Understanding the Use of 'pandas' Library in Python

The 'pandas' library is primarily used in Python for data analysis and manipulation. This powerful, flexible open-source data analysis and manipulation library for Python enables data scientists to perform a wide variety of complex tasks with ease.

Applications of 'pandas'

'pandas' has a broad range of applications but is fundamentally used for handling and analyzing data. It is typically used in exploratory data analysis, data transformation, and data visualization. When working with 'pandas', you can ingest data from different file formats such as CSV, excel, SQL databases, and even web APIs.

Here is a simple example usage of 'pandas' for data analysis:

import pandas as pd

# Create a simple dataframe
data = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]}
df = pd.DataFrame(data)

# Display the data
print(df)

In this example, a DataFrame is created — which is a two-dimensional table of data with columns that can be of different types, similar to a spreadsheet or SQL table.

Best Practices and Insights

Though the 'pandas' library in Python is primarily utilized for data analysis, it's recommended to follow certain best practices for efficient use.

1. Use Vectorization: Pandas operations are vectorized, making them extremely fast. Wherever possible, avoid using native Python loops and instead use pandas' built-in methods.
2. Avoid Chaining: Chained indexing can lead to unpredictable results, so it's best to avoid. Opt for using the .loc, .iloc or .at accessors whenever possible.
3. Handle Missing Data: Pandas gives you the tools to handle missing data effectively. The dropna() and fillna() functions are useful for treating missing values.

In conclusion, 'pandas' is an essential tool for any data scientist or analyst working in Python. Its data manipulation capabilities make it ideal for data cleaning, transformation, and extraction. With 'pandas', you can turn raw data into insights through an easily manipulatable, powerful data object, the DataFrame.