Numeric types (int64, float64) in Pandas - Step-by-Step Execution

Concept Flow - Numeric types (int64, float64)

Create DataFrame with numbers

↓

Check data types of columns

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int64 for whole numbers

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float64 for decimal numbers

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Use types for calculations and memory

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End

Start with a DataFrame, check column types, identify int64 for integers and float64 for decimals, then use these types for calculations.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.1, 2.2, 3.3]})
print(df.dtypes)

Create a DataFrame with integer and float columns, then print their data types.

Execution Table

Step	Action	DataFrame Content	Column 'A' dtype	Column 'B' dtype	Output
1	Create DataFrame	{'A': [1, 2, 3], 'B': [1.1, 2.2, 3.3]}	int64	float64	DataFrame created
2	Check dtypes	Same as above	int64	float64	A int64 B float64 dtype: object
3	Print dtypes	Same as above	int64	float64	Printed data types
4	End	-	-	-	Execution complete

💡 All steps done, data types identified correctly for each column.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
df	None	{'A': [1, 2, 3], 'B': [1.1, 2.2, 3.3]}	Same	Same
df['A'].dtype	None	int64	int64	int64
df['B'].dtype	None	float64	float64	float64

Key Moments - 2 Insights

Why is column 'A' int64 and not float64 even though pandas can store floats?

Because all values in column 'A' are whole numbers without decimals, pandas assigns int64 to save memory and keep data precise, as shown in execution_table step 2.

What happens if a float value is added to column 'A'?

The dtype of column 'A' would change to float64 to accommodate decimal numbers, since int64 cannot store decimals. This is implied by the difference between columns 'A' and 'B' in execution_table.

Visual Quiz - 3 Questions

Look at the execution table at step 2, what is the data type of column 'B'?

A object

B int64

C float64

D string

💡 Hint

Check the 'Column 'B' dtype' column in execution_table row for step 2.

At which step does the DataFrame get created with the numeric columns?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' column in execution_table to find when DataFrame is created.

If column 'A' had a decimal value, what would its dtype likely be?

A int64

B float64

C object

D bool

💡 Hint

Compare dtypes of columns 'A' and 'B' in execution_table to understand how decimals affect dtype.

Concept Snapshot

pandas numeric types:
- int64: stores whole numbers
- float64: stores decimal numbers
- Use df.dtypes to check column types
- Types affect memory and calculations
- Mixing decimals in int column converts it to float64

Full Transcript

We start by creating a pandas DataFrame with two columns: 'A' containing whole numbers and 'B' containing decimal numbers. We then check the data types of each column using df.dtypes. Column 'A' is assigned the int64 type because it contains only whole numbers, while column 'B' is assigned float64 because it contains decimals. These types help pandas optimize memory and calculations. If a decimal number is added to column 'A', its type would change to float64 to accommodate decimals. This step-by-step execution helps understand how pandas assigns numeric types.