# Determine the number of overlapping time slots in a data frame

I have a list of contracts with start and end dates.

How can I calculate the number of overlapping contracts over the duration of the contracts?

```
df = pd.DataFrame({
'contract': pd.Series(['A1', 'A2', 'A3', 'A4']),
'start': pd.Series(['01/01/2015', '03/02/2015', '15/01/2015', '10/01/2015']),
'end': pd.Series(['16/01/2015', '10/02/2015', '18/01/2015', '12/01/2015'])
})
```

which gives:

```
contract end start
0 A1 16/01/2015 01/01/2015
1 A2 10/02/2015 03/02/2015
2 A3 18/01/2015 15/01/2015
3 A4 12/01/2015 10/01/2015
```

A1 overlaps A3 and A4, so overlaps = 2. A2 overlaps without contract, so overlaps = 0. A3 overlaps with A1, so overlaps = 1. A4 overlaps with A1, so overlaps = 1.

I could just compare each time span (from start to finish), but is that `O(n**2)`

any better idea?

I have a feeling it could be improved by sorting and then looping through the sorted ranges

+3

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1 answer

Here's how to do it:

```
df = pd.DataFrame({
'contract': pd.Series(['A1', 'A2', 'A3', 'A4']),
'start': pd.Series(['01/01/2015', '03/02/2015', '15/01/2015', '10/01/2015']),
'end': pd.Series(['16/01/2015', '10/02/2015', '18/01/2015', '12/01/2015'])
})
df['start'] = pd.to_datetime(df.start, dayfirst=True)
df['end'] = pd.to_datetime(df.end, dayfirst=True)
periods = df[['start', 'end']].apply(lambda x: (pd.date_range(x['start'], x['end']),), axis=1)
overlap = periods.apply(lambda col: periods.apply(lambda col_: col[0].isin(col_[0]).any()))
df['overlap_count'] = overlap[overlap].apply(lambda x: x.count() - 1, axis=1)
print df
```

What gives:

```
contract end start overlap_count
0 A1 2015-01-16 2015-01-01 2
1 A2 2015-02-10 2015-02-03 0
2 A3 2015-01-18 2015-01-15 1
3 A4 2015-01-12 2015-01-10 1
```

I updated the code to output the number of matches, not the overlap in days.

+1

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