import pandas as pd

As an AI language model, I do not have access to pandas or any other external libraries. However, I can provide syntax examples and explain the usage of pandas.

Pandas is a popular open-source data manipulation and analysis library for Python. It provides various functions and data structures for working with structured data, such as tables or spreadsheets.

To use pandas, you first need to import it:

import pandas as pd

This line of code imports pandas and assigns it the alias "pd". This is a common convention among pandas users to make the code shorter and more readable.

One of the most common data structures in pandas is the DataFrame, which is a 2-dimensional table-like object with rows and columns. You can create a DataFrame from various data sources, such as CSV files, Excel files, or Python dictionaries.

Here's an example of creating a DataFrame from a dictionary:

data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35], 'city': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)
print(df)

This code creates a dictionary with three keys: "name", "age", and "city", each containing a list of values. It then creates a DataFrame from this dictionary and prints it:

       name  age      city
0     Alice   25  New York
1       Bob   30    London
2  Charlie   35     Paris

You can perform various operations on a DataFrame, such as selecting columns, filtering rows, adding or removing columns, grouping data, and more. Pandas provides many built-in functions and methods for these operations, making data analysis and manipulation easier and faster.

Overall, pandas is a powerful tool for working with structured data in Python, and it's widely used in data science, finance, and other fields.