numpy.searchsorted()
is used to search for indices in the sorted array arr, so if elements are inserted before the indices, the order of arr will still be preserved. Here binary search is used to find the required insertion indices.
Syntax: numpy.searchsorted (arr, num, side = `left`, sorter = None)
Parameters:
arr: [array_like] Input array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices that sort it.
num: [array_like] The Values which we want to insert into arr.
side: [`left`, `right`], optional.If `left`, the index of the first suitable location found is given. If `right`, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of a).
num: [array_like, Optional] array of integer indices that sort array a into ascending order. They are typically the result of argsort.Return: [indices], Array of insertion points with the same shape as num.
Code # 1: Work

Output:
Input array: [2, 3, 4, 5, 6] The number which we want to insert: 4 Output indices to maintain sorted array: 2
Code # 2:

Output:
Input array: [2, 3, 4, 5, 6] The number which we want to insert: 4 Output indices to maintain sorted array: 3
Code # 3:

Output:
Input array: [2, 3, 4, 5, 6] The number which we want to insert: [4, 8, 0] Output indices to maintain sorted array: [2 5 0]
You can use pandas.cut
:
bins = [0, 1, 5, 10, 25, 50, 100]
df["binned"] = pd.cut(df["percentage"], bins)
print (df)
percentage binned
0 46.50 (25, 50]
1 44.20 (25, 50]
2 100.00 (50, 100]
3 42.12 (25, 50]
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df["binned"] = pd.cut(df["percentage"], bins=bins, labels=labels)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
bins = [0, 1, 5, 10, 25, 50, 100]
df["binned"] = np.searchsorted(bins, df["percentage"].values)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
...and then value_counts
or groupby
and aggregate size
:
s = pd.cut(df["percentage"], bins=bins).value_counts()
print (s)
(25, 50] 3
(50, 100] 1
(10, 25] 0
(5, 10] 0
(1, 5] 0
(0, 1] 0
Name: percentage, dtype: int64
s = df.groupby(pd.cut(df["percentage"], bins=bins)).size()
print (s)
percentage
(0, 1] 0
(1, 5] 0
(5, 10] 0
(10, 25] 0
(25, 50] 3
(50, 100] 1
dtype: int64
By default cut
returns categorical
.
Series
methods like Series.value_counts()
will use all categories, even if some categories are not present in the data, operations in categorical.
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