Types of Data
Numerical, categorical, and time-series data.
Types of Data is a free Python Academy lesson on CoddyKit — lesson 1 of 5. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Python Academy learning path, one of 5 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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Types of Data
Understanding the types of data is essential for working with datasets effectively. In this lesson, we’ll explore three key types of data: numerical, categorical, and time-series.
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Numerical Data
Numerical data represents numbers and can be either:
- Discrete: Whole numbers like
1, 2, 3. - Continuous: Numbers with decimals like
3.14, 7.89.
Examples: Age, temperature, and income.
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Categorical Data
Categorical data represents groups or categories. It can be:
- Nominal: Categories without a specific order (e.g., colors: red, blue, green).
- Ordinal: Categories with a meaningful order (e.g., rankings: low, medium, high).
Examples: Gender, product type, and education level.
4
Time-Series Data
Time-series data consists of values recorded over time intervals. It’s used to track changes or trends.
Examples:
- Stock prices recorded daily.
- Temperature measured hourly.
- Website traffic tracked weekly.
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Identifying Data Types in Python
You can use the dtypes attribute in Pandas to identify the data types of columns in a dataset.
Example:
import pandas as pd
data = {'Age': [25, 30], 'Gender': ['Male', 'Female']}
df = pd.DataFrame(data)
print(df.dtypes)6
Importance of Data Types
Knowing the type of data helps you:
- Choose the right analysis techniques.
- Select appropriate visualizations.
- Apply suitable preprocessing methods.
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Data Type Conversion
In Python, you can convert data types using functions like:
astype()in Pandas.int(),float(),str()for individual values.
Example:
df['Age'] = df['Age'].astype(float)8
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Recap
In this lesson, you learned about:
- Numerical data: Discrete and continuous values.
- Categorical data: Nominal and ordinal categories.
- Time-series data: Values tracked over time intervals.
Understanding data types is crucial for effective data analysis.
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Congratulations!
You’ve completed the lesson on Types of Data. Continue to the next lesson to learn how to handle missing data effectively.
Frequently asked questions
Is the “Types of Data” lesson free?
Yes — the full text of “Types of Data” is free to read here on the web, and the Python Academy course includes 5 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Python Academy course, upgrade to CoddyKit PRO.
What will I learn in “Types of Data”?
Numerical, categorical, and time-series data. You practise Python Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Python Academy?
No prior experience is required. Python Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 5, so you can start here or from the beginning and move at your own pace.
How long does the “Types of Data” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Python Academy lesson?
Yes. Every Python Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.