<jupyter_start><jupyter_text>np.mean()<jupyter_code>matrix_A=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_A
np.mean(matrix_A[0])
np.mean(matrix_A,axis=0)
np.mean(matrix_A,axis=1)
matrix_A.mean()
np.sqrt(matrix_A)
np.mean(matrix_A,dtype=np.int64,axis=1)<jupyter_output><empty_output><jupyter_text>Min & Max Values<jupyter_code>matrix_B=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_B
np.min(matrix_B)
np.amin(matrix_B)
np.minimum(matrix_B[0],matrix_B[2])
np.minimum.reduce(matrix_A)
np.min(matrix_B,axis=0)
np.max(matrix_B)
np.amax(matrix_B)
np.maximum.reduce(matrix_A)<jupyter_output><empty_output><jupyter_text>Stastical Ordered Function<jupyter_code>matrix_C=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_C
np.ptp(matrix_C)
np.ptp(matrix_C,axis=0)
np.sort(matrix_C,axis=None)
np.percentile(matrix_C,70)
np.percentile(matrix_C,70,interpolation="midpoint")
np.percentile(matrix_C,90,interpolation="nearest")
np.quantile(matrix_C,0.70,interpolation="midpoint")<jupyter_output><empty_output><jupyter_text>Average and Variance<jupyter_code>matrix_D=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_D
np.median(matrix_D[0])
np.sort(matrix_D,axis=None)
np.median(matrix_D)
np.average(matrix_D)
np.var(matrix_D)
np.std(matrix_D)
2.8**2<jupyter_output><empty_output><jupyter_text>Covariance and Corelation<jupyter_code>matrix_E=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_E
np.cov(matrix_E)
np.corrcoef(matrix_E)<jupyter_output><empty_output><jupyter_text>Histogram<jupyter_code>matrix_F=np.array([[1,0,0,3,1],[3,6,6,2,9],[4,5,3,8,0]])
matrix_F
np.sort(matrix_F,axis=None)
np.histogram(matrix_F)
np.histogram(matrix_F,bins=4,range=(1,7))
import matplotlib.pyplot as plt
plt.hist(matrix_F.flat,bins=np.histogram(matrix_F)[1])
plt.show()
np.histogram2d(matrix_F[0],matrix_F[1],bins=4)
np.histogramdd(matrix_F.transpose(),bins=4)<jupyter_output><empty_output><jupyter_text>NAN Equivalents<jupyter_code>matrix_F
np.nanmean(matrix_F)
matrix_G=np.array([[1,0,0,3,1],[3,np.nan,6,2,9],[4,5,3,8,0]])
matrix_G
np.nanmean(matrix_G)
np.mean(matrix_G)
np.nanquantile(matrix_G,0.7)
np.nanvar(matrix_G)<jupyter_output><empty_output>
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