Analyze Data with Python - NumPy for Beginners

                                

import numpy as np
numbers = np.array([-5, 4, 0, 2, 3])
positives = numbers[numbers > 0]
print(positives)
# Output: array([4, 2, 3])


                              

                                

import numpy as np
numbers = np.array([-5, 4, 0, 2, 3])
is_positive = numbers > 0
print(is_positive)
# Output: array([False, True, False, True, True], dtype=bool)


                              

                                

import numpy as np
imported_data = np.genfromtxt("filename.csv", delimiter=",")


                              

                                

import numpy as np
my_array = np.array([1, 2, 3, 4])
print(my_array)
# Output: array([1, 2, 3, 4])


                              

                                

import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[0, 0])
# Output: 1
print(matrix[1, :])
# Output: array([4, 5, 6])


                              

                                

import numpy as np
digits = np.array([1, 3, 5, 7, 9])
evens = digits + 1
print(evens)
# Output: array([2, 4, 6, 8, 10])


                              

                                

import numpy as np
import matplotlib.pyplot as plt
# Generate data with normal distribution
mu = 0
sigma = 0.1
data = np.random.normal(mu, sigma, 1000)
plt.hist(data, bins=30)
plt.show()


                              

                                

import numpy as np
import matplotlib.pyplot as plt
# Example data
x_axis = np.random.normal(0, 1, 1000)
plt.hist(x_axis, bins=20, color='blue')
plt.show()


                              

                                

import numpy as np
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
mean_per_row = np.mean(data, axis=1)
print(mean_per_row)
# Output: array([2., 5., 8.])


                              

                                

import numpy as np
import matplotlib.pyplot as plt
# Example data
heights = np.array([49.7, 46.9, 62, 47.2, 47, 48.3, 48.7])
mean_height = np.mean(heights)
print(mean_height)
# Output: 48.4


                              

                                

import numpy as np
percentile_25 = np.percentile(data, 25)
print(percentile_25)
# Output: value at 25th percentile


                              

                                

import numpy as np
heights = np.array([49.7, 46.9, 62, 47.2, 47, 48.3, 48.7])
sorted_heights = np.sort(heights)
print(sorted_heights)
# Output: array([46.9, 47. , 47.2, 48.3, 48.7, 49.7, 62. ])