Load and Explore Data

Meet pandas

pandas is the Python library for working with tables of data, like Excel but with code. It's already installed in Google Colab. If you're new to Python, take Python for Beginners first.

You'll use a practice dataset from a fictional drinks and household goods distributor: every order line from January 2025 to June 2026.

Load the data

Open a new notebook at colab.research.google.com and run:

import pandas as pd

url = "https://academy.cloudtechanalytics.com/datasets/sales/orders.csv"
orders = pd.read_csv(url)

orders is now a DataFrame: a table with rows and named columns. pd is the usual short name for pandas.

Take a first look

Run each line in its own cell:

orders.head()      # first 5 rows
orders.tail(3)     # last 3 rows
orders.shape       # (rows, columns)
orders.columns     # column names

You should see 4,266 rows and 7 columns:

Understand the columns

orders.info()

info() shows each column's type and how many values are filled in. Here nothing is missing, but order_date is stored as text (object). You'll fix that in the next module.

orders.describe()

describe() gives quick statistics for the number columns: count, mean, min, max and quartiles. For example, the average quantity is about 13.8 cartons and unit prices run from ₦3,600 to ₦24,600.

Select columns and count values

orders["quantity"]                 # one column
orders[["order_date", "quantity"]] # several columns (note the double brackets)
orders["discount_pct"].value_counts()

value_counts() shows how often each value appears: 2,519 lines had no discount, 1,222 had 5% and 525 had 10%.

Try it

1. Load the orders data and check the shape is (4266, 7).
2. Use describe() to find the largest single quantity ordered.
3. Use value_counts() on product_id to find the product that appears in the most order lines.
4. Write two sentences in a text cell describing the dataset in your own words.