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20 Must-Know NumPy Functions for Data Analysis and Machine Learning in Python

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Rhythm Rawat
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NumPy is a powerful library for data analysis and machine learning in Python. It provides a wide range of mathematical functions for performing complex operations on large datasets. In this blog, we will discuss 20 important NumPy functions that are commonly used in data analysis and machine learning.

  1. np.array(): This function is used to create a NumPy array from a Python list or tuple.

  2. np.arange(): This function is used to create a NumPy array with evenly spaced values within a given range.

  3. np.linspace(): This function is used to create a NumPy array with a specified number of evenly spaced values within a given range.

  4. np.zeros(): This function is used to create a NumPy array filled with zeros.

  5. np.ones(): This function is used to create a NumPy array filled with ones.

  6. np.eye(): This function is used to create an identity matrix with a specified size.

  7. np.random.rand(): This function is used to create a NumPy array with random values between 0 and 1.

  8. np.random.randn(): This function is used to create a NumPy array with random values from a standard normal distribution.

  9. np.max(): This function is used to find the maximum value in a NumPy array.

  10. np.min(): This function is used to find the minimum value in a NumPy array.

  11. np.mean(): This function is used to find the mean value of a NumPy array.

  12. np.median(): This function is used to find the median value of a NumPy array.

  13. np.std(): This function is used to find the standard deviation of a NumPy array.

  14. np.var(): This function is used to find the variance of a NumPy array.

  15. np.sum(): This function is used to find the sum of all elements in a NumPy array.

  16. np.dot(): This function is used to perform matrix multiplication between two NumPy arrays.

  17. np.transpose(): This function is used to transpose a NumPy array.

  18. np.reshape(): This function is used to reshape a NumPy array.

  19. np.concatenate(): This function is used to concatenate two or more NumPy arrays.

  20. np.split(): This function is used to split a NumPy array into multiple smaller arrays.

In conclusion, NumPy provides a vast range of functions for data analysis and machine learning that can help you perform complex mathematical operations on large datasets. By using these functions, you can analyze and manipulate data more efficiently and accurately.Hope you got value out of this blog. Subscribe to get more such.

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