10
NumPy functions that are useful for data analysis:

10
NumPy functions that are useful for data analysis:

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np.mean()

np.mean()

Calculates the mean value of an array or a specific axis of an array.

Calculates the mean value of an array or a specific axis of an array.

np.argmax()

np.argmax()

Returns the index of the maximum value of an array or a specific axis of an array.

Returns the index of the maximum value of an array or a specific axis of an array.

np.sum()

np.sum()

Calculates the sum of an array or a specific axis of an array.

Calculates the sum of an array or a specific axis of an array.

np.max()

np.max()

Returns the maximum value of an array or a specific axis of an array.

Returns the maximum value of an array or a specific axis of an array.

np.argmin()

np.argmin()

Returns the index of the minimum value of an array or a specific axis of an array.

Returns the index of the minimum value of an array or a specific axis of an array.

np.reshape()

np.reshape()

Reshapes an array to a specified shape.

Reshapes an array to a specified shape.

np.transpose()

np.transpose()

ransposes an array.

ransposes an array.

np.dot()

np.dot()

Calculates the dot product of two arrays.

Calculates the dot product of two arrays.

Note that many of these functions can be applied to multi-dimensional arrays by specifying the axis parameter to apply the function along a specific axis.

Note that many of these functions can be applied to multi-dimensional arrays by specifying the axis parameter to apply the function along a specific axis.

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