By:
On:
In: Numpy

The numpy.fix() function is used to round the array values to the nearest integers towards zero. Syntax of numpy.fix(): The syntax required to use this function is as follows: Parameters: The description of the parameters of this function is as follows: Returned Values: This function always returns an array containing the rounded values. Now it’sContinue Reading

By:
On:
In: Numpy

The numpy.trunc() function of Python is used to return the truncated value of the input array elements. Syntax of numpy.trunc(): Below we have the required syntax to use this function: Note: In the above syntax, the array is used to indicate the array elements whose truncated values are to be returned. Returned Values: This method mainly returns anContinue Reading

By:
On:
In: Numpy

The numpy.floor() is used to return the floor value of the elements of an array. The floor value of any given scalar value x is the largest integer value, such that i <= x. For example, the floor value for 2.3 will be 2. Syntax of numpy.floor(): Below we have a basic syntax to use thisContinue Reading

By:
On:
In: Numpy

The numpy.ceil() function is used to return the ceil of the elements of an array. The ceil value of any scalar value x is the smallest integer i in a way such that i >= x. For example, the ceil value for 5.6 will be 6. In simpler words we can say, the nearest larger integer value is the ceil value. SyntaxContinue Reading

By:
On:
In: Numpy

The partition() function is used to split up the input array accordingly as per the given arguments. Syntax of numpy.partition(): The syntax required to use this method is as follows: Parameters: let us now take a look at the parameters of this function: Returned Values: This Function will return an Array of the same typeContinue Reading

By:
On:
In: Numpy

The transpose() function in the numpy library is mainly used to reverse or permute the axes of an array and then it will return the modified array. Syntax of numpy.transpose(): The syntax required to use this function is as follows: Parameters: Let us take a look at the parameters of this function: Returned Values: The transpose() functionContinue Reading

By:
On:
In: Numpy

In this tutorial, we will cover the concept of array() function in the NumPy library. The array() function in the NumPy library is mainly used to create an array. Just like the Numpy arange() function. Syntax of numpy.array(): The syntax required to use this method is as follows: Parameters: Let us now take a look atContinue Reading

By:
On:
In: Numpy

In this tutorial, we will cover the concatenate() function of the NumPy library. The concatenate() function is mainly used in order to combine two or more NumPy arrays together. ence we can say that the concatenate() function can be used to join a sequence of arrays along an existing axis. Syntax of numpy.concatenate(): The syntax required toContinue Reading

By:
On:
In: Numpy

In this tutorial, we will cover the numpy.append() function of the NumPy library. The “append” word simply means to add something to the existing data at the end or at the last. Syntax of numpy.append(): The syntax required to use this function is as follows: Parameters: Let us take a look at the parameters of this function:Continue Reading

By:
On:
In: Numpy

In this tutorial, we will cover the reshape() function of the NumPy library. The word “reshape” simply indicates changing the shape and that is what this function is used for Syntax of reshape(): The syntax required to use this function is as follows: Parameters: Let us take a look at the parameters ofContinue Reading