In thatTutorial de Python NumPy, let's discuss the**Log NumPy do Python**and also cover the examples below:

- Python NumPy log10
- Python NumPy registry space
- Python NumPy logic e
- Python NumPy 2 Registry Base
- Logical operators Python NumPy
- Python logical number
- Python NumPy logical not
- Python NumPy registry base
- Python NumPy register 1p

Index

## Log NumPy do Python

- In this section we will learn about the
**Log NumPy do Python**. - The logarithm function is used to calculate the user to find the real logarithm of x, where x belongs to all values in the input array.
- It is the inverse of the exponential method, as well as an elementary natural logarithm.
- In logarithmic, we can easily use the np.logspace() function.
- In logspace, the iterable sequence starts at base start power and lasts with base stop.
- The natural logarithm is the inverse of the exponential method, so taking the logarithmic value of the exponential of X will give you X, so the logarithm to base E is called the natural logarithm.
- In the registration function, we will provide an array of values. The NumPy log function will generate the logarithm for these elements.
- array-like input value.

**Syntax:**

Here is the syntax for**register numpy make python**

`numpy.log (x, out=Ninguno, where=True, casting='same_kind', order='K', dtype=Ninguno)`

- It consists of few parameters.
**X:**array_like(input value)**FOR:**ndarray(A location where the result is stored. If provided, it must have a format given by the inputs. If not provided or None, a new allocated array is returned.)**Where:**array_like, optional (This condition is passed by the input. In places where the condition is True, the output array will be set to the result of the function. In other places, the output array will always be precise to its original value.)**Returns:**The natural logarithm of x, element by element

**Example:**

`importar numpy como npa = np.array([1,2,5,6,7])log_value = np.log(a)print(log_value)`

Here is the screenshot of the given code below

File:Python NumPy where with examples

## Python NumPy log10

- In this section we will learn about the
**Python NumPy log10**. - It is a statistical function that helps the user to calculate the base 10 logarithm of x, where x is an array input value.
- It is used to obtain the natural logarithm of any object or element with base 10.
- For real value input types, log 10 always returns a real output. For each value that cannot be represented as a real number.

**Syntax:**

Here is the syntax for**Python numpy log10**

`numpy.log10 (x, out=Ninguno, where=True, casting='same_kind', order='K', dtype=Ninguno)`

**Example:**

`importar numpy como npa = np.array([2,4,8,9,7])log_value = np.log10(a)print(log_value)`

Here is the screenshot of the given code below

File:Python NumPy linspace

## Python NumPy registry space

- In this section we will learn about the
**Python NumPy registry space**. - Returns the number of spaces evenly on a logarithmic scale.
- The sequence begins at the base power start and ends with a base stop.
- The log space always returns even numbers on a logarithmic scale. Logspace has the same arguments

**Syntax:**

Here is the NumPy record space syntax

`numpy.logspace(start, stop, num, endpoint=True, base, dtype=None)`

**Example:**

`import numpy as npa = (np.logspace(4,8,num=3,base=2)) #start and stopprint(a)`

Here is the screenshot of the given code below

Lerempty array python numpy

## Python NumPy logic e

- In this section we will learn about
**Python NumPy logic_y() e**. - In this method, we can easily use logical operators to use the np.logical_and() method with various conditions
- Logical AND was used to define the condition. The first logic_and() function was applied to a one-dimensional array that will return the index array of the input array where the condition will be true.
- Helps users discover the true value of arr1 and arr2 element by element. Both numpy arrays must have the same shape.

**Syntax:**

Here is NumPy logic_and syntax

`numpy.logical_and (x1, x2, out=Ninguno, Where=True, casting='mismo tipo' dtype=Ninguno)`

- It consists of few parameters.
**X1:**array_like(input_array)**FOR:**ndarray(A location where the result is stored. If provided, it must have a format given by the inputs. If not provided or None, a new allocated array is returned.)**Returns:**The Boolean result of the logical AND operation applied to the elements of x1 and x2; shape is determined.

**Example:**

`importar numpy como npx = np.array(5)y = np.logical_and(x>1, x<4)print(y);`

Here is the screenshot of the given code below

File:Python NumPy concatenated + 9 examples

## Python NumPy 2 Registry Base

- In this section we will learn about
**Python NumPy 2 registry base.** - Helps the user calculate the base 2 logarithm of x, where x is an array input value.
- It is used to obtain the natural logarithm of any base 2 object.
- Returns the base 2 logarithm of x. This is a scalar if x is a scalar.

