Adding a column to dask dataframe, computing it through a rollin

ghz 14hours ago ⋅ 5 views

Adding a column to dask dataframe, computing it through a rolling window

Suppose I have the following code, to generate a dummy dask dataframe:

import pandas as pd
import dask.dataframe as dd
pandas_dataframe = pd.DataFrame({'A' : [0,500,1000], 'B': [-100, 200, 300]  , 'C' : [0,0,1.0] } )    
test_data_frame = dd.from_pandas( pandas_dataframe, npartitions= 1  )

Ideally I would like to know what is the recommended way to add another column to the data frame, computing the column content through a rolling window, in a lazy fashion.

I came up with the following approach:

import numpy as np
import dask.delayed as delay

@delay
def coupled_operation_example(dask_dataframe, 
                              list_of_input_lbls, 
                              fcn, 
                              window_size, 
                              init_value, 
                              output_lbl):

    def preallocate_channel_data(vector_length, first_components):
        vector_out = np.zeros(len(dask_dataframe))
        vector_out[0:len(first_components)] = first_components
        return vector_out

    def create_output_signal(relevant_data, fcn, window_size , initiated_vec):

       ## to be written; fcn would be  a fcn accepting the sliding window


    initiatied_vec = preallocate_channel_data(len(dask_dataframe, init_value))
    relevant_data = dask_dataframe[list_of_input_lbls]
    my_output_signal = create_output_signal(relevant_data, fcn, window_size, initiated_vec)

I was writing this, convinced that dask dataframe would allow me some slicing: they do not. So, my first option would be to extract the columns involved in the computations as numpy arrays, but so they would be eagerly evaluated. I think the penalty in performance would be significant. At the moment I create dask dataframes from h5 data, using h5py: so everything is lazy, until I write output files.

Up to now I was processing only data on a certain row; so I had been using:

 test_data_frame .apply(fcn, axis =1, meta = float)

I do not think there is an equivalent functional approach for rolling windows; am I right? I would like something like Seq.windowed in F# or Haskell. Any suggestion highly appreciated.

Answer

You're right that Dask DataFrames don't support the typical slicing you'd expect from Pandas, and apply over rows isn't well-suited for operations involving a rolling window. The Dask framework is designed to perform computations lazily and distribute them across multiple partitions, so operations like windowing or rolling require a slightly different approach.

In Dask, you can use a combination of Dask's lazy execution and rolling functionality. However, rolling operations like pandas.DataFrame.rolling do not work directly with Dask DataFrames in the same way. But you can achieve this behavior by leveraging the following methods:

Approach 1: Using map_partitions with rolling

You can apply a rolling operation on each partition of the Dask DataFrame using map_partitions, which lets you execute arbitrary Pandas operations on each partition in a lazy manner.

Below is an example of how you can compute a rolling window using map_partitions:

import pandas as pd
import dask.dataframe as dd

# Create the original Dask DataFrame
pandas_dataframe = pd.DataFrame({
    'A': [0, 500, 1000],
    'B': [-100, 200, 300],
    'C': [0, 0, 1.0]
})
dask_dataframe = dd.from_pandas(pandas_dataframe, npartitions=1)

# Define a function that computes a rolling window operation on each partition
def rolling_function(df, window_size):
    # Perform a rolling operation using pandas
    df['rolling_mean'] = df['A'].rolling(window=window_size, min_periods=1).mean()
    return df

# Apply the function using map_partitions
window_size = 2
result = dask_dataframe.map_partitions(rolling_function, window_size)

# Compute the result (triggering the actual computation)
result.compute()

Key Points:

  1. map_partitions: This is the core function that allows you to operate on each partition of the Dask DataFrame. Inside the function you define, you can apply any Pandas function (like rolling) and return the result.
  2. Lazy Evaluation: The operation is still lazy. Only when you call .compute() or trigger the computation does Dask actually execute the operation.
  3. Windowing: The rolling function in Pandas works in a straightforward way on a partitioned DataFrame.

Approach 2: Using dask.delayed for More Fine-grained Control

If you want more control, especially when dealing with more complex windowing functions or using custom functions on each row, you can use dask.delayed to delay computation, but this is generally less efficient than map_partitions.

Here's a more manual approach with dask.delayed:

import numpy as np
import dask.dataframe as dd
import dask.delayed as delay

# Create a dummy pandas dataframe
pandas_dataframe = pd.DataFrame({
    'A': [0, 500, 1000],
    'B': [-100, 200, 300],
    'C': [0, 0, 1.0]
})

# Convert to dask dataframe
dask_dataframe = dd.from_pandas(pandas_dataframe, npartitions=1)

@delay
def rolling_window_calculation(series, window_size):
    # Perform the rolling operation manually using a loop
    rolled_values = []
    for i in range(len(series)):
        window = series[max(0, i - window_size + 1): i + 1]
        rolled_values.append(window.mean())
    return rolled_values

# Apply the rolling calculation to the 'A' column
window_size = 2
rolling_result = rolling_window_calculation(dask_dataframe['A'], window_size)

# Convert the delayed result into a Dask DataFrame
rolling_df = dd.from_delayed(rolling_result)

# Combine the original dataframe with the rolling result
dask_dataframe['rolling_mean'] = rolling_df

# Compute the result (trigger the computation)
dask_dataframe.compute()

Key Points:

  1. dask.delayed: By using dask.delayed, you can delay the computation and create more customized processing logic (like manually computing a rolling mean).
  2. Performance: While this method provides more control, it can lead to less efficient computation since Dask has to manage the delay manually. Therefore, for simple rolling operations, the map_partitions approach is more efficient.

Summary:

For your use case, where you want to perform a rolling window calculation lazily across a Dask DataFrame, the recommended approach would be:

  • Use map_partitions for most cases, as it integrates well with Dask's parallel computation and allows you to use Pandas' rolling functionality efficiently.
  • If you need more control or have a custom function to apply, dask.delayed can be used but might be less performant.

In most cases, map_partitions is the preferred method as it leverages Dask's partitioning model and is more efficient.

#py