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Numpy Functions in Python

Last Updated on March 2, 2023 by Prepbytes

NumPy is a powerful library for scientific computing in Python that provides a multidimensional array object and a wide range of tools for working with these arrays. It is widely used in fields such as data analysis, machine learning, and scientific computing. In this article, we will be discussing NumPy functions in Python, which are essential for the efficient and effective manipulation of NumPy arrays.

What are Numpy Functions in Python?

NumPy functions are built-in functions provided by the NumPy library that operate on NumPy arrays. These functions are designed to efficiently process large amounts of data and perform complex calculations on multidimensional arrays. NumPy functions are designed to work with NumPy arrays and offer superior performance compared to traditional Python functions.

Syntax of Numpy Functions in Python

The syntax of NumPy functions in Python typically follows the following structure:

``numpy.function_name(array, parameters)``

where "function_name" is the name of the NumPy function, "array" is the NumPy array on which the function is to be applied, and "parameters" are any additional parameters required by the function.

Parameters of Numpy Functions in Python

NumPy functions in Python take one or more arrays as input and can also accept additional parameters that modify their behavior. The parameters of NumPy functions depend on the specific function being used. Here are some common parameters:

• axis: This parameter specifies the axis along which the function is applied. It is an optional parameter and its default value is None. If axis is set to None, the function will be applied to the entire array.
• dtype: This parameter specifies the data type of the returned array. It is an optional parameter and its default value is None. If dtype is set to None, the data type of the returned array will be the same as the input array.
• out: This parameter specifies the output array where the result of the function will be stored. It is an optional parameter and its default value is None. If out is set to None, the function will create a new array to store the result.
• keepdims: This parameter specifies whether to keep the dimensions of the input array in the output array. It is an optional parameter and its default value is False. If keepdims is set to True, the dimensions of the output array will be the same as the input array. If keepdims is set to False, the dimensions of the output array will be reduced by one.

Return Value of Numpy Functions in Python

The return value of a NumPy function in Python depends on the specific function being used. Some NumPy functions return a new array with the calculated results, while others modify the original array in place. However, some NumPy functions have optional output parameters such as the ‘out’ parameter. These functions allow you to specify an output array where the result will be stored. In this case, the return value will be a reference to the output array rather than a new array.

Important Numpy Functions in Python

Here are some of the important NumPy functions in Python which every Data scientist must know:

1. np.array(): This function is used to create an array in NumPy.

Example Code:

``````import numpy as np

arr = np.array([1, 2, 3])
print(arr)``````

Output:

``[1 2 3]``

Explanation: In this example, we created a numpy array by passing a list of 3 numbers as a parameter into np.array() function.

2. np.arange(): This function is used to create an array with a range of values.

Example Code:

``````import numpy as np

arr = np.arange(1, 6)
print(arr)``````

Output:

``[1 2 3 4 5]``

Explanation: In this example, we created a numpy array with a range of values which is (1,6), where 6 is excluded.

3. np.zeros(): This function is used to create an array filled with zeros.

Example Code:

``````import numpy as np

arr = np.zeros(3)
print(arr)``````

Output:

``[0. 0. 0.]``

Explanation: In this example, we created an array of size 3 which is filled with only zeroes.

4. np.ones(): This function is used to create an array filled with ones.

Example Code:

``````import numpy as np

arr = np.ones(3)
print(arr)``````

Output:

``[1. 1. 1.]``

Explanation: In this example, we created an array of size 3 which is filled with only ones.

5. np.linspace(): This function is used to create an array with a specified number of evenly spaced values.

Example Code:

``````import numpy as np

arr = np.linspace(0, 1, 5)
print(arr)``````

Output:

``[0.   0.25 0.5  0.75 1.  ]``

6. np.random.rand(): This function is used to create an array with random values between 0 and 1.

Example Code:

``````import numpy as np

arr = np.random.rand(3)
print(arr)``````

Output:

``[0.5488135  0.71518937 0.60276338]``

Explanation: In this example, we created an array of size 3 which is filled with random values which lie between 0 and 1.

7. np.random.randint(): This function is used to create an array with random integer values between a specified range.

