First up on our list is NumPy (short for Numerical Python). This library allows us to work with large arrays of numbers quickly and efficiently. For example, let’s say we have a dataset that includes the heights of 10,000 people. With NumPy, we can easily calculate their average height without breaking a sweat:
# Importing the NumPy library
import numpy as np
# Creating an array of heights in string format
heights = np.array(['5\'6", '5\'7", ...'])
# Converting the string heights to float values and removing the last two characters (inches)
heights_float = np.asarray(map(lambda x: float(x[:-2]), heights))
# Calculating the average height using the mean function from NumPy
average_height = np.mean(heights_float)
Next, we have Pandas the Swiss Army knife of data analysis libraries. With Pandas, we can easily read and write data from various formats (CSV, Excel, SQL), manipulate it with ease, and even perform statistical analyses. For example:
# Importing the Pandas library
import pandas as pd
# Reading a CSV file using the Pandas function "read_csv"
# and storing the data in the variable "data"
data = pd.read_csv('my_dataset.csv')
# Creating a new column named "new column" by multiplying
# the values in "column1" by 2 and assigning it to the new column
data['new column'] = data['column1'] * 2
Moving on, we have Matplotlib the go-to library for visualizing our data. With Matplotlib, we can create beautiful and informative plots using just a few lines of code:
# Importing the necessary library for data visualization
import matplotlib.pyplot as plt
# Creating an array with x values
x = [1, 2, 3]
# Creating an array with y values
y = [5, 7, 9]
# Plotting the data using the plot function from Matplotlib
plt.plot(x, y)
And that’s just scratching the surface! There are many other libraries on this list that can help us do everything from cleaning our data to predicting future trends. So if you want to become a data analysis superstar, make sure to check them out!