Visualizing Frequency Measurements by Day and Hour with Python and Matplotlib.

Creating a Frequency DataFrame with Hourly Measurements

Creating a bar chart to visualize the frequency of measurements per day is a common use case. However, when we add an additional variable such as the hour of measurement, it becomes more complex and requires a different approach.

In this article, we will explore how to create a stacked bar chart that shows the frequency of measurements per day and hour. We’ll dive into the details of creating this chart using Python’s Pandas library and Matplotlib for visualization.

Introduction

When dealing with time-series data, it is common to have multiple variables that need to be visualized together. In this case, we have two variables: day and hour, each with its own frequency measurements. The goal is to create a stacked bar chart that shows the frequency of measurements per day and hour.

Understanding DataFrames

A DataFrame in Pandas is a 2-dimensional labeled data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database. When working with time-series data, it’s essential to have a well-structured DataFrame that allows for efficient manipulation and analysis.

In our case, we’ll assume that our DataFrame is already populated with the required data:

import pandas as pd

# Sample data
data = {
    'date': ['2022-01-01 08:00', '2022-01-01 09:00', '2022-01-01 10:00'],
    'day': [1, 1, 1],
    'hour': [8, 9, 10]
}

df = pd.DataFrame(data)

This will create a DataFrame with the required columns, date, day, and hour.

Grouping Data by Day and Hour

To create a stacked bar chart that shows the frequency of measurements per day and hour, we need to group our data by these two variables.

We’ll use the groupby function from Pandas to achieve this:

# Group by day and hour
df_grouped = df.groupby(['day', 'hour']).size().reset_index(name='frequency')

This will create a new DataFrame with the frequency measurements grouped by day and hour. The size function returns the number of elements in each group, which is our desired frequency measurement.

Creating a Stacked Bar Chart

Now that we have our data grouped by day and hour, we can create a stacked bar chart to visualize the frequency measurements.

We’ll use Matplotlib’s bar function with the stacked=True parameter to achieve this:

import matplotlib.pyplot as plt

# Create a figure and axis object
fig, ax = plt.subplots()

# Plot the data
ax.bar(df_grouped['day'], df_grouped['frequency'], label='Day')
ax.bar(df_grouped['day'], df_grouped['frequency'], bottom=df_grouped['frequency'], label='Hour')

# Set labels and title
ax.set_xlabel('Day')
ax.set_ylabel('Frequency')
ax.set_title('Frequency Measurements by Day and Hour')

# Show the legend
plt.legend()

# Display the plot
plt.show()

This will create a stacked bar chart with two separate bars for each day, one for the frequency measurements themselves and another for the hourly measurements. The bottom parameter is used to stack the hourly measurements on top of the daily measurements.

Handling Missing Values

When working with time-series data, it’s essential to handle missing values correctly. In our case, we might have some hours without any measurements. To handle this, we can use Pandas’ groupby function with the droplevel parameter:

# Group by day and hour, dropping rows with missing values
df_grouped = df.groupby(['day', 'hour']).size().reset_index(name='frequency').dropna()

This will remove any rows with missing values from our DataFrame.

Using a Pivot Table

Another approach to visualize the frequency measurements is to use a pivot table. A pivot table allows us to summarize data by creating a new table that shows the sum of values for each group.

We can create a pivot table using Pandas’ pivot_table function:

# Create a pivot table
pivot_df = pd.pivot_table(df, index='day', columns='hour', aggfunc='size')

This will create a pivot table with the frequency measurements aggregated by hour for each day.

Visualizing the Pivot Table

To visualize the pivot table, we can use Matplotlib’s bar function:

# Create a figure and axis object
fig, ax = plt.subplots()

# Plot the data
pivot_df.plot(kind='bar', ax=ax)

# Set labels and title
ax.set_xlabel('Day')
ax.set_ylabel('Frequency')
ax.set_title('Frequency Measurements by Hour')

# Display the plot
plt.show()

This will create a bar chart with one bar for each hour, showing the frequency measurements for that hour on each day.

Conclusion

Creating a stacked bar chart to visualize the frequency of measurements per day and hour requires careful consideration of our data structure and grouping strategy. By using Pandas’ groupby function and Matplotlib’s bar function, we can create an effective visual representation of our data. Additionally, handling missing values correctly is essential for accurate results.

In this article, we explored how to create a frequency DataFrame with hourly measurements using Python’s Pandas library and Matplotlib for visualization. We covered topics such as grouping data by day and hour, creating a stacked bar chart, handling missing values, and using pivot tables. With these techniques, you can effectively visualize your time-series data and make informed decisions about your analysis.

Additional Resources

Last modified on 2025-02-26