Getting Started with Pandas in Python

Before you can manipulate a single DataFrame, Pandas has to be installed in your environment. If you're working locally, that's a single pip install pandas (or conda install pandas in a Conda environment); if you're on a hosted notebook like Google Colab or a full Anaconda distribution, it usually ships pre-installed already. Once it's available, the near-universal convention across every tutorial, Stack Overflow answer, and production codebase you'll encounter is to import it under the short alias pd — import pandas as pd — so the rest of your code can reference pd.DataFrame, pd.Series, and so on without repeating the full module name.

After importing, it's worth confirming what you actually have installed. pd.__version__ prints the installed release (e.g. 2.2.1), which matters because Pandas' API has changed meaningfully across major versions — some methods have been deprecated or renamed, so knowing your version helps you match the right documentation and explains behavior differences between environments.

Pandas doesn't reimplement numerical computing from scratch — it's built directly on top of NumPy. A DataFrame's columns are ultimately backed by NumPy arrays, which is why you can construct one straight from a NumPy array (pd.DataFrame(np.array([[1, 2], [3, 4]]), columns=["A", "B"])) and why Pandas inherits NumPy's vectorized, C-level performance for numerical operations. This also means NumPy is a hard dependency: without it installed, Pandas cannot function.

Before using Pandas, it must be installed. It usually comes pre-installed in Data Science distributions like Anaconda or Google Colab.

Once installed, the universal convention is to import it under the alias "pd".

What is the industry-standard alias for importing Pandas?

• →pn
• →pd
• →pan

You can check which version of Pandas you are running using the __version__ attribute.

Which attribute is used to check the installed version of Pandas?

• →version()
• →__version__
• →pd.info()

Pandas is heavily dependent on NumPy. While Pandas provides the tabular structure, NumPy provides the mathematical engine.

True or False: Pandas can create a DataFrame directly from a NumPy array.

• →True
• →False

Now, prepare yourself. We are about to enter the ADA Defense Protocol. Ensure you know the basic dependencies.

ADA DEFENSE: If you uninstall NumPy, what will happen to Pandas?

• →Pandas will switch to using pure Python lists.
• →Pandas will crash, as NumPy is a required underlying dependency.
• →Nothing, Pandas operates completely independently.

Threat neutralized. System dependencies understood. You are ready to manipulate data.

Build a Real DataFrame from NumPy. Finish build_dataframe(): wrap the NumPy array into a DataFrame with named columns.