dtypes for column data types in Pandas - Step-by-Step Execution

Concept Flow - dtypes for column data types

Create DataFrame

↓

Access .dtypes

↓

Return Series

↓

Show each column's data type

↓

Use for analysis or debugging

We create a DataFrame, then access its .dtypes attribute to get a Series showing each column's data type.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
  'A': [1, 2],
  'B': [3.5, 4.5],
  'C': ['x', 'y']
})
print(df.dtypes)

This code creates a DataFrame with three columns and prints the data types of each column.

Execution Table

Step	Action	Code/Expression	Result
1	Create DataFrame	df = pd.DataFrame({'A': [1, 2], 'B': [3.5, 4.5], 'C': ['x', 'y']})	DataFrame with columns A, B, C
2	Access dtypes attribute	df.dtypes	Series showing: A int64, B float64, C object
3	Print dtypes	print(df.dtypes)	Output: A int64 B float64 C object dtype: object

💡 All columns' data types are shown as a Series; no further steps.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
df	undefined	DataFrame with columns A, B, C	Same DataFrame	Same DataFrame
df.dtypes	undefined	undefined	Series with dtypes	Series with dtypes

Key Moments - 2 Insights

Why does the 'C' column show 'object' instead of 'string'?

In pandas, text columns are stored as 'object' dtype by default, which means they hold Python objects like strings. See execution_table step 2 where 'C' is 'object'.

Is df.dtypes a DataFrame or something else?

df.dtypes returns a pandas Series, not a DataFrame. It lists each column name as index and its data type as values, as shown in execution_table step 2.

Visual Quiz - 3 Questions

Look at the execution table, what is the dtype of column 'B' after step 2?

A object

B int64

C float64

D bool

💡 Hint

Check the 'Result' column in execution_table row for step 2.

At which step is the DataFrame 'df' created?

A Step 2

B Step 1

C Step 3

D No step creates it

💡 Hint

Look at the 'Action' column in execution_table for when df is assigned.

If column 'A' had float numbers instead of integers, how would df.dtypes change?

A Column 'A' dtype would be float64

B Column 'A' dtype would be int64

C Column 'A' dtype would be object

D No change in dtypes

💡 Hint

Refer to variable_tracker and how dtypes reflect actual data types.

Concept Snapshot

Use df.dtypes to see each column's data type in a DataFrame.
It returns a Series with column names as index and dtypes as values.
Common dtypes: int64 for integers, float64 for decimals, object for text.
Useful for checking data before analysis or cleaning.

Full Transcript

We start by creating a pandas DataFrame with three columns: A with integers, B with floats, and C with strings. Then, we access the .dtypes attribute of the DataFrame, which returns a Series showing the data type of each column. The output shows that column A is int64, B is float64, and C is object (which pandas uses for text). This helps us understand what kind of data each column holds. The variable tracker shows how df and df.dtypes change during execution. Key moments clarify why text columns show as object and that df.dtypes returns a Series, not a DataFrame. The quiz tests understanding of dtypes and execution steps. Finally, the snapshot summarizes how to use df.dtypes to check column data types.