To compute a rolling cumulative distinct count over the past 24 hours using Pandas, you need to use the rolling
method in combination with a custom aggregation function. Here’s a step-by-step guide on how to achieve this:
- Import Pandas and Create Sample Data:
Ensure you have Pandas installed and then create a DataFrame with a timestamp column and the values you want to count distinctly. - Set the Timestamp Column as the Index:
Convert the timestamp column to a Pandasdatetime
type and set it as the index. - Define a Custom Function for Rolling Distinct Count:
Create a custom aggregation function that counts the number of distinct elements in a given window. - Apply the Rolling Window:
Use therolling
method with a 24-hour window and apply your custom function.
Here is a complete example:
import pandas as pd
import numpy as np
# Sample data
data = {
'timestamp': pd.date_range(start='2023-01-01', periods=100, freq='H'),
'value': np.random.randint(1, 20, 100) # Random values between 1 and 20
}
df = pd.DataFrame(data)
# Convert timestamp to datetime and set it as the index
df['timestamp'] = pd.to_datetime(df['timestamp'])
df.set_index('timestamp', inplace=True)
# Define a custom aggregation function for counting distinct values
def rolling_cumulative_distinct_count(window):
return window.apply(lambda x: x.nunique())
# Compute the rolling cumulative distinct count over the past 24 hours
# Note: '24H' denotes a 24-hour rolling window
result = df['value'].rolling('24H').apply(lambda x: x.nunique(), raw=False)
print(result)
Explanation:
- Sample Data: We create a DataFrame with a timestamp and some random values.
- Datetime Conversion: We ensure the timestamp column is in datetime format and set it as the index to enable time-based rolling operations.
- Custom Aggregation Function: We define a lambda function within the
apply
method to count distinct values within each window. - Rolling Window: We use the
rolling
method with a ’24H’ window to indicate a 24-hour period. Theapply
method with the custom function is then used to calculate the number of distinct values within each window.
This approach allows you to compute a rolling cumulative distinct count for each hour, considering the past 24 hours of data. Adjust the freq
parameter in pd.date_range
and the rolling window parameter ’24H’ as needed for your specific use case.
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