Now, how these libraries work and why they’re so great. First up we have NumPy, which stands for Numerical Python. This library helps us do math stuff with arrays (which is basically just a fancy way of saying “lists that can hold numbers”). For example:

# Import the NumPy library and give it an alias 'np' so we don't have to type out 'numpy.' every time
import numpy as np 

# Create a new array called x with values of 1, 2, and 3
x = np.array([1, 2, 3]) 

# Create another array called y with values of 4, 5, and 6
y = np.array([4, 5, 6]) 

# Add the two arrays together using NumPy's '+' operator (which is faster than doing it manually)
z = x + y 

# Print out the result
print(z)

So basically what we did here was import the NumPy library, create two arrays called `x` and `y`, added them together using NumPy’s `+` operator, and then printed out the resulting array. This is just one example of how NumPy can make data analysis easier and faster in Python!
Basically, this library helps us work with tables of data (which are called “DataFrames” in pandas-speak) by providing functions to manipulate and analyze them. For example:

# Import the Pandas library and give it an alias 'pd' so we don't have to type out 'pandas.' every time
import pandas as pd 

# Read in a CSV file called "my-data.csv" using pandas' `read_csv()` function (which is faster than doing it manually)
data = pd.read_csv('my-data.csv') 

# Print out the first five rows of our dataframe using Pandas' `head()` method (which is also faster than doing it manually)
print(data.head())

So basically what we did here was import the pandas library, read in a CSV file called “my-data.csv” using pandas’ `read_csv()` function, and then printed out the first five rows of our dataframe using Pandas’ `head()` method. This is just one example of how pandas can make working with tables of data easier and faster in Python!
And that’s just two examples of the many libraries available for data analysis in Python. There are also libraries like Matplotlib (for creating visualizations), Scikit-Learn (for machine learning tasks), and more.