NumPy Part-4 ||in telugu|| NumPy Tutorial ||Python for beginners

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NumPy provides a comprehensive collection of mathematical functions that can be applied to arrays. These functions are designed to operate element-wise on arrays, enabling efficient and concise mathematical operations. Below is a summary of key NumPy mathematical functions:

Arithmetic Operations

1. *Addition:*
```
np.add(array1, array2)
```
Adds corresponding elements of `array1` and `array2`.

2. *Subtraction:*
```
np.subtract(array1, array2)
```
Subtracts corresponding elements of `array2` from `array1`.

3. *Multiplication:*
```
np.multiply(array1, array2)
```
Multiplies corresponding elements of `array1` and `array2`.

4. *Division:*
```
np.divide(array1, array2)
```
Divides corresponding elements of `array1` by `array2`.

Exponential and Logarithmic Functions

1. *Exponential:*
```
np.exp(array)
```
Calculates the exponential (e^x) of each element in the array.

2. *Natural Logarithm:*
```
np.log(array)
```
Computes the natural logarithm (base e) of each element in the array.

3. *Logarithm Base 10:*
```
np.log10(array)
```
Computes the logarithm base 10 of each element in the array.

4. *Logarithm Base 2:*
```
np.log2(array)
```
Computes the logarithm base 2 of each element in the array.

Power and Root Functions

1. *Power:*
```
np.power(array, exponent)
```
Raises each element of the array to the specified power.

2. *Square Root:*
```
np.sqrt(array)
```
Computes the square root of each element in the array.

Trigonometric Functions

1. *Sine:*
```
np.sin(array)
```
Computes the sine of each element (in radians) in the array.

2. *Cosine:*
```
np.cos(array)
```
Computes the cosine of each element (in radians) in the array.

3. *Tangent:*
```
np.tan(array)
```
Computes the tangent of each element (in radians) in the array.
Rounding Functions

1. *Round:*
```
np.round(array, decimals=0)
```
Rounds each element to the specified number of decimals.

2. *Floor:*
```
np.floor(array)
```
Computes the floor of each element (rounds down to the nearest integer).

3. *Ceil:*
```
np.ceil(array)
```
Computes the ceiling of each element (rounds up to the nearest integer).

Statistical Functions

1. *Sum:*
```
np.sum(array, axis=None)
```
Computes the sum of all elements along the specified axis.

2. *Mean:*
```
np.mean(array, axis=None)
```
Computes the mean of all elements along the specified axis.

3. *Median:*

np.median(array, axis=None)
```
Computes the median of all elements along the specified axis.

4. *Standard Deviation:*

np.std(array, axis=None)
```
Computes the standard deviation of all elements along the specified axis.

5. *Variance:*

np.var(array, axis=None)
```
Computes the variance of all elements along the specified axis.

Aggregation Functions

1. *Min:*

np.min(array, axis=None)
```
Computes the minimum value of all elements along the specified axis.

2. *Max:*

np.max(array, axis=None)

Computes the maximum value of all elements along the specified axis.