NumPy Basics for Python Programming

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.