Resampling Timeseries Data into X Hours and Getting Output in One-Hot Encoded Format

In this article, we will discuss the process of resampling timeseries data into x hours and converting it into one-hot encoded format. We’ll cover how to achieve this using pandas, a popular Python library for data manipulation and analysis.

Introduction

Resampling timeseries data involves changing the frequency or resolution of the data. In this case, we want to resample the data into x hours and get output in one-hot encoded format. One-hot encoding is a technique used to convert categorical variables into numerical variables that can be processed by machine learning algorithms.

Removing Times from Datetimes

The first step in achieving our goal is to remove times from datetime objects in the dataset. We can do this using the Series.dt.floor method, which rounds down each datetime object to the nearest day (or any other frequency specified).

# Convert 'TimeStamp' column to datetime type if necessary
df['TimeStamp'] = pd.to_datetime(df['TimeStamp'])

# Remove times from datetime objects and set as index
df1 = df.set_index(df['TimeStamp'].dt.floor('d'))['Event']

One-Hot Encoding

After removing times from the datetime objects, we can use get_dummies to perform one-hot encoding on the ‘Event’ column.

# Perform one-hot encoding on 'Event' column
df2 = pd.get_dummies(df1, columns=['Event'])

However, in some cases, we may want only the binary values (0 or 1) from the encoded DataFrame. We can achieve this using the max method.

# Get only the binary values (0 or 1)
df3 = df2.max(level=0)

print(df3)

This will output a new DataFrame with only the one-hot encoded binary values for each datetime index.

Counting Binary Values

Alternatively, we can also count the number of times each event appears in each time interval. We can achieve this using the sum method.

# Count the number of times each event appears in each time interval
df4 = df2.sum(level=0)

print(df4)

This will output a new DataFrame with the count of binary values for each event in each time interval.

Combining Binary and Count Outputs

If we want to combine both the one-hot encoded binary values and the count outputs, we can use a concatenation approach.

# Combine binary and count outputs
df5 = pd.concat([df3, df4], axis=1)

print(df5)

This will output a new DataFrame with both the one-hot encoded binary values and the counts for each event in each time interval.

Conclusion

In this article, we have discussed how to resample timeseries data into x hours and convert it into one-hot encoded format using pandas. We covered how to remove times from datetime objects, perform one-hot encoding, count binary values, and combine both outputs.

Last modified on 2023-12-30