8.1. Array Reduction
8.1.1. Sum
a.sum()
np.sum()
np.nansum()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1.1, 2.2, 3.3],
... [4.4, 5.5, 6.6]])
>>>
>>> a.sum()
23.1
>>>
>>> a.sum(axis=0)
array([5.5, 7.7, 9.9])
>>>
>>> a.sum(axis=1)
array([ 6.6, 16.5])
>>>
>>> np.sum(a)
23.1
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1.1, 2.2, 3.3],
... [4.4, np.nan, 6.6]])
>>>
>>> a.sum()
nan
>>>
>>> np.sum(a)
nan
>>>
>>> np.nansum(a)
17.6
8.1.2. Cumulative Sum
a.cumsum()
np.cumsum()
np.nancumsum()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1.1, 2.2, 3.3],
... [4.4, 5.5, 6.6]])
>>>
>>> a.cumsum()
array([ 1.1, 3.3, 6.6, 11. , 16.5, 23.1])
>>>
>>> np.cumsum(a)
array([ 1.1, 3.3, 6.6, 11. , 16.5, 23.1])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1.1, 2.2, 3.3],
... [4.4, np.nan, 6.6]])
>>>
>>> a.cumsum()
array([ 1.1, 3.3, 6.6, 11. , nan, nan])
>>>
>>> np.cumsum(a)
array([ 1.1, 3.3, 6.6, 11. , nan, nan])
>>>
>>> np.nancumsum(a)
array([ 1.1, 3.3, 6.6, 11. , 11. , 17.6])
8.1.3. Product
a.prod()
np.prod()
np.nanprod()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.prod()
720
8.1.4. Cumulative Product
a.cumprod()
np.cumprod()
np.nancumprod()
8.1.5. Mean
a.mean()
np.mean()
np.nanmean()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.mean()
3.5
>>>
>>> a.mean(axis=0)
array([2.5, 3.5, 4.5])
>>>
>>> a.mean(axis=1)
array([2., 5.])
8.1.6. Cumulative Mean
a.cummean()
np.cummean()
np.nancummean()
8.1.7. Variance
a.var()
np.var()
np.nanvar()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.var()
2.9166666666666665
>>>
>>> a.var(axis=0)
array([2.25, 2.25, 2.25])
>>>
>>> a.var(axis=1)
array([0.66666667, 0.66666667])
8.1.8. Standard Deviation
a.std()
np.std()
np.nanstd()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.std()
1.707825127659933
>>>
>>> a.std(axis=0)
array([1.5, 1.5, 1.5])
>>>
>>> a.std(axis=1)
array([0.81649658, 0.81649658])
8.1.9. Minimal Value
np.ndarray.argmin()
index of annp.ndarray.min()
element in arraynp.nanmin()
np.nanargmin()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.min()
1
>>>
>>> a.min(axis=0)
array([1, 2, 3])
>>>
>>> a.min(axis=1)
array([1, 4])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.argmin()
0
>>>
>>> a.argmin(axis=0)
array([0, 0, 0])
>>>
>>> a.argmin(axis=1)
array([0, 0])
>>> import numpy as np
>>>
>>> a = np.array([[99, 2, 33],
... [22, 0, 4],
... [4, 155, 6]])
>>>
>>> a.min()
0
>>> a.min(axis=0)
array([4, 0, 4])
>>> a.min(axis=1)
array([2, 0, 4])
>>> a.min(axis=-1)
array([2, 0, 4])
>>>
>>> a.argmin()
4
>>> a.argmin(axis=0)
array([2, 1, 1])
>>> a.argmin(axis=1)
array([1, 1, 0])
>>> a.argmin(axis=-1)
array([1, 1, 0])
>>>
>>> a.flat[4]
0
>>> np.unravel_index(4, (3, 3))
(1, 1)
>>> np.unravel_index(a.argmin(), a.shape)
(1, 1)
Shows the coordinates of argmin
value:
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.min()
1
>>>
>>> a.argmin()
0
>>>
>>> np.unravel_index(a.argmin(), a.shape)
(0, 0)
>>>
>>> a == a.min()
array([[ True, False, False],
[False, False, False]])
8.1.10. Maximal Value
np.ndarray.argmax()
index of ana.max()
element in arraynp.nanmax()
np.nanargmax()
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.max()
6
>>>
>>> a.max(axis=0)
array([4, 5, 6])
>>>
>>> a.max(axis=1)
array([3, 6])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.argmax()
5
>>>
>>> a.argmax(axis=1)
array([2, 2])
>>>
>>> a.argmax(axis=0)
array([1, 1, 1])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[99, 2, 33],
... [22, 0, 4],
... [4, 155, 6]])
>>>
>>> a.max()
155
>>> a.max(axis=0)
array([ 99, 155, 33])
>>> a.max(axis=1)
array([ 99, 22, 155])
>>> a.max(axis=-1)
array([ 99, 22, 155])
>>>
>>> a.argmax()
7
>>> a.argmax(axis=0)
array([0, 2, 0])
>>> a.argmax(axis=1)
array([0, 0, 1])
>>> a.argmax(axis=-1)
array([0, 0, 1])
>>>
>>> a.flat[7]
155
>>> np.unravel_index(7, (3, 3))
(2, 1)
>>> np.unravel_index(a.argmax(), a.shape)
(2, 1)
Shows the coordinates of argmax
value:
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
... [4, 5, 6]])
>>>
>>> a.max()
6
>>>
>>> a.argmax()
5
>>>
>>> np.unravel_index(a.argmax(), a.shape)
(1, 2)
>>>
>>> a == a.max()
array([[False, False, False],
[False, False, True]])
8.1.11. Median
np.median()
np.nanmedian()
8.1.12. Quantile
np.quantile()
np.nanquantile()
8.1.13. Percentile
np.percentile()
np.nanpercentile()