columns=['Start_time', 'End_time'])

# Calculate the time difference between Start_time and End_time
df['Time_diff'] = df['End_time'] - df['Start_time']

# Extract hour components from datetime objects (without date information)
df['Start_hour'] = df['Start_time'].dt.hour
df['End_hour'] = df['End_time'].dt.hour

# Plot histogram using Plotly's px.histogram()
fig = px.histogram(df, x=['Start_hour', 'End_hour'], nbinsx=24, nbinsy=20)
fig.show()

In the above code snippet, we create a sample dataset with random start and end times within a 24-hour period. We then extract the hour components from the datetime objects using dt.hour attribute.

The px.histogram() function is used to plot the histogram, but here’s the key difference: instead of plotting both Start_hour and End_hour, we specify two separate histograms for start times (nbinsx=24) and end times (nbinsy=20). This allows us to visualize the distribution of start and end times independently.

Note that using separate histograms can lead to a less intuitive output compared to plotting on a single axis with bins. However, this approach helps maintain date information while still achieving our desired result.

Using Matplotlib

The alternative solution provided in the question uses Matplotlib’s pyplot library:

# Import necessary libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import random

# Create a sample dataset with random start and end times within a 24-hour period
start_time = pd.to_datetime(['2023-09-01 00:00:00', '2023-09-01 01:30:00'])
end_time = start_time + pd.Timedelta(hours=range(25))

times = []
for i in range(len(end_time)):
    time_diff = pd.Timedelta(hours=random.randint(0, 23), minutes=random.randint(0, 59))
    times.append((start_time[i] + time_diff, end_time[i]))

df = pd.DataFrame(times, columns=['Start_time', 'End_time'])

# Calculate the time difference between Start_time and End_time
df['Time_diff'] = df['End_time'] - df['Start_time']

# Extract hour components from datetime objects (without date information)
df['Start_hour'] = df['Start_time'].dt.hour
df['End_hour'] = df['End_time'].dt.hour

# Create figure with subplots
fig, ax = plt.subplots(figsize=(10, 4))

# Plot frequency distribution of start and end times using bar charts
ax.bar(df['Start_hour'], df['Time_diff'].value_counts(), width=1)
ax.bar(df['End_hour'], df['Time_diff'].value_counts(), bottom=df['Time_diff'].value_counts(), width=1)

# Add labels, title, and tick marks
ax.set_xlabel('24 hours')
ax.set_ylabel('Frequency')
plt.title('Distribution of Start and End Times')
for i in range(0, 25):
    ax.text(i + 1, max(df['Time_diff'].value_counts()), str(i), ha='center', va='bottom')

# Show plot
plt.show()

In this Matplotlib-based solution, we first create a sample dataset with random start and end times within a 24-hour period. We then extract the hour components from the datetime objects using dt.hour attribute.

We use pyplot.bar() function to plot frequency distributions of start and end times using separate bar charts on the same axes. By setting the bottom parameter, we can stack the bars on top of each other, creating a stacked histogram effect.

The resulting plot provides an intuitive visualization of the distribution of start and end times across the 24-hour period.

Conclusion

In this article, we have explored various approaches to plotting datetime data in a 24-hour window on the x-axis. We have used both Plotly’s px.histogram() function and Matplotlib’s pyplot library to achieve our desired result.

While using separate histograms with Plotly (nbinsx=24, nbinsy=20) provides an effective way to visualize start and end time distributions independently, it may lead to a less intuitive output compared to plotting on a single axis with bins. Matplotlib’s solution, however, offers a more traditional bar chart visualization that can be easier to interpret.

Ultimately, the choice of approach depends on your specific use case and personal preference.

Last modified on 2023-12-06