Checking data types in Data Analysis Python - Step-by-Step Execution

Concept Flow - Checking data types

Start with data

↓

Select variable or value

↓

Use type() function

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Get data type result

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Use data type info for decisions or display

This flow shows how to check the type of any data value using the type() function in Python.

Execution Sample

Data Analysis Python

x = 10
print(type(x))
y = 'hello'
print(type(y))

This code checks and prints the data types of two variables, one integer and one string.

Execution Table

Step	Code Line	Variable	Action	Output
1	x = 10	x	Assign integer 10 to x
2	print(type(x))	x	Check type of x	<class 'int'>
3	y = 'hello'	y	Assign string 'hello' to y
4	print(type(y))	y	Check type of y	<class 'str'>
5	End		No more code to run	Execution stops

💡 All lines executed, program ends

Variable Tracker

Variable	Start	After Step 1	After Step 3	Final
x	undefined	10	10	10
y	undefined	undefined	hello	hello

Key Moments - 2 Insights

Why does print(type(x)) show <class 'int'> instead of just 'int'?

The type() function returns a type object, which prints as <class 'int'>. This means x is an integer type. See execution_table row 2 for the output.

Can type() be used on values directly, not just variables?

Yes, type() works on any value or expression. For example, type(3.14) returns <class 'float'>. This is similar to what happens in execution_table rows 2 and 4.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the output of type(x)?

A int

B <class 'str'>

C <class 'int'>

D 10

💡 Hint

Check the Output column in execution_table row 2

At which step is the variable y assigned a value?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the Code Line and Variable columns in execution_table

If we change x = 10 to x = 3.14, what would print(type(x)) output at step 2?

A <class 'float'>

B <class 'int'>

C <class 'str'>

D 3.14

💡 Hint

Recall that 3.14 is a floating point number, so type() returns

Concept Snapshot

Checking data types in Python:
- Use type(variable) to find the data type
- Returns a type object like <class 'int'> or <class 'str'>
- Works on variables or direct values
- Helps understand data for processing or debugging

Full Transcript

This lesson shows how to check data types in Python using the type() function. We assign values to variables x and y, then print their types. The output shows <class 'int'> for x=10 and <class 'str'> for y='hello'. The type() function returns the data type object, which helps us know what kind of data we are working with. This is useful for making decisions in code or debugging. The execution table traces each step and output clearly.

Practice

1. What does the DataFrame.dtypes attribute show in a pandas DataFrame?

easy

A. The data type of each column in the DataFrame

B. The number of rows in the DataFrame

C. The summary statistics of numeric columns

D. The first five rows of the DataFrame

Solution

1. Step 1: Understand what dtypes means

   The dtypes attribute in pandas shows the data type of each column in a DataFrame.

2. Step 2: Compare options with the definition

   Only The data type of each column in the DataFrame ('The data type of each column in the DataFrame') correctly describes this: it shows the data type of each column.

3. Final Answer:

   The data type of each column in the DataFrame -> Option A

4. Quick Check:

   DataFrame.dtypes = column types [OK]

Hint: Remember: dtypes means data types per column [OK]

Common Mistakes:

• Confusing dtypes with DataFrame shape
• Thinking dtypes shows data values
• Mixing dtypes with summary statistics

2. Which of the following is the correct way to check data types of columns in a pandas DataFrame named df?

easy

A. df.types()

B. df.dtypes

C. df.type()

D. df.get_types()

Solution

1. Step 1: Recall pandas syntax for data types

   In pandas, dtypes is an attribute (not a method), so it is accessed without parentheses.

2. Step 2: Evaluate each option

   df.dtypes uses df.dtypes correctly. Options A, B, and C use incorrect method names or parentheses.

3. Final Answer:

   df.dtypes -> Option B

4. Quick Check:

   Use attribute dtypes without parentheses [OK]

Hint: Use df.dtypes without parentheses to check types [OK]

Common Mistakes:

• Adding parentheses after dtypes
• Using incorrect method names
• Confusing dtypes with type() function

3. Given the following code:

import pandas as pd
data = {'age': [25, 30], 'name': ['Alice', 'Bob'], 'score': [88.5, 92.0]}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. age int64
name object
score float64
dtype: object

B. age float64
name object
score int64
dtype: object

C. age int32
name string
score float32
dtype: object

D. age object
name object
score object
dtype: object

Solution

1. Step 1: Check data types of each column in the dictionary

   Column 'age' has integers (25, 30) so type is int64 by default. 'name' has strings, so type is object. 'score' has floats (88.5, 92.0), so type is float64.

2. Step 2: Confirm pandas default data types

   Pandas uses int64 for integers, object for strings, and float64 for floats by default.

3. Final Answer:

   age int64 name object score float64 dtype: object -> Option A

4. Quick Check:

   Integers=int64, Strings=object, Floats=float64 [OK]

Hint: Integers=int64, strings=object, floats=float64 in pandas [OK]

Common Mistakes:

• Assuming strings have type 'string' instead of 'object'
• Mixing int and float types
• Confusing int32 with int64 default

4. You run this code:

import pandas as pd
data = {'A': [1, 2], 'B': ['x', 'y']}
df = pd.DataFrame(data)
print(df.dtypes())

But it gives an error. What is the problem?

medium

A. The DataFrame is empty

B. You need to import numpy first

C. The column names must be integers

D. You should use df.dtypes without parentheses

Solution

1. Step 1: Identify the error cause

   The code uses df.dtypes() with parentheses, but dtypes is an attribute, not a method.

2. Step 2: Correct the syntax

   Remove parentheses and use df.dtypes to get the data types without error.

3. Final Answer:

   You should use df.dtypes without parentheses -> Option D

4. Quick Check:

   dtypes is attribute, not method [OK]

Hint: No parentheses after dtypes attribute [OK]

Common Mistakes:

• Adding parentheses after dtypes
• Thinking DataFrame is empty causes error
• Believing numpy import is needed for dtypes

5. You have a DataFrame df with columns: ['id', 'value', 'flag']. The flag column contains only 0 and 1 but is stored as integers. You want to convert flag to a boolean type for better memory use. Which code correctly changes the data type?

hard

A. df['flag'] = df['flag'].to_bool()

B. df['flag'] = bool(df['flag'])

C. df['flag'] = df['flag'].astype(bool)

D. df['flag'] = df['flag'].convert(bool)

Solution

1. Step 1: Understand how to convert column types in pandas

   Use the astype() method to convert a column to a different data type.

2. Step 2: Check each option's validity

   df['flag'] = df['flag'].astype(bool) uses astype(bool) correctly. df['flag'] = bool(df['flag']) tries to convert the whole Series to bool, which returns a single True/False, not a column. Options C and D use non-existent methods.

3. Final Answer:

   df['flag'] = df['flag'].astype(bool) -> Option C

4. Quick Check:

   Use astype(bool) to convert column to boolean [OK]

Hint: Use astype(bool) to convert column to boolean type [OK]

Common Mistakes:

• Using bool() on entire Series instead of astype
• Trying non-existent methods like to_bool()
• Confusing convert() with astype()