Loading structured data

As programs grow more data-driven, we quickly move beyond small, hand-written lists and dictionaries. Real systems often work with structured datasets that come from files created by other tools. In Python’s data ecosystem, pandas exists to make this kind of work practical, especially when tabular data needs to be inspected, filtered, or prepared for analysis inside larger AI-capable programs.

Tabular data

Tabular data is data arranged in rows and columns, where each row represents a record and each column represents a named attribute. This format is common because it maps well to how humans think about datasets and how many tools export information. CSV files are a typical example, and they are often the starting point for data analysis workflows.

Pandas in the Python ecosystem

pandas is a third-party library designed for working with structured, tabular data. It sits alongside libraries like NumPy and builds on them, providing higher-level tools for organizing and inspecting datasets. In practice, pandas is often the first stop when raw data needs to be explored before further processing or modeling.

Loading CSV data into a DataFrame

The core structure in pandas is the DataFrame, which represents an entire table of data. A common way to create a DataFrame is by loading it from a CSV file using pandas’ built-in loading functions.

import pandas as pd

data = pd.read_csv("planets.csv")

Here, the CSV file is read from disk and converted into an in-memory DataFrame that can be queried and transformed.

Inspecting a DataFrame’s structure

Once data is loaded, the first step is usually to get a sense of its shape and contents. pandas provides simple methods to inspect the structure without examining every value.

print(data.head())
print(data.columns)

These calls reveal column names and a small sample of rows, which is often enough to understand how the data is organized.

Accessing rows and columns

A DataFrame allows direct access to its columns by name, making it easy to work with individual attributes of the dataset. Rows can also be accessed by position or by label, depending on the situation.

planet_names = data["name"]
first_row = data.iloc[0]

This style of access supports clear, readable code when preparing or analyzing structured data.

Conclusion

At this point, we are oriented to what tabular data looks like, why pandas plays a central role in Python’s data ecosystem, and how structured data can be loaded and inspected using a DataFrame. That foundation is enough to recognize when pandas is the right tool and to begin exploring real datasets with confidence.