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Jul 09, 2021 · Approach : Import **numpy** package. Initialize the nested **list** and then use **numpy**.**array** () function to convert the **list** to an **array** and store it in a different object. Display both **list** and **NumPy** **array** and observe the difference. Below is the implementation.. We will use the **NumPy** module instead of the **array** module to create a **2D array** , as the **NumPy** module provides high-performance **multidimensional arrays** and different tools to work with. The above code we can use to create empty **NumPy** **array** without shape in **Python**.. Read **Python** **NumPy** nan. **Python** **numpy** empty **2d** **array**. In this section, we will discuss **Python** **numpy** empty **2d** **array**.; **To** create an empty 2Dimensional **array** we can pass the shape of the **2D** **array** ( i.e is row and column) as a tuple to the empty() function. Because of these benefits, **NumPy** is the de facto standard for **multidimensional arrays** in **Python** data science, and many of the most popular libraries are built on top of it. Learning **NumPy** is a great way to set down a solid foundation as you expand your knowledge into more specific areas of data science. You can convert your **list** **of** **lists** **to** a **NumPy** **array** the same way as above, by calling the **array**() function. # two dimensional example from **numpy** import **array** # **list** **of** data data = [[11, 22], [33, 44], [55, 66]] # **array** **of** data data = array(data) print(data) print(type(data)) 1 2 3 4 5 6 7 8 9 10 # two dimensional example. **Numpy** sum 3d **array Python NumPy Array** Object Exercises, Practice and Solution: Write a **NumPy** program to calculate the sum of all columns of a **2D NumPy array** To get Stream of **array** elements, we can use **Arrays** We use cookies to ensure you have the best browsing experience on our website Here is an implementation of the function in **Python**: then. A **NumPy array **is different from a **Python list**. The data types stored in a **Python list **can all be different. **python**_**list **= [ 1, -0.038, 'gear', True] The **Python list **above contains four different data types: 1 is an integer, -0.038 is a float, 'gear' is a string, and 'True' is a boolean.. Aug 19, 2022 · Previous: Write a **NumPy** program to calculate average values of two given **numpy** **arrays**. Next: Write a **NumPy** program to find the k smallest values of a given **numpy** **array**. What is the difficulty level of this exercise?. The **list** [0] * c is constructed r times as a new **list**, and. **Array** indexing is the same as accessing an **array** element. You can access an **array** element by referring to its index number. The indexes in **NumPy** **arrays** start with 0, meaning that the first element has index 0, and the second has index 1 etc. Example Get the first element from the .... If you want to use **Numpy**.NET you have two options: **Numpy**.dll Just reference **Numpy**.dll via Nuget, set your build configuration to x64 and you are good to go. Thanks to **Python**.Included it doesn't require a local **Python** installation or will not clash with existing installations. **Numpy**.Bare.dll.

We can use the **numpy**.array()function to create a **numpy** **array** from a **python** **list**. The array()function takes a **list** as its input argument and returns a **numpy** **array**. In this case, the data type of **array** elements is the same as the data type of the elements in the **list**. myList=[1,2,3,4,5] print("The **list** is:") print(myList) myArr = np.array(myList). **Array** count **python**: We use the count_nonzero () function to count occurrences of a value in a **NumPy** **array**, which returns the count of values in a given **numpy** **array**. If the value of the axis argument is None, then it returns the count. Let's take an example, count all occurrences of value '6' in an **array**,. **Python** **Numpy** **Array** Tutorial. A **NumPy** tutorial for beginners in which you'll learn how to create a **NumPy** **array**, use broadcasting, access values, manipulate **arrays**, and much more. **NumPy** is, just like SciPy, Scikit-Learn, Pandas, etc. one of the packages that you just can't miss when you're learning data science, mainly because this library.

