NumPy Basics for Python Programming

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Questions and Answers

What will be the output when slicing the array from index 1 to index 5?

[2 3 4 5] (correct)

[3 4 5 6]

[1 2 3 4]

[4 5 6 7]

If an array is sliced from index 4 to the end, which part of the array will be returned?

[4 5 6 7]

[1 2 3 4]

[6 7]

[5 6 7] (correct)

When creating a DataFrame from two Series, what structure does the resultant DataFrame take?

A single column

A two-dimensional array

A single-dimensional array

A multi-dimensional table (correct)

How would you obtain every other element from index 1 to index 5 in a NumPy array?

arr[1:5:2] Signup and view all the answers

What is the term for a single column in a Pandas DataFrame?

Series Signup and view all the answers

What happens when you slice an array using negative indexes?

Starts from the end of the array Signup and view all the answers

What command is used to install Pandas in Python?

pip install pandas Signup and view all the answers

When slicing a 2D array with the notation arr[0:2, 1:4], what part of the array do you retrieve?

A sub-array of specified rows and columns Signup and view all the answers

Which of the following correctly demonstrates how to create a Pandas DataFrame from a dictionary of cars and their corresponding passing values?

pd.DataFrame({'cars': ['BMW', 'Volvo', 'Ford'], 'passings': [3, 7, 2]}) Signup and view all the answers

What is the output of the following Pandas Series creation: `pd.Series(a, index = ['x', 'y', 'z'])` where `a = [1, 7, 2]`?

x 1 y 7 z 2 dtype: int64 Signup and view all the answers

How can you create a one-dimensional NumPy array given a list of numeric values like `[12.23, 13.32, 100, 36.32]`?

np.array([12.23, 13.32, 100, 36.32]) Signup and view all the answers

What is the result of the following operation: `myvar = pd.Series(calories, index=['day1', 'day2'])` where `calories = {'day1': 420, 'day2': 380, 'day3': 390}`?

day1 420 day2 380 dtype: int64 Signup and view all the answers

Which command correctly initializes an 8x8 matrix filled with a checkerboard pattern in NumPy?

np.indices((8,8)).sum(axis=0) % 2 Signup and view all the answers

What command is used to create a 1-D array with the values 1, 2, 3, 4, 5?

arr = np.array([1, 2, 3, 4, 5]) Signup and view all the answers

How do you correctly slice a NumPy array to obtain elements from index 1 to 3?

arr[1:4] Signup and view all the answers

Which of the following is the correct way to import NumPy using an alias?

import numpy as np Signup and view all the answers

Which command correctly creates a 3-D NumPy array?

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) Signup and view all the answers

What will be the output of accessing the element at arr[0, 1, 2] for the array defined as arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])?

6 Signup and view all the answers

What is the shape of the array created by arr = np.array([[1, 2], [3, 4], [5, 6]])?

(3, 2) Signup and view all the answers

Which function is used to create a 2-D array containing two distinct 1-D arrays?

np.array([[1, 2, 3], [4, 5, 6]]) Signup and view all the answers

Using NumPy, how can you change the first row of a 2-D array arr = np.array([[1, 2, 3], [4, 5, 6]]) to [7, 8, 9]?

arr[0] = [7, 8, 9] Signup and view all the answers

Study Notes

NumPy

NumPy is short for "Numerical Python"
It's a Python library used for working with arrays.

NumPy Installation

Install using pip:
- C:\Users\Your Name>pip install --upgrade pip
- C:\Users\Your Name>pip install numpy

Importing NumPy

Import using import numpy as np
Create an array: arr = np.array([1, 2, 3, 4, 5])

NumPy as np

NumPy is typically imported as np
Example: import numpy as np

Creating NumPy Arrays from tuples

Using tuples to create NumPy arrays:
- arr = np.array((1, 2, 3, 4, 5))

1-D Arrays

Create a 1-D array:
- arr = np.array([1, 2, 3, 4, 5])

Accessing Array Elements

Accessing elements:
- arr[0] (first element from array)
- arr[1] (second element from array)

2-D Arrays

Create a 2-D array:
- arr = np.array([[1, 2, 3], [4, 5, 6]])

Accessing 2-D Array elements

Accessing specific elements:
- arr[0, 1] (2nd element in the first row)

3-D Arrays

Create a 3-D array:
- arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

Accessing 3-D Array elements

Accessing specific elements:
- arr[0, 1, 2] (third element in the second array from the first array)

NumPy Array Slicing

Slicing extracts elements from a NumPy array
Basic slicing: arr[start:end]
- arr[1:5] slices items from index 1 up to (but not including) 5.
Slicing with step: arr[start:end:step]
- arr[1:5:2] slices elements from index 1 to 5 with a step of 2.
Slicing to end of array: arr[start:]
- arr[4:] starts at index 4 and goes to the end
Slicing from beginning: arr[:end]
- arr[:4] starts at beginning and goes up to (but not including) 4.
Different slicing approaches with 2-D arrays.
- Slicing from indexes or a range of indexes of 2-D arrays.

Pandas

Pandas is a Python library for data analysis
It's used for manipulating and analyzing data sets.

Pandas Installation

Install using pip:
- pip install --upgrade pip
- pip install pandas

Pandas DataFrames

DataFrames are multi-dimensional tabular data structures.
Series are one-dimensional arrays.
Example DataFrame creation from a dictionary: myvar = pd.DataFrame(data)

Pandas Series

A Pandas Series is like a column in a table.
It's a one-dimensional array holding data of any type.

Creating Pandas Series

Create a Series from a list: myvar = pd.Series([1, 7, 2])
Create a Series from a dictionary: myvar = pd.Series({"day1": 420, "day2": 380})

Accessing Series elements

Accessing elements: print(myvar[0])

Pandas Labels

Labels can be used to access values.
Default labels are index numbers.
User defined labels are possible: myvar = pd.Series([1, 7, 2], index=["x", "y", "z"])
Key value pairs can also be used to create a Series

NumPy to One-Dimensional array

Convert a list to a one-dimensional NumPy array: np.array([12.23, 13.32, 100, 36.32])

NumPy to Array Range

Create a NumPy array with values from 12 to 38: np.arange(12, 39)

NumPy Checkerboard Pattern

Create an 8x8 matrix with checkerboard pattern using NumPy.

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Description

This quiz covers the essentials of NumPy, a powerful library for numerical operations in Python. Learn how to install NumPy, create arrays, and access their elements in both 1-D and 2-D formats.