Datasets and tips

Datasets

We have nine datasets for you to choose from. We recommend saving your data inside your project.

Dataset	Description	Source
World populations	A summary of world populations and corresponding statistics	Data from a Tidy Tuesday post on 2014 CIA World Factbook data
Soccer players	A summary of approx. 6000 soccer players from 2024	Data from a Kaggle submission.
Coffee survey	A survey of blind coffee tasting results	Data from a Kaggle submission
Gapminder	GDP and life expectancy data by country	Data from the Research Bazaar’s R novice tutorial, sourced from Gapminder.
Melbourne housing data	A collection of houses for sale in Melbourne.	Data from a Kaggle submission
Goodreads books	A summary of books on Goodreads.	Data from a Kaggle submission
Queensland hospitals	Queensland emergency department statistics.	Data from the Queensland Government’s Open Data Portal.
Queensland fuel prices	Fuel prices by the pump in Queensland	Data from the Queensland Government’s Open Data Portal
Aeroplane bird strikes	Aeroplane bird strike incidents fron the 90s	Data from a Tidy Tuesday post sourced from an FAA database

Tips

Here’s a few general tips. In addition, we strongly recommend using popular cheatsheets, which give a quick and easy reference for common packages and functions, and from Data to Viz, which guides you through choosing a visualisation.

Hotkeys

Code	Hotkey	Description
	`F9` (or `Fn` + `F9`)	Run current line
`# %%`	`Ctrl` + `2`	New cell (only in Spyder)
	`Ctrl`+`Enter`	Run current cell (when in Script)
	`Ctrl`+`C`	Cancel current operation (when in Console)

Data manipulation

Use the pandas package to analyse your data:

import pandas as pd

Importing and exporting data

In case you’ve forgotten, use the read.csv() function to import data:

df = pd.read_csv("data/dataset.csv")

If you’d like to export any files from Python to “.csv”, use the .to_csv() method

df.to_csv("data/output_name.csv")

Initial exploration

You’ll want to explore the data to start with - below are a few functions to get started.

Function	Example	Description
`df.columns`		Returns the variable names
`df.info()`		Returns the structure of the dataset (variable names, counts and types)
`df["variable"]`		Returns a specific column
`pd.unique("variable")`		Returns the unique values of a variable
`df.describe()` or `df["variable"].describe()`	Returns a statistical summary of the dataset or a variable

Removing `nan`s

We can remove nans by filtering with the condition df["variable"].notna():

df = df[df["variable"].notna()]

Time series data

If you’ve picked a dataset with time-series data (e.g. a “date” variable), you should transform that variable so that it visualises better:

df["variable"] = pd.to_datetime(df["variable"])

Categorical and ordered data

If you’re dealing with categorical data, it can be helpful to tell Python

df["variable"] = df["variable"].astype("category")

To manually specify the order of categories, use the df["variable"].cat.reorder_categories() function and use the ordered = True parameter

df["variable"] = df["variable"].cat.reorder_categories(["cat1", "cat2", ...], ordered = True)

This is particularly useful for the Coffee survey dataset.

If you’re dealing with categorical data, look at the pandas guide for inspiration and help.

Renaming variables

Some datasets have cumbersome names for their variables. We can change variable names with df.rename(), sending a dictionary to the columns = parameter:

df = df.rename(columns = {"old_name": "new_name"})

This is particularly useful for the World population dataset.

A dictionary is a Python variable with key-value pairs. The structure is key: value, so above we have a dictionary with one key, "old_name" and corresponding value "new_name". They are created as follows:
example_dictionary = {"key1": "value1",
                      "key2": "value2",
                      "key3": "value3",
                      ...}
Note that multiple lines are used purely for readability, you could just as well do this on one line.

Visualisation

You can make simple visualisations with seaborn’s relplot(), catplot() and displot() functions

import seaborn as sns

sns.relplot(data = df, x = "variable_x", y = "variable_y", hue = "variable_colour", ...)

We can add plot elements easily with matplotlib.pyplot

import seaborn as sns
import matplotlib.pyplot as plt

sns.relplot(data = df, x = "variable_x", y = "variable_y", hue = "variable_colour", ...)
plt.xlabel("x axis label")
plt.ylabel("y axis label")