1. `np.array()`: This function is used to create a NumPy array from a list or tuple. It is the fundamental building block for working with arrays in NumPy.
2. `np.zeros()`: This function creates an array filled with zeros of a specified shape. It is useful for initializing arrays before performing calculations.
3. `np.ones()`: Similar to `np.zeros()`, this function creates an array filled with ones of a specified shape. It can be used for initializing arrays with a constant value.
4. `np.arange()`: This function generates a range of values within a specified interval. It is similar to the built-in `range()` function in Python but returns a NumPy array.
5. `np.linspace()`: This function creates an array of evenly spaced values within a specified range. It is commonly used for generating data points for plotting graphs.
6. `np.mean()`: This function calculates the arithmetic mean of an array. It is useful for finding the average value of a set of data.
7. `np.sum()`: This function computes the sum of all elements in an array. It is a versatile function that can be used for various calculations.
8. `np.min()`, `np.max()`: These functions return the minimum and maximum values in an array, respectively. They are handy for finding the extremes in a dataset.
9. `np.shape()`: This function returns the shape of an array, i.e., the number of rows and columns. It is essential for understanding the structure of arrays.
10. `np.transpose()`: This function transposes an array, i.e., it switches the rows and columns of an array. It is useful for reshaping data for different operations.
These are just a few of the essential NumPy functions that every Python developer should be familiar with. NumPy offers a wide range of functions for working with arrays and performing numerical computations. By mastering these functions, you can leverage the power of NumPy for your data analysis and scientific computing projects. So, start practicing these functions and see how they can enhance your Python coding skills.
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