info() for column types and nulls in Pandas - Step-by-Step Execution

Concept Flow - info() for column types and nulls

Create DataFrame

↓

Call df.info()

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Scan each column

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Count non-null values

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Identify data types

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Print summary table

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Show memory usage

The flow shows how calling info() scans each DataFrame column to count non-null values, identify data types, and then prints a summary with memory usage.

Execution Sample

Pandas

import pandas as pd

data = {'A': [1, 2, None], 'B': ['x', None, 'z']}
df = pd.DataFrame(data)
df.info()

This code creates a DataFrame with some nulls and calls info() to show column types and null counts.

Execution Table

Step	Action	Column	Non-null Count	Data Type	Memory Usage
1	Create DataFrame	-	-	-	-
2	Call df.info()	-	-	-	-
3	Scan column	A	2	float64	-
4	Scan column	B	2	object	-
5	Print summary	-	A: 2 non-null B: 2 non-null	A: float64 B: object	Approx. 224 bytes
6	Show memory usage	-	-	-	224 bytes

💡 All columns scanned and summary printed

Variable Tracker

Variable	Start	After df.info()
df	{'A':[1,2,None],'B':['x',None,'z']}	DataFrame with 2 columns, 3 rows, null counts shown

Key Moments - 2 Insights

Why does info() show fewer non-null counts than total rows?

Because info() counts only non-null (not missing) values per column, as seen in execution_table rows 3 and 4 where columns have 2 non-null out of 3 rows.

Why are some columns shown as 'object' type?

Columns with mixed or string data are shown as 'object' type, like column B in execution_table row 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the non-null count for column 'A' at step 3?

A 3

B 1

C 2

D 0

💡 Hint

Check the 'Non-null Count' column in execution_table row 3.

At which step does info() print the summary table?

A Step 5

B Step 4

C Step 2

D Step 6

💡 Hint

Look for 'Print summary' action in execution_table.

If column 'B' had no nulls, how would the non-null count change at step 4?

A It would be 0

B It would be 3

C It would be 2

D It would be 1

💡 Hint

Non-null count equals number of non-missing values; see variable_tracker for row count.

Concept Snapshot

df.info() shows a summary of DataFrame columns:
- Non-null counts per column
- Data types (e.g., float64, object)
- Memory usage
It helps quickly see missing data and column types.

Full Transcript

We start by creating a DataFrame with some missing values. When we call df.info(), pandas scans each column to count how many values are not missing. It also identifies the data type of each column, like float64 for numbers or object for text. Then it prints a summary table showing these counts and types, plus the memory used. This helps us understand the data structure and find missing values quickly.