The Five Types of Data You Will Meet

Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up causes most beginner bugs.

25 min · Reviewed 2026

Types Matter More Than You Think

In a CSV file, everything looks like text. But when you load it into Python or a database, each column gets a type. Get the types wrong and your model will behave bizarrely, or refuse to train at all.

The five you must know

Type	Example	Common pitfall
Numeric	17.5, 1000	Zip codes look numeric but are not
Text (string)	hello, Claude	Dates often masquerade as strings
Date/time	2026-04-23	Time zones cause silent bugs
Boolean	true, false	1 and 0 are often used instead
Identifier (ID)	user_a7b9c	Do not compute statistics on IDs

Numeric: continuous vs. discrete

Continuous numbers can take any value (height: 170.5 cm). Discrete numbers are whole counts (students in class: 27). Machine learning models treat these differently. A continuous model might predict 27.3 students, which is meaningless.

Categorical data, disguised as numbers

Colors like red, blue, green are categories. Sometimes datasets encode them as 1, 2, 3 for space. But computing an average category (1.8) is nonsense. Models need to know these are categorical, not numeric.

import pandas as pd

df = pd.read_csv('students.csv')

# Force zip code to stay a string
df = pd.read_csv('students.csv', dtype={'zip_code': str})

# Parse a date column properly
df['birthday'] = pd.to_datetime(df['birthday'])

# Mark a column as categorical
df['grade'] = df['grade'].astype('category')

print(df.dtypes)Setting types correctly in pandas

The big idea: types are the skeleton of a dataset. Get them right at load time, and everything downstream gets easier. Get them wrong, and you will spend hours chasing phantom bugs.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-types-of-data

What is the core idea behind "The Five Types of Data You Will Meet"?
1. Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up causes most beginner bugs.
2. json_normalize
3. markdown
4. Your location pings a cell tower every few minutes
Which term best describes a foundational idea in "The Five Types of Data You Will Meet"?
1. categorical
2. numeric
3. datetime
4. boolean
A learner studying The Five Types of Data You Will Meet would need to understand which concept?
1. numeric
2. datetime
3. categorical
4. boolean
Which of these is directly relevant to The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. boolean
4. datetime
What is the key insight about "The zip code trap" in the context of The Five Types of Data You Will Meet?
1. A zip code like 02139 is an identifier, not a number. Storing it as int strips the leading zero and turns it into 2139.
2. json_normalize
3. markdown
4. Your location pings a cell tower every few minutes
Which statement accurately describes an aspect of The Five Types of Data You Will Meet?
1. json_normalize
2. In a CSV file, everything looks like text. But when you load it into Python or a database, each column gets a type.
3. markdown
4. Your location pings a cell tower every few minutes
What does working with The Five Types of Data You Will Meet typically involve?
1. json_normalize
2. markdown
3. Continuous numbers can take any value (height: 170.5 cm). Discrete numbers are whole counts (students in class: 27).
4. Your location pings a cell tower every few minutes
Which of the following is true about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Your location pings a cell tower every few minutes
4. Colors like red, blue, green are categories. Sometimes datasets encode them as 1, 2, 3 for space. But computing an average category (1.
Which best describes the scope of "The Five Types of Data You Will Meet"?
1. It focuses on Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up cau
2. It is unrelated to foundations workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. The five you must know
3. markdown
4. Your location pings a cell tower every few minutes
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Numeric: continuous vs. discrete
4. Your location pings a cell tower every few minutes
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Your location pings a cell tower every few minutes
4. Categorical data, disguised as numbers
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean

← Back to interactive lesson

Tendril · Builders · AI Foundations

The Five Types of Data You Will Meet

Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up causes most beginner bugs.

25 min · Reviewed 2026

Types Matter More Than You Think

The five you must know

Type	Example	Common pitfall
Numeric	17.5, 1000	Zip codes look numeric but are not
Text (string)	hello, Claude	Dates often masquerade as strings
Date/time	2026-04-23	Time zones cause silent bugs
Boolean	true, false	1 and 0 are often used instead
Identifier (ID)	user_a7b9c	Do not compute statistics on IDs

Numeric: continuous vs. discrete

Categorical data, disguised as numbers

import pandas as pd

df = pd.read_csv('students.csv')

# Force zip code to stay a string
df = pd.read_csv('students.csv', dtype={'zip_code': str})

# Parse a date column properly
df['birthday'] = pd.to_datetime(df['birthday'])

# Mark a column as categorical
df['grade'] = df['grade'].astype('category')

print(df.dtypes)Setting types correctly in pandas

The big idea: types are the skeleton of a dataset. Get them right at load time, and everything downstream gets easier. Get them wrong, and you will spend hours chasing phantom bugs.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-types-of-data

What is the core idea behind "The Five Types of Data You Will Meet"?
1. Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up causes most beginner bugs.
2. json_normalize
3. markdown
4. Your location pings a cell tower every few minutes
Which term best describes a foundational idea in "The Five Types of Data You Will Meet"?
1. categorical
2. numeric
3. datetime
4. boolean
A learner studying The Five Types of Data You Will Meet would need to understand which concept?
1. numeric
2. datetime
3. categorical
4. boolean
Which of these is directly relevant to The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. boolean
4. datetime
What is the key insight about "The zip code trap" in the context of The Five Types of Data You Will Meet?
1. A zip code like 02139 is an identifier, not a number. Storing it as int strips the leading zero and turns it into 2139.
2. json_normalize
3. markdown
4. Your location pings a cell tower every few minutes
Which statement accurately describes an aspect of The Five Types of Data You Will Meet?
1. json_normalize
2. In a CSV file, everything looks like text. But when you load it into Python or a database, each column gets a type.
3. markdown
4. Your location pings a cell tower every few minutes
What does working with The Five Types of Data You Will Meet typically involve?
1. json_normalize
2. markdown
3. Continuous numbers can take any value (height: 170.5 cm). Discrete numbers are whole counts (students in class: 27).
4. Your location pings a cell tower every few minutes
Which of the following is true about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Your location pings a cell tower every few minutes
4. Colors like red, blue, green are categories. Sometimes datasets encode them as 1, 2, 3 for space. But computing an average category (1.
Which best describes the scope of "The Five Types of Data You Will Meet"?
1. It focuses on Every column in a dataset has a type: number, text, date, boolean, or identifier. Mixing them up cau
2. It is unrelated to foundations workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. The five you must know
3. markdown
4. Your location pings a cell tower every few minutes
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Numeric: continuous vs. discrete
4. Your location pings a cell tower every few minutes
Which section heading best belongs in a lesson about The Five Types of Data You Will Meet?
1. json_normalize
2. markdown
3. Your location pings a cell tower every few minutes
4. Categorical data, disguised as numbers
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean
Which of the following is a concept covered in The Five Types of Data You Will Meet?
1. numeric
2. categorical
3. datetime
4. boolean

← Back to interactive lesson