# NumPy Exercises, Practice, Solution

## NumPy

NumPy is a Python package providing fast, flexible, and expressive data structures designed to make working with 'relationa' or 'labeled' data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

The best way we learn anything is by practice and exercise questions. Here you have the opportunity to practice the NumPy concepts by solving the exercises starting from basic to more complex exercises. A sample solution is provided for each exercise. It is recommended to do these exercises by yourself first before checking the solution.

Hope, these exercises help you to improve your NumPy coding skills. Currently, following sections are available, we are working hard to add more exercises .... Happy Coding!

NumPy Basics

Operator Description
np.array([1,2,3]) 1d array
np.array([(1,2,3),(4,5,6)]) 2d array
np.arange(start,stop,step) range array

Placeholders

Operator Description
np.linspace(0,2,9) Add evenly spaced values btw interval to array of length
np.zeros((1,2)) Create and array filled with zeros
np.ones((1,2)) Creates an array filled with ones
np.random.random((5,5)) Creates random array
np.empty((2,2)) Creates an empty array

Array

Syntax Description
array.shape Dimensions (Rows,Columns)
len(array) Length of Array
array.ndim Number of Array Dimensions
array.dtype Data Type
array.astype(type) Converts to Data Type
type(array) Type of Array

Copying/Sorting

Operators Description
np.copy(array) Creates copy of array
other = array.copy() Creates deep copy of array
array.sort() Sorts an array
array.sort(axis=0) Sorts axis of array

Array Manipulation

Operator Description
np.append(a,b) Append items to array
np.insert(array, 1, 2, axis) Insert items into array at axis 0 or 1
np.resize((2,4)) Resize array to shape(2,4)
np.delete(array,1,axis) Deletes items from array

Combining Arrays

Operator Description
np.concatenate((a,b),axis=0) Concatenates 2 arrays, adds to end
np.vstack((a,b)) Stack array row-wise
np.hstack((a,b)) Stack array column wise

Splitting Arrays

Operator Description
numpy.split() Split an array into multiple sub-arrays.
np.array_split(array, 3) Split an array in sub-arrays of (nearly) identical size
numpy.hsplit(array, 3) Split the array horizontally at 3rd index

More

Operator Description
other = ndarray.flatten() Flattens a 2d array to 1d
array = np.transpose(other)
array.T
Transpose array
inverse = np.linalg.inv(matrix) Inverse of a given matrix

Mathematics

Operations

Operator Description
x + y
np.substract(x,y)
x - y
Subtraction
np.divide(x,y)
x / y
Division
np.multiply(x,y)
x @ y
Multiplication
np.sqrt(x) Square Root
np.sin(x) Element-wise sine
np.cos(x) Element-wise cosine
np.log(x) Element-wise natural log
np.dot(x,y) Dot product
np.roots([1,0,-4]) Roots of a given polynomial coefficients

Comparison

Operator Description
== Equal
!= Not equal
< Smaller than
> Greater than
<= Smaller than or equal
>= Greater than or equal
np.array_equal(x,y) Array-wise comparison

Basic Statistics

Operator Description
np.mean(array) Mean
np.median(array) Median
array.corrcoef() Correlation Coefficient
np.std(array) Standard Deviation

More

Operator Description
array.sum() Array-wise sum
array.min() Array-wise minimum value
array.max(axis=0) Maximum value of specified axis
array.cumsum(axis=0) Cumulative sum of specified axis

Slicing and Subsetting

Operator Description
array[i] 1d array at index i
array[i,j] 2d array at index[i][j]
array[i<4] Boolean Indexing, see Tricks
array[0:3] Select items of index 0, 1 and 2
array[0:2,1] Select items of rows 0 and 1 at column 1
array[:1] Select items of row 0 (equals array[0:1, :])
array[1:2, :] Select items of row 1
[comment]: <> ( array[1,...]
array[ : :-1] Reverses array

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## Python: Tips of the Day

Serialization:

Sometimes you may need to save your Python object locally for later use or Network transfers. Python has fantastic libraries for serialization such as Json and Pickle.

Serialization is storing data structures in the program so they don't just disappear after the program is terminated.

There is also marshal library but it's more primitive and can't handle certain techniques such as class instances and recursion. Marshal is closer to json in its scope of serialization.

So, when would you use pickle, cpickle, json or ujson?

cpickle and ujson are faster versions of respective libraries that take advantage of C implementations in Python. So they'd always be favorable for speed reasons.

Apart from that, json is a more secure and readable version of serialization than pickle which comes at a cost of speed.

While you can take care of almost any data structure in Python with Json it gets inefficient with large files or uncommon objects. Pickle on the other hand operates in a sweet spot where you'd like to work with large files (multiple GBs) and still don't want to be bothered with database solutions.

The thing is depending on your application, you may have to watch out for security vulnerabilities pickle introduces to the system so it's usually wise to seek out json or database solutions before resorting to pickle when possible.

```import pickle
lst = ["LST", "LGA", "SFO", "BOM", "JFK", "MIA"]
x = pickle.dumps(lst)
print(x)
```

Output:

```b'\x80\x03]q\x00(X\x03\x00\x00\x00LSTq\x01X\x03\x00\x00\x00LGAq\x02X\x03\x00\x00\x00SFOq\x03X\x03\x00\x00\x00BOMq\x04X\x03\x00\x00\x00JFKq\x05X\x03\x00\x00\x00MIAq\x06e.'

```
```import pickle
lst = ["LST", "LGA", "SFO", "BOM", "JFK", "MIA"]
x = pickle.dumps(lst)
`['LST', 'LGA', 'SFO', 'BOM', 'JFK', 'MIA']`