CODEWITHSUNDEEP
pythonpandasnumpydataframe
MrClean
MrClean

Reputation: 1460

Pulling Multiple Ranges from Dataframe Pandas

Lets say I have the following data set:

A      B    
10.1   53
12.5   42
16.0   37
20.7   03
25.6   16
30.1   01
40.9   19
60.5   99

I have a the following list of ranges. 

[[9,15],[19,22],[39,50]]

How do I efficiently pull rows that lie in those ranges?

Wanted Output

A      B    
10.1   53
12.5   42
20.7   03
40.9   19

Edit: Needs to work for floating points

Upvotes: 1

Views: 156

Answers (2)

David Gaertner
David Gaertner

Reputation: 386

Here's another method (edit: works with floats or integers). @jpp's might be faster, but this code is easier to understand (in my opinion).

df = pd.DataFrame([[10.1,53],[12.5,42],[16.0,37],[20.7,3],[25.6,16],[30.1,1],[40.9,19],[60.5,99]],columns=list('AB'))
ranges = [[9,15],[19,22],[39,50]]  

result = pd.DataFrame(columns=list('AB'))
for r in ranges:
    result = result.append(df[df['A'].between(r[0], r[1], inclusive=False)])

print (result)

Here's the output:

      A   B
0  10.1  53
1  12.5  42
3  20.7   3
6  40.9  19

PS: the following one-line list comprehension also works:

result = result.append([source[source['A'].between(r[0], r[1], inclusive=False)] for r in ranges])

Upvotes: 0

jpp
jpp

Reputation: 164773

Update for modified question

For floats, you can construct a mask using NumPy array operations:

L = np.array([[9,15],[19,22],[39,50]])
A = df['A'].values

mask = ((A >= L[:, 0][:, None]) & (A <= L[:, 1][:, None])).any(0)

res = df[mask]

print(res)

      A   B
0  10.1  53
1  12.5  42
3  20.7   3
6  40.9  19

Previous answer to original question

For integers, you can use numpy.concatenate with numpy.arange:

L = [[9,15],[19,22],[39,50]]

vals = np.concatenate([np.arange(i, j) for i, j in L])

res = df[df['A'].isin(vals)]

print(res)

    A   B
0  10  53
1  12  42
3  20   3
6  40  19

An alternative solution with itertools.chain and range:

from itertools import chain

vals = set(chain.from_iterable(range(i, j) for i, j in L))

res = df[df['A'].isin(vals)]

Upvotes: 1

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