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I have a pandas data frame and I want to calculate some features based on some short_window, long_window and bins values. More specifically, for each different row, I want to calculate some features. In order to do so, I move one row forward the df_long = df.loc[row:long_window+row] such as in the first iteration the pandas data frame for row=0 would be df_long = df.loc[0:50+0] and some features would be calculated based on this data frame, for row=1 would be df_long = df.loc[1:50+1] and some other features would be calculated and continues.

from numpy.random import seed
from numpy.random import randint
import pandas as pd
from joblib import Parallel, delayed

bins = 12
short_window = 10
long_window = 50

# seed random number generator
seed(1)

price = pd.DataFrame({
    'DATE_TIME': pd.date_range('2012-01-01', '2012-02-01', freq='30min'),
    'value': randint(2, 20, 1489),
    'amount': randint(50, 200, 1489)
})


def vap(row, df, short_window, long_window, bins):

    df_long = df.loc[row:long_window+row]
    df_short = df_long.tail(short_window)

    binning = pd.cut(df_long['value'], bins, retbins=True)[1]
    group_months = pd.DataFrame(df_short['amount'].groupby(pd.cut(df_short['value'], binning)).sum())

    return group_months['amount'].tolist(),  df.loc[long_window + row + 1, 'DATE_TIME']


def feature_extraction(data, short_window, long_window, bins):

    # Vap feature extraction
    ls = [f"feature{row + 1}" for row in range(bins)]
    amount, date = zip(*Parallel(n_jobs=4)(delayed(vap)(i, data, short_window, long_window, bins)
                                                for i in range(0, data.shape[0] - long_window - 1)))
    temp = pd.DataFrame(date, columns=['DATE_TIME'])
    temp[ls] = pd.DataFrame(amount, index=temp.index)
    data = data.merge(temp, on='DATE_TIME', how='outer')

    return data


df = feature_extraction(price, short_window, long_window, bins)

I tried to run it in parallel in order to save time but due to the dimensions of my data, it takes a long of time to finish.

Is there any way to change this iterative process (df_long = df.loc[row:long_window+row]) in order to reduce the computational cost? I was wondering if there is any way to use pandas.rolling but I am not sure how to use it in this case.

Any help would be much appreciated! Thank you

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  • \$\begingroup\$ This might be of use: pandas.pydata.org/pandas-docs/stable/reference/api/…. Is this program runnable? Can you explain what it's meant to do? \$\endgroup\$ – AMC Dec 14 '19 at 1:17
  • \$\begingroup\$ Yes, it is runnable. For each different pandas data frame of a specific length calculates some features. Then this is being done for each different row. I know pandas rolling but I don't know how there is a way to run this example with pandas.rolling. Thanks! \$\endgroup\$ – Nestoras Chalkidis Dec 14 '19 at 10:42
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Just some stylistic suggestions

Constants

Constants in your program should be UPPERCASE. (PEP 8)

bins -> BINS
short_window -> SHORT_WINDOW
long_window -> LONG_WINDOW
price -> PRICE

Docstrings

You can add docstrings to your functions to allow more description about the function and about the parameters it accepts and the value(s) it returns, if any. (PEP 8)

def vap(row, df, short_window, long_window, bins):
    """
    Description Here

    :param row: Description Here
    :param df: Description Here
    :param short_window: Description Here
    :param long_window: Description Here
    :param bins: Description Here

    :return: Description Here
    """

Type Hints

You can add type hints to your functions to show what types of parameters are accepted, and what types are returned.

You can also use typing's NewVar to create custom types to return.

from typing import List

PandasTimeStamp = NewType("PandasTimeStamp", pd._libs.tslibs.timestamps.Timestamp)

def vap(row: int, df: pd.DataFrame, short_window: int, long_window: int, bins: int) -> List, PandasTimeStamp:
    ...
| improve this answer | |
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  • \$\begingroup\$ Thanks but this suggestion doesn't solve my problem. \$\endgroup\$ – Nestoras Chalkidis Dec 17 '19 at 12:07

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