Example 1: Mean **of **all the elements in a **NumPy Array**. In this example, we take a **2D NumPy Array **and compute the mean **of **the **Array**. **Python **Program. import **numpy **as np #initialize **array **A = np.**array **( [ [2, 1], [5, 4]]) #compute mean output = np.mean. stickley furniture retail price **list **yamaha xsr 155 spare parts. . Example: Use + Operator to Add Elements of Two **Lists**. This method uses **NumPy** module of **Python** . **Numpy arrays** are given as input and the addition of elements is done using + operator. In order to print the result as **Python List**, use to_**list**() function. The drawback of this method is that it takes **lists** of equal lengths but it is a fast and also. Slicing: Similar to **Python** **lists**, **numpy** **arrays** can be sliced. Since **arrays** may be multidimensional, you must specify a slice for each dimension of the **array**: ... import **numpy** as np from scipy.spatial.distance import pdist, squareform # Create the following **array** where each row is a point in **2D** space: # [[0 1] # [1 0] # [2 0]]. Create **Array** from **List** Here, we declared an integer **list** **of** numbers from 1 to 5. Next, we used the **Python** **numpy** **array** function available in the module to convert that **list**. We also created a new one of mixed items. a = [1, 2, 3, 4, 5] arr = np.**array** (a) print (arr) b = [2.5, 3.5, 7, 4.5, 8] arr2 = np.**array** (b) print (arr2). # Use this 2-Dimensional **Array** for this exercises arr_2_d = np.arange (1,26).reshape (5,5) print (arr_2_d) [ [ 1 2 3 4 5] [ 6 7 8 9 10] [11 12 13 14 15] [16 17 18 19 20] [21 22 23 24 25]] 1.

Here, we created a **2D** **array** and then calculated its sum. You can see that we get the sum of all the elements in the above **2D** **array** with the same syntax. This can be extended to higher-dimensional **numpy** **arrays** as well. Sum of every row in a **2D** **array**. **To** get the sum of each row in a **2D** **numpy** **array**, pass axis=1 to the sum() function. This argument. Because of these benefits, **NumPy** is the de facto standard for **multidimensional arrays** in **Python** data science, and many of the most popular libraries are built on top of it. Learning **NumPy** is a great way to set down a solid foundation as you expand your knowledge into more specific areas of data science. You can use avg_monthly_precip[2] to select the third element in (1.85) from this one-dimensional **numpy** **array**.. Recall that you are using use the index [2] for the third place because **Python** indexing begins with [0], not with [1].. Indexing on Two-dimensional **Numpy** **Arrays**. For two-dimensional **numpy** **arrays**, you need to specify both a row index and a column index for the element (or range of. From **Python Nested Lists to Multidimensional numpy Arrays** Posted on October 08, 2020 by Jacky Tea From **Python Nested Lists to Multidimensional numpy Arrays**. ... Example 2: add **numpy arrays** u and v to form a new **numpy array** z. Where the term “z:**array**([1,1])” means the variable z contains an **array**. The actual vector operation is shown in. Matrix Multiplication in **Python**. The **Numpy** matmul () function is used to return the matrix product of 2 **arrays**. Here is how it works. 1) **2-D** **arrays**, it returns normal product. 2) Dimensions > 2, the product is treated as a stack of matrix. 3) 1-D **array** is first promoted to a matrix, and then the product is calculated. Intersection between two 2d numpy arrays If the input arrays are not 1d, they’ll be flattened and then the intersection will be computed. # create two 2d arrays ar1 = np.array( [ [1, 1], [2, 3]]) ar2 = np.array( [ [4, 5], [3, 1]]) # intersection b/w the two arrays common_elements = np.intersect1d(ar1, ar2) # display the intersection array. Add two **numpy** **arrays** You can use the **numpy** np.add () function to get the elementwise sum of two **numpy** **arrays**. The + operator can also be used as a shorthand for applying np.add () on **numpy** **arrays**. The following is the syntax: import **numpy** as np # x1 and x2 are **numpy** **arrays** **of** same dimensions # using np.add () x3 = np.add(x1, x2) # using + operator.

From **Python Nested Lists to Multidimensional numpy Arrays** Posted on October 08, 2020 by Jacky Tea From **Python Nested Lists to Multidimensional numpy Arrays**. ... Example 2: add **numpy arrays** u and v to form a new **numpy array** z. Where the term “z:**array**([1,1])” means the variable z contains an **array**. The actual vector operation is shown in. **Numpy** performs logical and mathematical operations of **arrays**. In **python**, **numpy** is faster than the **list**.. For a **2D** **array**, the former will store the **array** row by row in a long line, while the latter stores the data column by column. When accessing the element on the ith row and jth column in.. Converting a **2D Numpy array to list of lists** using iteration : We can iterate a **2D array** row by row and during iteration we can add it to the **list**. And at the end we can get the **list** of **lists** containing all the elements from **2D numpy array**. #Program : import **numpy** as np. arr = np.**array**( [ [11, 22, 33, 44],. The **numpy.array** () function inside the **NumPy** package is used to create an **array** in **Python**. We pass a sequence of elements enclosed in a pair of square brackets to the **numpy.array** () function, and it returns an **array** containing the exact sequence of elements. The **array** **of** **arrays**, or known as the multidimensional **array**, can be created by passing.

