Data validation checks in Pandas - Step-by-Step Execution

Concept Flow - Data validation checks

Load DataFrame

↓

Check for missing values?

Yes→Report or fill missing

|No↓

Check data types?

Mismatch→Convert or report

↓

Check value ranges?

Out of range→Report or fix

↓

Check unique constraints?

Duplicates→Report or remove

↓

Data is valid

↓

Proceed with analysis

This flow shows how to check a DataFrame step-by-step for missing values, data types, value ranges, and duplicates to ensure data quality before analysis.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'age': [25, 30, None, 22],
    'score': [88, 92, 85, 90]
})

missing = df.isnull().sum()

This code creates a DataFrame and checks how many missing values each column has.

Execution Table

Step	Action	Evaluation	Result
1	Create DataFrame	DataFrame with 4 rows, 2 columns	DataFrame created
2	Check missing values with df.isnull().sum()	age: 1 missing, score: 0 missing	Missing values found in 'age'
3	Check data types with df.dtypes	age: float64, score: int64	Data types as expected
4	Check value ranges for 'age' (0-120)	Values: 25,30,None,22	None is missing, others in range
5	Check duplicates with df.duplicated()	No duplicates found	Data unique
6	Decide action on missing	Fill missing with mean age	Missing values filled
7	Final DataFrame ready	No missing, valid data	Data ready for analysis

💡 All validation checks passed or handled, data is clean for analysis

Variable Tracker

Variable	Start	After Step 2	After Step 6	Final
df	{'age':[25,30,None,22],'score':[88,92,85,90]}	Same	{'age':[25,30,25.6667,22],'score':[88,92,85,90]}	Cleaned DataFrame

Key Moments - 3 Insights

Why do we check for missing values first?

Missing values can cause errors or wrong results in later steps, so we detect and handle them early as shown in step 2 and 6 of the execution_table.

What if data types don't match expected types?

If types mismatch, we may need to convert them before analysis to avoid errors, as indicated in step 3 where data types are checked.

Why check for duplicates?

Duplicates can bias analysis results, so we identify and remove them if needed, as done in step 5.

Visual Quiz - 3 Questions

Look at the execution_table, how many missing values are found in the 'age' column at step 2?

A 0

B 2

C 1

D 3

💡 Hint

Check the 'Evaluation' column in row for step 2 in execution_table

At which step is the missing value in 'age' filled?

A Step 6

B Step 3

C Step 4

D Step 5

💡 Hint

Look for the step mentioning filling missing values in execution_table

If the 'score' column had duplicates, which step would detect it?

A Step 4

B Step 5

C Step 2

D Step 7

💡 Hint

Check the step about duplicates in execution_table

Concept Snapshot

Data validation checks in pandas:
- Check missing values with df.isnull().sum()
- Verify data types with df.dtypes
- Validate value ranges manually
- Detect duplicates with df.duplicated()
- Handle issues before analysis to ensure clean data

Full Transcript

This visual execution shows how to validate data in a pandas DataFrame step-by-step. First, we create a DataFrame with some missing values. We check for missing data using isnull().sum(), finding one missing in 'age'. Next, we verify data types to ensure they are as expected. Then, we check if values fall within acceptable ranges. We also look for duplicates to avoid bias. Finally, we fill missing values with the mean and confirm the data is clean and ready for analysis.