info() for column types in Data Analysis Python - Step-by-Step Execution

Concept Flow - info() for column types

Load DataFrame

↓

Call df.info()

↓

Scan each column

↓

Count non-null values

↓

Identify data type

↓

Print summary table

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Show memory usage

The info() method scans each column in the DataFrame, counts non-null values, identifies data types, and prints a summary including memory usage.

Execution Sample

Data Analysis Python

import pandas as pd

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

df.info()

This code creates a DataFrame with two columns and calls info() to show column types and non-null counts.

Execution Table

Step	Action	Column	Non-null Count	Data Type	Memory Usage
1	Scan column	A	2	float64	24 bytes
2	Scan column	B	3	object	72 bytes
3	Calculate total memory	-	-	-	96 bytes
4	Print summary table	-	-	-	-
5	End	-	-	-	-

💡 All columns scanned and summary printed

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
non_null_counts	{}	{'A': 2}	{'A': 2, 'B': 3}	{'A': 2, 'B': 3}	{'A': 2, 'B': 3}
data_types	{}	{'A': 'float64'}	{'A': 'float64', 'B': 'object'}	{'A': 'float64', 'B': 'object'}	{'A': 'float64', 'B': 'object'}
memory_usage	0	24	96	96	96

Key Moments - 2 Insights

Why does column 'A' show 2 non-null values instead of 3?

Because one value in column 'A' is None (missing), so info() counts only non-null entries as shown in execution_table rows 1 and 2.

What does the 'object' data type mean for column 'B'?

It means the column holds strings or mixed types; info() shows this as 'object' in execution_table row 2.

Visual Quiz - 3 Questions

Look at the execution table, what is the non-null count for column 'B' at step 2?

A 3

B 2

C 0

D None

💡 Hint

Check the 'Non-null Count' column in execution_table row 2.

At which step is the total memory usage calculated?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Memory Usage' column and the 'Action' column in execution_table.

If column 'A' had no missing values, how would the non-null count change at step 1?

A It would be 0

B It would be 3

C It would be 2

D It would be 'object'

💡 Hint

Refer to variable_tracker for 'non_null_counts' and how missing values affect counts.

Concept Snapshot

df.info() shows a summary of a DataFrame:
- Lists each column
- Shows non-null counts
- Shows data types
- Displays memory usage
Useful to quickly check data completeness and types.

Full Transcript

The info() method in pandas scans each column of a DataFrame to count how many values are not missing (non-null). It also identifies the data type of each column, such as float64 for numbers or object for strings. Then it prints a summary table showing these counts and types, along with the memory used by the DataFrame. For example, if a column has missing values, the non-null count will be less than the total rows. This helps understand the data structure and completeness quickly.