The **NumPy** size () function has two arguments. First is an **array**, required an argument need to give **array** or **array** name. Second is an axis, default an argument. The axis contains none value, according to the requirement you can change it. The np.size () function count items from a given **array** and give output in the form of a number as size. Slice **2D** **Array** With the **numpy**.ix_ () Function in **NumPy** The **numpy**.ix_ () function forms an open mesh form sequence of elements in **Python**. This function takes n 1D **arrays** and returns an nD **array**. We can use this function to extract individual 1D slices from our main **array** and then combine them to form a **2D** **array**.

In general, we know that **python** has many libraries like matplotlib, **Numpy**, etc. **Numpy** is one of the efficient and powerful libraries. nditer() is an efficient multi-dimensional iterator object to iterate over an **array**. Iterating means going through elements one by one. **Numpy** contains a function nditer() that can be used for very basic iterations to advanced iterations. From **Python Nested Lists to Multidimensional numpy Arrays** Posted on October 08, 2020 by Jacky Tea From **Python Nested Lists to Multidimensional numpy Arrays**. ... Example 2: add **numpy arrays** u and v to form a new **numpy array** z. Where the term “z:**array**([1,1])” means the variable z contains an **array**. The actual vector operation is shown in. Line 1: We import the CSV and **numpy** libraries. Lines 3-5: We open the sampleCSV file and then read each CSV file’s data using the CSV.reader () method and convert the results into a **list** of **lists**. Line 6: Now, we use the **numpy**.**array** method. How to Sort the **NumPy** **array** by Row in **Python**? Sort **2d** **array** **python**: By using similar logic, we can also sort a **2D** **Numpy** **array** by a single row i.e. mix-up the columns of the **2D** **numpy** **array** **to** get the furnished row sorted. Look at the below examples and learn how it works easily, Let's assume, we have a **2D** **Numpy** **array** i.e. Returns a new **array** with the specified shape. 2: append. Appends the values to the end of an **array**. 3: insert. Inserts the values along the given axis before the given indices. 4: delete. Returns a new **array** with sub-**arrays** along an axis deleted. 5: unique. Finds the unique elements of an **array**. Intersection between two 2d numpy arrays If the input arrays are not 1d, they’ll be flattened and then the intersection will be computed. # create two 2d** arrays ar1 = np.array( [ [1, 1], [2, 3]]) ar2 = np.array( [ [4, 5], [3, 1]])** # **intersection b/w** the two arrays** common_elements = np.intersect1d(ar1, ar2)** # display the intersection array. And at the end we can get the **list** of **lists** containing all the elements from **2D** **numpy** **array**. #Program : import **numpy** as np # **2D** **Numpy** **array** created arr = np.**array**([[11, 22, 33, 44], [55, 66, 77, 88], [12, 13, 23, 43]]) #printing the **2D** **array** print(arr) # Converting a **2D** **Numpy Array to list of** **lists** #iterating row by row using for loop **list**_of .... Creating **Python** **Arrays**. **To** create an **array** **of** numeric values, we need to import the **array** module. For example: Output. Here, we created an **array** **of** float type. The letter d is a type code. This determines the type of the **array** during creation. Commonly used type codes are listed as follows: Code.

