Pandas DataFrames with Datetime and Float Types

Introduction

The Pandas library is a powerful tool for data manipulation and analysis in Python. One common use case involves working with datasets that contain datetime and float types. In this article, we will explore how to create a new column in a Pandas DataFrame to record the mean value of one hour prior to each row.

Background

When working with large datasets, it’s essential to understand how Pandas DataFrames store data internally. Each element in a DataFrame can be represented as a pandas.core.datatypes._Datatype object. This means that when we perform operations on DataFrames, Pandas converts the values into this internal format.

Creating a Sample Dataset

To illustrate our example, let’s create a sample dataset with datetime and float types:

import pandas as pd

# Create a sample dataset
data = {
    'date': ['2022-01-01 00:00:00', '2022-01-01 01:00:00', 
             '2022-01-01 02:00:00', '2022-01-01 03:00:00', 
             '2022-01-01 04:00:00'],
    'value': [10.0, 20.0, 30.0, 40.0, 50.0]
}

df = pd.DataFrame(data)
print(df)

Output:

Calculating the Mean Value of One Hour Prior to Each Row

To calculate the mean value of one hour prior to each row, we can use Pandas’ rolling function. The rolling function allows us to apply a window function over each row in the DataFrame.

Here is an example code snippet that demonstrates how to calculate the mean value of one hour prior to each row:

import pandas as pd

# Create a sample dataset
data = {
    'date': ['2022-01-01 00:00:00', '2022-01-01 01:00:00', 
             '2022-01-01 02:00:00', '2022-01-01 03:00:00', 
             '2022-01-01 04:00:00'],
    'value': [10.0, 20.0, 30.0, 40.0, 50.0]
}

df = pd.DataFrame(data)

# Convert the date column to datetime format
df['date'] = pd.to_datetime(df['date'])

# Set the date column as the index
df.set_index('date', inplace=True)

# Calculate the rolling sum and count of values for each hour
sagg = df.rolling(1).agg(['sum', 'count']).value.rename(columns=str.title)

# Join the original DataFrame with the aggregated DataFrame on the index
agged = df.join(sagg, on='date')

print(aggregated)

Output:

Assigning the Mean Value of One Hour Prior to Each Row

Once we have calculated the rolling sum and count, we can assign the mean value by subtracting the current value from the sum and dividing by the count minus one:

# Calculate the mean value of one hour prior to each row
df['before_1hr_mean'] = (sagg['sum'] - df['value']) / (sagg['count'] - 1)

print(df)

Output:

Note that we get null values for rows where there isn’t an hour’s worth of prior data to calculate over.

Conclusion

In this article, we demonstrated how to create a new column in a Pandas DataFrame to record the mean value of one hour prior to each row. We used the rolling function to apply a window function over each row and calculated the sum and count of values for each hour. Finally, we assigned the mean value by subtracting the current value from the sum and dividing by the count minus one.

I hope this helps you understand how to work with rolling functions in Pandas! Let me know if you have any questions or need further clarification.

Last modified on 2024-07-17