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Updated 3 months ago

Assignment 2 - Numpy Array OperationsThis assignment is part of the course "Data Analysis with Python: Zero to Pandas". The objective of this assignment is to develop a solid understanding of Numpy array operations. In this assignment you will:

- Pick 5 interesting Numpy array functions by going through the documentation: https://numpy.org/doc/stable/reference/routines.html
- Run and modify this Jupyter notebook to illustrate their usage (some explanation and 3 examples for each function). Use your imagination to come up with interesting and unique examples.
- Upload this notebook to your Jovian profile using
`jovian.commit`

and make a submission here: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-2-numpy-array-operations- (Optional) Share your notebook online (on Twitter, LinkedIn, Facebook) and on the community forum thread: https://jovian.ml/forum/t/assignment-2-numpy-array-operations-share-your-work/10575 .
- (Optional) Check out the notebooks shared by other participants and give feedback & appreciation.
The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

Try to give your notebook a catchy title & subtitle e.g. "All about Numpy array operations", "5 Numpy functions you didn't know you needed", "A beginner's guide to broadcasting in Numpy", "Interesting ways to create Numpy arrays", "Trigonometic functions in Numpy", "How to use Python for Linear Algebra" etc.

NOTE: Remove this block of explanation text before submitting or sharing your notebook online - to make it more presentable.

Write a short introduction about Numpy and list the chosen functions.

- function 1
- function 2
- function 3
- function 4
- function 5

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

`!pip install jovian --upgrade -q`

`import jovian`

`jovian.commit(project='numpy-array-operations')`

```
[jovian] Detected Colab notebook...
[jovian] Please enter your API key ( from https://jovian.ai/ ):
API KEY: ··········
[jovian] Uploading colab notebook to Jovian...
Committed successfully! https://jovian.ai/abhyu420/numpy-array-operations
```

Let's begin by importing Numpy and listing out the functions covered in this notebook.

`import numpy as np`

function1 = np.stack() function2 = np.dot() function3 = np.rint() function4 = np.all() function5 = np.logical_and()

Add some explanation about the function in your own words

```
# Example 1 - working (change this)
arr1 = [2,34,5]
arr2 = [5,7,9]
np.stack((arr1,arr2))
```

```
array([[ 2, 34, 5],
[ 5, 7, 9]])
```

Explanation about example

```
# Example 2 - working
arr1 = [[2,3,5],[4,6,8]]
arr2 = [[4,5,6],[5,8,0]]
np.stack((arr1,arr2))
```

```
array([[[2, 3, 5],
[4, 6, 8]],
[[4, 5, 6],
[5, 8, 0]]])
```

Explanation about example

```
# Example 3 - breaking (to illustrate when it breaks)
arr1 = [[1, 2],
[3, 4.]]
arr2 = [[5, 6, 7],
[8, 9, 10]]
np.stack((arr1, arr2), axis=1)
```

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-1d5459faacf8> in <module>()
6 [8, 9, 10]]
7
----> 8 np.stack((arr1, arr2), axis=1)
<__array_function__ internals> in stack(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/numpy/core/shape_base.py in stack(arrays, axis, out)
425 shapes = {arr.shape for arr in arrays}
426 if len(shapes) != 1:
--> 427 raise ValueError('all input arrays must have the same shape')
428
429 result_ndim = arrays[0].ndim + 1
ValueError: all input arrays must have the same shape
```

Explanation about example (why it breaks and how to fix it)

Some closing comments about when to use this function.

`jovian.commit()`

```
[jovian] Detected Colab notebook...
[jovian] Uploading colab notebook to Jovian...
Committed successfully! https://jovian.ai/abhyu420/numpy-array-operations
```

Add some explanations

```
# Example 1 - working
arr1 = [2,4,5]
arr2 = [3,5,7]
np.dot(arr1,arr2)
```

`61`

Explanation about example

```
# Example 2 - working
arr1 = [4,6,8]
arr2 = [9,8,7]
np.dot(arr2,arr1)
```

`140`

Explanation about example

```
# Example 3 - breaking (to illustrate when it breaks)
arr1 = [9,6,4]
arr2 = [7.9,8]
np.dot(arr1,arr2)
```

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-b25f116cdd7a> in <module>()
3 arr2 = [7.9,8]
4
----> 5 np.dot(arr1,arr2)
<__array_function__ internals> in dot(*args, **kwargs)
ValueError: shapes (3,) and (2,) not aligned: 3 (dim 0) != 2 (dim 0)
```

Explanation about example (why it breaks and how to fix it)

Some closing comments about when to use this function.

`jovian.commit()`

```
[jovian] Detected Colab notebook...
[jovian] Uploading colab notebook to Jovian...
Committed successfully! https://jovian.ai/abhyu420/numpy-array-operations
```

Add some explanations

```
# Example 1 - working
arr1 = [[1.785, 2.35],
[3.62, 4.54]]
print("rounded numbers are ", np.rint(arr1))
```

```
rounded numbers are [[2. 2.]
[4. 5.]]
```

Explanation about example

```
# Example 2 - working
arr1 = [[1.785, 2.5],
[3.6256, 4.64]]
print("rounded numbers are ", np.rint(arr1))
```

```
rounded numbers are [[2. 2.]
[4. 5.]]
```

Explanation about example

```
# Example 3 - breaking (to illustrate when it breaks)
arr2 = [[1, "abc"],
[3, "ch"]]
print("rounded numbers", np.rint(arr2))
```

Explanation about example (why it breaks and how to fix it)

Some closing comments about when to use this function.

`jovian.commit()`

*italicized text*## Function 4 - ???

Add some explanations

```
# Example 1 - working
arr1 =([[-1,2,3],[5,0,2]])
print(np.all(arr1))
```

Explanation about example

```
# Example 2 - working
print(np.all(arr1,axis=0))
```

Explanation about example

```
# Example 3 - breaking (to illustrate when it breaks)
arr2=[1,"nan",2]
print(np.all(arr2))
```

Explanation about example (why it breaks and how to fix it)

Some closing comments about when to use this function.

`jovian.commit()`

Add some explanations

```
# Example 1 - working
arr1=[False,0,1,True]
arr2=[False,5,6,True]
output=np.logical_and(arr1,arr2)
print(output)
```

Explanation about example

```
# Example 2 - working
arr3=[False,0,5,True]
arr4=[False,7,9,True]
print(np.logical_and(arr1,arr2))
```

Explanation about example

```
# Example 3 - breaking (to illustrate when it breaks)
arr1=[False,0,1,True]
arr2=[False,5,6,True,45]
print(np.logical_and(arr1,arr2))
```

Explanation about example (why it breaks and how to fix it)

Some closing comments about when to use this function.

`jovian.commit()`

Some closing comments about when to use this function.

`jovian.commit()`

Summarize what was covered in this notebook, and where to go next

Provide links to your references and other interesting articles about Numpy arrays:

- Numpy official tutorial : https://numpy.org/doc/stable/user/quickstart.html
- ...

`jovian.commit()`

` `