**Syntax:**

`numpy.log2 (x, out=Ninguno, where=True, casting='same_kind', order='K', dtype=Ninguno)`

**Example:**

`importar numpy como npa = np.array([1,4,8,9,7])log_value = np.log2(a)print(log_value)`

Here is the screenshot of the given code below

LerPython NumPy iteration

## Logical operators Python NumPy

- In this section we will learn about
**Logical operators Python NumPy**. - Python NumPy logical operators should calculate the truth value using the truth table, i.e. it represents the boolean value
- The numerical logical operators are logical_and logical_not.
- In this example first, we declare a NumPy array and each element against the given condition to calculate the true value using the Python NumPy logical operators.

**Syntax:**

`numpy.log (x, out=Ninguno, where=True, casting='same_kind', order='K', dtype=Ninguno)`

**Example:**

`importar numpy como npx = np.arange(5)y = np.logical_and(x>1, x<4) #print(y)z = np.logical_or(x<2,x<3)print(z)a = np.logical_not(x<2,x<3)print(a)`

Here is the screenshot of the given code below

File:Python sort NumPy array

## Python logical number

- In this section we will learn about
**Python Logical NumPy O**. - It is a logical function and helps the user to find out the actual value of arr1 or arr2 element by element.
- But the most important point is that the array must have the same shape.
- Returns a boolean result in the same form as arr1 and arr2 of the logical operation or on the elements of arr1 and arr2.

**Syntax:**

Here is the logical syntax or

`numpy.logical_or (x1, x2, out=Ninguno, Where=True, casting='mismo tipo' dtype=Ninguno)`

**Example:**

`importar numpy como nparr1 = [2, 4, False, 6]arr2 = [2,3,True,7]log_arr = np.logical_or(arr1, arr2)print(log_arr)`

Here is the screenshot of the given code below

LerPython NumPy absolute value

## Python NumPy logical not

- In this section we will learn about
**Python NumPy logical number.** - It is a logical function and helps the user to calculate the truth value of NOT arr element by element.
- Returns boolean results of non-array elements.

**Syntax:**

Here is the non-logical syntax

`numpy.logical_and (arr1, arr2, out=Ninguno, Where=True, casting='mismo tipo' dtype=Ninguno)`

**Example:**

`import numpy as nparr1 = [2, 4, False, 6]arr2 = [2,3.2,True,False]log_arr = np.logical_not(arr1)log_arr2= np.logical_not(arr2)print(log_arr)print(log_arr2)`

Here is the screenshot of the given code below

File:Python Array NumPy + Examples

## Python NumPy registry base

- In this section we will learn about the
**Python NumPy registry base**. - The Numpy log() function offers the ability to find logarithmic values relative to user-defined bases.
- The natural logarithm is the inverse of the exponential function, so the logarithm of the exponential of X will give you X, so the logarithm to base E is called the natural logarithm.

**Syntax:**

`numpy.log (x, out=Ninguno, where=True, casting='same_kind', order='K', dtype=Ninguno)`

**Example:**

`import numpy as npdata = 10base = 4log_val = np.log(data)/np.log(base)print(log_val)`

In the example above, we calculated the logarithmic value of 10 with base 4.

Here is the screenshot of the given code below.

## Python NumPy register 1p

- In this section we will learn about the
**Python NumPy register 1p**. - It is a statistical function used to obtain the value of the natural logarithm x+1, where x is a value of a numpy array.
- log1p is the inverse of exp(x)-1.
- The numpy.log1p() method accepts two parameters which are the arr and out parameters and returns a numpy array of natural logarithms of value x+1 of the elements of the given array values.

**Syntax:**

`numpy.log1p (arr, out=None, where=True)`

- It consists of two parameters:
**ARR:**Is the object whose log is to be calculated.**FOR:**It is the place where the result is stored.

**Example:**

`importar numpy como nparr= ([2,3,4,5,6])out = np.log1p(arr)print(out)`

Here is the screenshot of the given code below

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In this Python NumPy tutorial, we discuss**Log NumPy do Python**and also cover the examples below:

- Python NumPy log10
- Python NumPy registry space
- Python NumPy logic e
- Python NumPy 2 Registry Base
- Logical operators Python NumPy
- Python logical number
- Python NumPy logical not
- Python NumPy registry base
- Python NumPy register 1p

Bijay Kumar

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