Top 20 NumPy Library Functions You Need to Know

NumPy is a popular Python library for numerical computing and data analysis. Here are some of the most important NumPy functions used in data science:

1. numpy.array(): Creates a NumPy array
2. numpy.arange(): Returns evenly spaced values within a given interval
3. numpy.linspace(): Returns evenly spaced numbers over a specified interval
4. numpy.zeros(): Creates an array filled with zeros
5. numpy.ones(): Creates an array filled with ones
6. numpy.eye(): Creates a 2-D array with ones on the diagonal and zeros elsewhere
7. numpy.random.rand(): Generates an array of random numbers
8. numpy.random.randn(): Generates an array of random numbers with a normal distribution
9. numpy.reshape(): Changes the shape of an array
10. numpy.transpose(): Transposes an array
11. numpy.dot(): Computes the dot product of two arrays
12. numpy.sum(): Calculates the sum of array elements
13. numpy.mean(): Calculates the mean of array elements
14. numpy.std(): Calculates the standard deviation of array elements
15. numpy.max(): Returns the maximum value of an array
16. numpy.min(): Returns the minimum value of an array
17. numpy.argmax(): Returns the index of the maximum value in an array
18. numpy.argmin(): Returns the index of the minimum value in an array
19. numpy.unique(): Finds the unique elements in an array
20. numpy.concatenate(): Joins two or more arrays together

These functions are just a small subset of what NumPy has to offer, but they are some of the most frequently used functions in data science.