Example Code:

``````import numpy as np

arr = np.random.randint(0, 10, 5)
print(arr)``````

Output:

``[1 5 8 9 8]``

Explanation: In this example, we created an array of size 5 which is filled with random values which lie between 0 and 10.

8. np.max(): This function is used to find the maximum value in an array.

Example Code:

``````import numpy as np

arr = np.array([1, 2, 3])
max_value = np.max(arr)
print(max_value)``````

Output:

``3``

Explanation: In this example, we used max() function to find the max element in our array, which is 3 in this case.

9. np.min(): This function is used to find the minimum value in an array.

Example Code:

``````import numpy as np

arr = np.array([1, 2, 3])
min_value = np.min(arr)
print(min_value)``````

Output:

``1``

Explanation: In this example, we used min() function to find the min element in our array, which is 1 in this case.

10. np.mean(): This function is used to find the mean value of an array.

Example Code:

``````import numpy as np

arr = np.array([1, 2, 3])
mean_value = np.mean(arr)
print(mean_value)``````

Output:

``2.0``

11. np.median(): This function is used to find the median value of an array.

Example Code:

``````import numpy as np

arr = np.array([1, 2, 3])
median_value = np.median(arr)
print(median_value)``````

Output:

``2.0``

12. np.dot(): This function is used to find the dot product of two arrays.

Example Code:

``````import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

dot_product = np.dot(arr1, arr2)
print(dot_product)``````

Output:

``32``

Explanation: In this example, we created two NumPy arrays called array1 and array2, which contain the values [1, 2, 3] and [4, 5, 6], respectively. We then used np.dot() to calculate the dot product of these two arrays, which is equal to 14 + 25 + 3*6, or 32. The resulting value is stored in the variable result and printed to the console.

Summary
Here’s a summary of the article on NumPy functions in Python:

• NumPy is a Python library for numerical computing that provides powerful data structures and functions for scientific computing tasks.
• NumPy arrays are the primary data structure used in NumPy, and they can be one-dimensional or multi-dimensional.
• NumPy functions are used to create, manipulate, and analyze NumPy arrays.
• The syntax of NumPy functions generally involves calling the function and passing one or more parameters, such as the shape of the array, the range of values to generate, or the type of data to use.
• The return value of NumPy functions can be an array, a scalar value, or a tuple of values.
• Some important NumPy functions include np.array(), np.arange(), np.zeros(), np.ones(), np.linspace(), and np.random.rand().
• These functions can be used to create NumPy arrays with specific shapes, ranges of values, and data types.
• NumPy functions are powerful tools for data analysis and scientific computing, and they are essential for many applications in fields such as physics, engineering, and data science.

FAQs Related to Numpy Function

Q1 – What is the difference between a view and a copy in NumPy?
Ans – A view is a shallow copy of an array that shares the same memory as the original array. Changes made to a view will also affect the original array. A copy, on the other hand, is a deep copy of an array that creates a new array with its own memory. Changes made to a copy will not affect the original array.

Q2 – What is the difference between a NumPy array and a Python list?
Ans – A NumPy array is a homogeneous multidimensional container of elements of the same data type. It is much faster and more memory efficient than a Python list for large data sets. A Python list can contain elements of different data types and is generally slower and less memory efficient than a NumPy array.

Q3 – Can NumPy be used with other Python libraries like Pandas and Matplotlib?
Ans – Yes, NumPy can be used in conjunction with other Python libraries like Pandas and Matplotlib. Pandas use NumPy arrays for their data structures, and Matplotlib uses NumPy arrays for plotting data.

Q4 – Can I use NumPy with non-numeric data types like strings?
Ans – NumPy is primarily designed for numerical data, but it can be used with other data types like strings. However, string operations in NumPy are generally slower and less memory efficient than numerical operations, so it is not recommended to use NumPy with large string datasets.

Q5 – What is broadcasting in NumPy?
Ans – NumPy broadcasting allows you to perform operations on arrays of different shapes and sizes. When performing an operation between two arrays with different shapes, NumPy will automatically broadcast the smaller array to match the shape of the larger array.