3.4. Array Reduction¶
3.4.1. 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
3.4.2. 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])
3.4.3. Product¶
a.prod()np.prod()np.nanprod()
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
a.prod()
# 720
3.4.4. Cumulative Product¶
a.cumprod()np.cumprod()np.nancumprod()
3.4.5. 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.])
3.4.6. Cumulative Mean¶
a.cummean()np.cummean()np.nancummean()
3.4.7. 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])
3.4.8. 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])
3.4.9. 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]])
3.4.10. 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]])
3.4.11. Median¶
np.median()np.nanmedian()
3.4.12. Quantile¶
np.quantile()np.nanquantile()
3.4.13. Percentile¶
np.percentile()np.nanpercentile()
3.4.14. Assignments¶
Todo
Create assignments