# importing **numpy** import **numpy** as np # we will create a **2d** **array** # of shape 4x3 arr1 = np.**array** ( [ (1, 2, 3), (4, 5, 6), (7, 8, 9), (50, 51, 52)]) # printing the **array** print ( "the **array** is: " ) print (arr1) # now we will call delete () function # to delete the subarray present in **array** # this indicates this will delete 3rd column # of the **array**. Let's see a first example of how to use **NumPy** arange (): >>> >>> np.arange(start=1, stop=10, step=3) **array** ( [1, 4, 7]) In this example, start is 1. Therefore, the first element of the obtained **array** is 1. step is 3, which is why your second value is 1+3, that is 4, while the third value in the **array** is 4+3, which equals 7. You can use the np alias to create ndarray of a **list** using the **array** () method. li = [1,2,3,4] numpyArr = np.**array** (li) or numpyArr = np.**array** ( [1,2,3,4]) The **list** is passed to the **array** () method which then returns a **NumPy** **array** with the same elements. Example: The following example shows how to initialize a **NumPy** **array** from a **list**. Python3. Convert a Tensor **to **a **NumPy Array **With the Tensor.eval () Function in **Python**. import **numpy **a = **numpy**.**array **( [1, 2, 3, 4, 5]) print (" **Array to list **= ", a.tolist ()) The output will be as follows: In this code, we simply called the tolist () method which converts the **array to **a **list**. Then we print the newly created **list to **the output screen.. The homogeneous multidimensional **array** is the main object of **NumPy**. It is basically a table of elements which are all of the same type and indexed by a tuple of positive integers. The dimensions are called axis in **NumPy**. The **NumPy's** **array** class is known as ndarray or alias **array**. The **numpy.array** is not the same as the standard **Python** library. In general, we know that **python** has many libraries like matplotlib, **Numpy**, etc. **Numpy** is one of the efficient and powerful libraries. nditer() is an efficient multi-dimensional iterator object to iterate over an **array**. Iterating means going through elements one by one. **Numpy** contains a function nditer() that can be used for very basic iterations to advanced iterations.

Here, two one-dimensional **NumPy** **arrays** have been created by using the rand () function. These **arrays** have been used in the where () function with the multiple conditions to create the new **array** based on the conditions. The condition will return True when the first **array's** value is less than 40 and the value of the second **array** is greater than 60. Here we are creating a **Numpy array** using the np.**array** and printing the **array** before the conversion and after the conversion using **Python** typecasting to **list** using **list** function. Python3. import **numpy** as np. arr = np.**array** ( [1, 2, 4, 5]). Convert the following 1-D **array** with 12 elements into a **2-D array**. The outermost dimension will have 4. **Arrays** in **Python** - HackerRank Solution . Problem : The **NumPy** (Numeric **Python**) package helps us manipulate large **arrays** and matrices of numeric data. ... is used to convert a **list** into a **NumPy** **array**. The second argument (float) can be used to set the type of **array** elements. Task : You are given a space separated **list** **of** numbers. cmp specifies a custom comparison function of two arguments (**list** items) which should return a negative, zero or positive number depending on whether the first argument is considered smaller than, equal **to**, or larger than the second argument: cmp=lambda x,y: cmp (x.lower (), y.lower ()). The default value is None.

Exercises: 1) Create an arbitrary one dimensional **array** called "v". 2) Create a new **array** which consists of the odd indices of previously created **array** "v". 3) Create a new **array** in backwards ordering from v. 5) Create a two dimensional **array** called "m". The homogeneous multidimensional **array** is the main object of **NumPy**. It is basically a table of elements which are all of the same type and indexed by a tuple of positive integers. The dimensions are called axis in **NumPy**. The **NumPy's** **array** class is known as ndarray or alias **array**. The **numpy.array** is not the same as the standard **Python** library.

import **numpy** as np # import **python** standard library module - random. import random # Function to populate a matrix with random data. def FillMatrix(matrix_in): ... the two dimensional **arrays** created using ndarray objects. Create **Numpy Array** From **Python List** Let's define a **list** and then turn that **list** into the **NumPy array** . app_**list** = [ 18, 0, 21, 30, 46 ] np_app_**list** = np. **array** (app_**list**) np_app_**list** First, we have defined a **List** and then turn that **list** into the **NumPy array** using the np. **array** function. See the output below. Slice **2D** **Array** With the **numpy**.ix_ () Function in **NumPy** The **numpy**.ix_ () function forms an open mesh form sequence of elements in **Python**. This function takes n 1D **arrays** and returns an nD **array**. We can use this function to extract individual 1D slices from our main **array** and then combine them to form a **2D** **array**.

Other than creating Boolean **arrays** by writing the elements one by one and converting them into a **NumPy array**, we can also convert an **array** into a 'Boolean' **array** in some easy ways, that we will look at here in this post But like **Numpy**, the behind the scenes things are complex This is easy to use, and simple is working Tuple of bytes to step.**Python** convolve - 30 примеров.

