Split data by threshold

Divide the data in ascending order into the number of thresholds + 1 at multiple thresholds

python3

import numpy as np, pandas as pd
def partition(attr, ths, tgt=None):
    if tgt is None:
        tgt = attr
    elif isinstance(attr, pd.DataFrame):
        tgt = attr[tgt]
    po = 0
    for th in ths:
        pr = po
        while tgt[po] < th:
            po += 1
        yield tgt[pr:po]
    yield tgt[po:]
# def partition(arr, ths, tgt=None):
#     if tgt is None:
#         tgt = arr
#     elif isinstance(arr, pd.DataFrame):
#         tgt = arr[tgt]
#     r = []
#     pr = 0
#     for th in ths:
#         po = ilen(takewhile(lambda i: i < th, tgt[pr:]))+pr
#         r.append(arr[pr:po])
#         pr = po
#     r.append(arr[po:])
#     return r

from IPython.display import display
for i in partition(range(1,11), [3,6]):
    display(i)
for i in partition(np.arange(1,11), [3,6]):
    display(i)
for i in partition(pd.Series(np.arange(1,11)), [3,6]):
    display(i)
for i in partition(pd.DataFrame(np.arange(1,11)), [3,6], 0):
    display(i)
>>>
range(1, 3)
range(3, 6)
range(6, 11)

array([1, 2])
array([3, 4, 5])
array([ 6,  7,  8,  9, 10])

0    1
1    2
dtype: int32
2    3
3    4
4    5
dtype: int32
5     6
6     7
7     8
8     9
9    10
dtype: int32

The comment part was made with reference to "NumPy to find the position above the threshold value --Qiita", but if the number is small, simply While was faster, so I replaced it.

that's all