Check if value in pandas dataframe is within any two values of two other columns in another datafram-CodePudding

I have two dataframes of different length. dfSamples (63012375 rows) and dfFixations (200000 rows).

dfSamples = pd.DataFrame({'tSample':[4, 6, 8, 10, 12, 14]})  
dfFixations = pd.DataFrame({'tStart':[4,12],'tEnd':[8,14]})

I would like to check each value in dfSamples if it is within any two ranges given in dfFixations and then assign a label to this value. I have found this: Check if value in a dataframe is between two values in another dataframe, but the loop solution is terribly slow and I cannot make any other solution work.

Working (but very slow) example:

labels = np.empty_like(dfSamples['tSample']).astype(np.chararray)
for i, fixation in dfFix.iterrows():
    log_range = dfSamples['tSample'].between(fixation['tStart'], fixation['tEnd'])
    labels[log_range] = 'fixation'
labels[labels != 'fixation'] = 'no_fixation'
dfSamples['labels'] = labels

Following this example: Performance of Pandas apply vs np.vectorize to create new column from existing columns I have tried to vectorize this but with no success.

def check_range(samples, tstart, tend):
    log_range = (samples > tstart) & (samples < tend)
    return log_range
fixations = list(map(check_range, dfSamples['tSample'], dfFix['tStart'], dfFix['tEnd']))

Would appreciate any help!

CodePudding user response：

Use IntervalIndex.from_arrays with IntervalIndex.get_indexer , if not match is returned -1, so checked and set ouput in numpy.where:

i = pd.IntervalIndex.from_arrays(dfFixations['tStart'],
                                 dfFixations['tEnd'], 
                                 closed="both")
pos = i.get_indexer(dfSamples['tSample'])
dfSamples['labels'] = np.where(pos != -1, "fixation", "no_fixation")

print (dfSamples)
   tSample       labels
0        4     fixation
1        6     fixation
2        8     fixation
3       10  no_fixation
4       12     fixation
5       14     fixation

Performance: In ideal nice sorted not overlap data, in real should be performance different, the best test it.

dfSamples = pd.DataFrame({'tSample':range(10000)})  
dfFixations = pd.DataFrame({'tStart':range(0, 10000, 5),'tEnd':range(2, 10000, 5)})
    


In [165]: %%timeit
     ...: labels = np.empty_like(dfSamples['tSample']).astype(np.chararray)
     ...: for i, fixation in dfFixations.iterrows():
     ...:     log_range = dfSamples['tSample'].between(fixation['tStart'], fixation['tEnd'])
     ...:     labels[log_range] = 'fixation'
     ...: labels[labels != 'fixation'] = 'no_fixation'
     ...: dfSamples['labels'] = labels
     ...: 
     ...: 
1.25 s ± 52.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


In [168]: %%timeit
     ...: ii = pd.IntervalIndex.from_arrays(dfFixations['tStart'], dfFixations['tEnd'], closed="both")
     ...: dfSamples["labels1"] =  np.where(dfSamples["tSample"].apply(ii.contains).apply(any), "fixation", "no_fixation")
     ...: 
315 ms ± 18.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


In [170]: %%timeit
     ...: ii = pd.IntervalIndex.from_arrays(dfFixations['tStart'], dfFixations['tEnd'], closed="both")
     ...: contained = np.logical_or.reduce(piso.contains(ii, dfSamples["tSample"], include_index=False), axis=0)
     ...: dfSamples["labels1"] = np.where(contained, "fixation", "no_fixation")
     ...: 
82.4 ms ± 213 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [166]: %%timeit
     ...: s = pd.IntervalIndex.from_arrays(dfFixations['tStart'], dfFixations['tEnd'], closed="both")
     ...: pos = s.get_indexer(dfSamples['tSample'])
     ...: dfSamples['labels'] = np.where(pos != -1, "fixation", "no_fixation")
     ...: 
27.8 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

CodePudding user response：

setup

dfSamples = pd.DataFrame({'tSample':[4, 6, 8, 10, 12, 14]})  
dfFixations = pd.DataFrame({'tStart':[4,12],'tEnd':[8,14]})

solution

Create an interval index from your start and end points

ii = pd.IntervalIndex.from_arrays(dfFixations['tStart'], dfFixations['tEnd'], closed="both")

ii.contains is a method which checks if a point is contained by each interval in the interval index, eg

dfSamples["tSample"].apply(ii.contains)

gives

0     [True, False]
1     [True, False]
2     [True, False]
3    [False, False]
4     [False, True]
5     [False, True]
Name: tSample, dtype: object

We're going to take this result, apply any function to each element (a list) to get a pandas.Series of booleans, which we can then use with numpy.where

dfSamples["labels"] =  np.where(dfSamples["tSample"].apply(ii.contains).apply(any), "fixation", "no_fixation")

the result

   tSample       labels
0        4     fixation
1        6     fixation
2        8  no_fixation
3       10  no_fixation
4       12     fixation
5       14  no_fixation

edit: faster version

Using piso v0.6.0

import piso
import numpy as np

ii = pd.IntervalIndex.from_arrays(dfFixations['tStart'], dfFixations['tEnd'], closed="both")
contained = np.logical_or.reduce(piso.contains(ii, dfSamples["tSample"], include_index=False), axis=0)
dfSamples["labels"] = np.where(contained, "fixation", "no_fixation")

This will run in a similar time to @jezrael's solution, however it can handle cases where intervals overlaps eg

dfFixations = pd.DataFrame({'tStart':[4,5,12],'tEnd':[8,9,14]})