Slicing **arrays**. Slicing in **python** means taking elements from one given index to another given index. We pass slice instead of index like this: [ start: end]. We can also define the step, like this: [ start: end: step]. If we don't pass start its considered 0. If we don't pass end its considered length of **array** in that dimension. So let's import **numpy** as np and import math. Right, **arrays** are displayed as a **list** or **lists** **of** **lists** and can be created through **lists** as well. When creating an **array**, we pass into it a **list** as an argument in a **numpy** **array**. So, a equals np.**array** and I'm just going to create a **list** here, 1, 2, 3 and we'll print out what it looks like. We can use the **numpy**.array()function to create a **numpy** **array** from a **python** **list**. The array()function takes a **list** as its input argument and returns a **numpy** **array**. In this case, the data type of **array** elements is the same as the data type of the elements in the **list**. myList=[1,2,3,4,5] print("The **list** is:") print(myList) myArr = np.array(myList). **To** create **NumPy** **2D** **array** use **array** () function and give one argument of items of **lists** **of** the **list** **to** it. Syntax: **array** (object, dtype=None, copy=True, order='K', subok=False, ndmin=0) 1 2 3 import **numpy** as np arr_2D = np.**array** ( [ [0, 1, 1], [1, 0, 1], [1, 1, 0]]) print(arr_2D) 1 2 3 4 5 Output >>> [ [0 1 1] [1 0 1] [1 1 0]].

Here are two methods for updating values in the **2-D** **array** (**list**). You can update rows by using the following syntax **array** [row_index]= [values] You can update column values inside rows by using the following syntax **array** [row_index] [column_index]= [values] Example:. Example 1: Resizing a Single Dimension **Numpy Array**. Let’s create a sample 1D **Numpy array** and resize it using the resize() method. **array**_1d= np.**array**([1,2,3,4,5,6,7]) Suppose I want to change the dimension of the above **array** to 3 rows and 2 columns. Then I will pass (3,2) as an argument of the resize() method. np.resize(**array**_1d,(3,2)) Output. We will use the **NumPy** module instead of the **array** module to create a **2D** **array** , as the **NumPy** module provides high-performance multidimensional **arrays** and different tools to work with these **arrays** . ... How to write **to 2d** **arrays** in **python** . The **2D** **array** is nothing but an **array** of several **arrays** . To access the elements in a **2D** **array** , we use 2.. A two-dimensional **array** is an **array** **of** (references **to**) one-dimensional **arrays**. Whereas the elements of a one-dimensional **array** are indexed by a single integer, the elements of a two-dimensional **array** are indexed by a pair of integers: the first specifying a row, and the second specifying a column. ... **Python's** **numpy** module. **Python's** built-in. Operations on **Python** **NumPy** **Arrays**. In this section, we will discuss the operations we can perform and functions we can use on the **numpy** **arrays**. 1. Checking Data type: As said before, the **numpy** **arrays** hold data of the same type. We do not have to explicitly specify the data type, **NumPy** decides it. The data type could be any of the following:. Basically, **2D array** means the **array** with 2 axes, and the **array**’s length can be varied. **Arrays** play a major role in data science, where speed matters. **Numpy** is an acronym for numerical. The comments and answers pointing to **numpy**.array(A) are all correct, with one caveat: the inner elements (whether they're tuples, **lists**, or np.**arrays** themselves) must have the same length. If they don't, you'll still get A.shape = (3,) and A will have dtype=object.

. **To** create a **NumPy** **array**, you only need to specify the items (enclosed in square brackets, of course): array_2 = np.**array** ( ["numbers", 3, 6, 9, 12]) print (array_2) print(type(array_2)) ['numbers' '3' '6' '9' '12'] <class **'numpy**.ndarray'> As you can see, array_2 contains one item of the string type (i.e., "numbers") and four integers. import pandas as pd import **numpy** **numpyArray** = **numpy**.array([[51, 61, 91], [121, 118, 127]]) df = pd.DataFrame(numpyArray, index=['row 1', 'row 2'], columns=['col 1', 'col 2', 'col 3']) df = df.to_json(orient='index') print("Json Encoded **Array**") print(df) Run Output:. The **list** [0] * c is constructed r times as a new **list**, and. **Array** indexing is the same as accessing an **array** element. You can access an **array** element by referring to its index number. The indexes in **NumPy** **arrays** start with 0, meaning that the first element has index 0, and the second has index 1 etc. Example Get the first element from the .... Converting a **2D Numpy array** to **list** of **lists** using iteration : We can iterate a **2D array** row by row and during iteration we can add it to the **list**. And at the end we can get the. May 25, 2020 · arr: the arr parameter is the **array** you want to transpose. The type of this parameter is array_like. axes: By default the value is None.When None or no value is passed it will reverse the dimensions of **array** arr. The axes parameter takes a **list** **of** integers as the value to permute the given **array** arr. **numpy**.transpose() on 1-D **array**.

