What is pandas?

pandas is a Python library for working with tabular data -- rows and columns, like a spreadsheet or a CSV file, except you filter, sort and summarize it with code instead of clicking around a UI. It's the standard starting point for data analysis in Python.

Importing pandas

import pandas as pd

pd is just a conventional short name -- everyone writes it this way, so your code reads the same as everyone else's.

Creating a DataFrame

A DataFrame is pandas' table type. The simplest way to build one is from a dict, where each key becomes a column:

data = {
    "name": ["Ada", "Tomi", "Chidi", "Bola"],
    "score": [60, 70, 80, 90],
}
df = pd.DataFrame(data)
print(df)

name score 0 Ada 60 1 Tomi 70 2 Chidi 80 3 Bola 90

The leftmost column (0, 1, 2, 3) is the index -- pandas adds it automatically to label each row.

Picking out a column

print(df["score"])

A single column (df["score"]) is a Series -- a DataFrame is really just a collection of Series sharing one index.

Peeking at a big table

print(df.head())   # first 5 rows by default

Quick summaries

print(df["score"].mean())   # average
print(df["score"].sum())    # total
print(df["score"].max())    # highest
print(df["score"].min())    # lowest

Why This Matters for Your Exams

Almost everything else in data analysis is built on these two ideas: a DataFrame is a table, and df["column"] pulls out one column as a Series. Get comfortable with that before anything else -- filtering, grouping, and cleaning are all just operations on top of it.

Try it yourself