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In this python numpy program, we will calculate the variance of an array using NumPy.
import numpy as np
age = np.array([20,25,21,23,26,27,26,30,24,26])
print("Variance: ",np.var(age))
Output :
Variance: 7.76
In this python numpy program, we will calculate the standard deviation of an array using NumPy.
import numpy as np
age = np.array([20,25,21,23,26,27,26,30,24,26])
print("Standard Deviation: ",np.std(age))
Output :
Standard Deviation: 2.7856776554368237
In this python numpy program, we will calculate the sin angle of an array using NumPy.
import numpy as np
angle = np.array([45,13])
print(f"Sin of {angle}: ",np.sin(angle))
Output :
Sin of [45 13]: [0.85090352 0.42016704]
In this python numpy program, we will check whether the dimensions of two arrays are the same or not.
import numpy as np
x = np.arange(15).reshape(3,5)
y = np.arange(20).reshape(4,5)
if x.shape == y.shape:
print("Same dimensions")
else:
print("Different dimensions")
Output :
Different dimensions
In this python numpy program, we will create a NumPy array and reshape it.
import numpy as np
x = np.arange(30)
y = x.reshape(5,6)
print("Array: \n",y)
Output :
Array:
[[ 0 1 2 3 4 5]
[ 6 7 8 9 10 11]
[12 13 14 15 16 17]
[18 19 20 21 22 23]
[24 25 26 27 28 29]]
In this python numpy program, we will convert a NumPy array to a data frame with headers.
import numpy as np
import pandas as pd
x = np.random.rand(6,3)
df = pd.DataFrame(x,columns=['A','B','C'])
print("\nPandas DataFrame: ")
print(df)
Output :
Pandas DataFrame:
A B C
0 0.307619 0.526206 0.982625
1 0.865438 0.248926 0.248677
2 0.080811 0.619051 0.774105
3 0.716698 0.419266 0.181618
4 0.223379 0.055573 0.168672
5 0.106160 0.929609 0.886170
create an array and reshape it.
In this python numpy program, we will merge two given NumPy arrays of identical shape.
import numpy as np
x = np.array([1,2,3])
y = np.array([4,5,6])
print("Original arrays:")
print(x)
print(y)
output = np.concatenate((x, y))
print("\nAfter concatenate:")
print(output)
Output :
Original arrays:
[1 2 3]
[4 5 6]
After concatenate:
[1 2 3 4 5 6]
calculate averages without NaNs along a row.
In this python numpy program, we will calculate averages without NaNs along a row of an array.
import numpy as np
x = np.array([[6, 5, np.nan],[np.nan, 9, 1],[7, 8, 2]])
temp = np.ma.masked_array(x,np.isnan(x))
print("Array after removing nan values: \n",temp)
print("\nAverages without NaNs along the said array:")
print(np.average(temp,axis=1))
Output :
Array after removing nan values:
[[6.0 5.0 --]
[-- 9.0 1.0]
[7.0 8.0 2.0]]
Averages without NaNs along the said array:
[5.5 5.0 5.666666666666667]
In this python numpy program, we will calculate the sum of all columns of the NumPy array.
import numpy as np
x = np.array([[6, 5, 7],[10, 9, 1],[7, 8, 2]])
print("Sum of all columns: ",x.sum(axis=0))
Output :
Sum of all columns: [23 22 10]
In this python numpy program, we will copy data from a given array to another array.
import numpy as np
x = np.array([[6, 5, 7],[10, 9, 1],[7, 8, 2]])
y = [None] * len(x)
print(y)
for i in range(0,len(x)):
y[i] = x[i]
print("Elements of original array: ");
for i in range(0, len(x)):
print(x[i])
print("Elements of new array: ");
for i in range(0, len(y)):
print(y[i])
Output :
[None, None, None]
Elements of original array:
[6 5 7]
[10 9 1]
[7 8 2]
Elements of new array:
[6 5 7]
[10 9 1]
[7 8 2]
calculate the sum of all columns of an array