I use an external module , which does not support **numpy** **arrays**, only tuples, **lists** and dicts. But my data is in a **2d** **numpy** **array**. But my data is in a **2d** **numpy** **array**. How can I convert it the pythonic way, aka without loops. **NumPy** **Array** Object [205 exercises with solution] [ An editor is available at the bottom of the page to write and execute the scripts.] 1. Write a **NumPy** program to print the **NumPy** version in your system. Go to the editor. 2. Write a **NumPy** program to convert a **list** **of** numeric value into a one-dimensional **NumPy** **array**. Add two **numpy** **arrays** You can use the **numpy** np.add () function to get the elementwise sum of two **numpy** **arrays**. The + operator can also be used as a shorthand for applying np.add () on **numpy** **arrays**. The following is the syntax: import **numpy** as np # x1 and x2 are **numpy** **arrays** **of** same dimensions # using np.add () x3 = np.add(x1, x2) # using + operator. Improve Performance of Comparing two **Numpy** **Arrays**. I had a code challenge for a class I'm taking that built a NN algorithm. I got it to work but I used really basic methods for solving it. There are two 1D NP **Arrays** that have values 0-2 in them, both equal length. They represent two different trains and test data The output is a confusion. **Python** **Numpy** **Array** Tutorial. A **NumPy** tutorial for beginners in which you'll learn how to create a **NumPy** **array**, use broadcasting, access values, manipulate **arrays**, and much more. **NumPy** is, just like SciPy, Scikit-Learn, Pandas, etc. one of the packages that you just can't miss when you're learning data science, mainly because this library. The most common way to slice a **NumPy** **array** is by using the : operator with the following syntax: **array** [start:end] **array** [start:end:step] The start parameter represents the starting index, end is the ending index, and step is the number of items that are "stepped" over. **NumPy** is a free **Python** package that offers, among other things, n.

import **numpy** as np A = [[1,2,3],[4,5,6],[7,8,9]] A = np.**array**(A) If A is a **list** of **numpy array**, how about this: Ah = np.vstack(A) Av = np.hstack(A) Solution 2. If I understood. For many languages such as Java and C++, **arrays** and **lists** are different objects. C++ doesn't even technically have "**lists**" and Java has a hybrid object called an ArrayList.While there are **arrays** in **Python**, such as **numpy** **arrays**, **Python's** most common sequence or series collection is a **list** object.. **Python** **list** objects may contain entries of any type from numbers to strings to. Dimensions and **Arrays** in **NumPy**. A dimension is a value that defines the number of indexes you need to specify to select an **array** element. Note: the code in the last example demonstrated a multidimensional **array**. The first **array** (array1) was a one-dimensional **array** (1D). The second **array** (array2) was a two-dimensional **array** (**2D**). Using **NumPy**, we can perform concatenation of multiple **2D** **arrays** in various ways and methods. Method 1: Using concatenate () function We can perform the concatenation operation using the concatenate() function. With this function, **arrays** are concatenated either row-wise or column-wise, given that they have equal rows or columns respectively. You can use the np alias to create ndarray of a **list** using the **array** () method. li = [1,2,3,4] numpyArr = np.**array** (li) or numpyArr = np.**array** ( [1,2,3,4]) The **list** is passed to the **array** () method which then returns a **NumPy** **array** with the same elements. Example: The following example shows how to initialize a **NumPy** **array** from a **list**. Python3. Exercises: 1) Create an arbitrary one dimensional **array** called "v". 2) Create a new **array** which consists of the odd indices of previously created **array** "v". 3) Create a new **array** in backwards ordering from v. 5) Create a two dimensional **array** called "m". For working with **numpy** we need to first import it into **python** code base. import **numpy** as np Creating an **Array** Syntax - arr = np.array([2,4,6], dtype='int32') print(arr) [2 4 6] In above code we used dtype parameter to specify the datatype To create a **2D** **array** and syntax for the same is given below - arr = np.array([[1,2,3],[4,5,6]]) print(arr). Intersection between two 2d numpy arrays If the input arrays are not 1d, they’ll be flattened and then the intersection will be computed. # create two 2d** arrays ar1 = np.array( [ [1, 1], [2, 3]]) ar2 = np.array( [ [4, 5], [3, 1]])** # **intersection b/w** the two arrays** common_elements = np.intersect1d(ar1, ar2)** # display the intersection array.