10 Useful Python Libraries Every Developer Should Know About (and How to Use Them)

Let’s take a look at the pandas library, one of the most popular libraries for data manipulation and analysis in Python. Here's an example of how to use it:

First, we need to import the pandas library:

import pandas as pd

Then, we can create a simple dataframe with some dummy data:

df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'],
                     'Age': [25, 30, 35],
                     'Occupation': ['Engineer', 'Teacher', 'Designer']})
print(df)

This will output a table with three columns and three rows, representing our simple dataset:

Name   Age  Occupation
0  Alice  25  Engineer
1   Bob  30    Teacher
2  Charlie  35     Designer

We can then perform various data manipulation tasks using the pandas library. For example, let's aggregate the data by grouping it by occupation:

df.groupby('Occupation').count()

This will output a new table showing the count of occurrences for each category:

Age Occupation
Occupation
Engineer   ~ Sheet1     3
Teacher     ~ Sheet1     1
Designer   ~ Sheet1     1

Pandas also provides powerful support for data cleaning and transformation, such as filling missing data, handling duplicate rows, and converting data types. With its concise and expressive syntax, pandas has become a…