Why combining datasets creates complete pictures in Data Analysis Python - Visual Breakdown

Concept Flow - Why combining datasets creates complete pictures

Dataset A

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Identify common key column

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Merge datasets on key

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Combined dataset with more info

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Better analysis and insights

We start with two datasets, find a common column to join them, merge them, and get a richer dataset for better understanding.

Execution Sample

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Amy', 'Bob', 'Cara']})
df2 = pd.DataFrame({'ID': [2, 3, 4], 'Age': [25, 30, 22]})

combined = pd.merge(df1, df2, on='ID', how='outer')
print(combined)

This code merges two datasets on the 'ID' column to combine names and ages, showing all IDs from both datasets.

Execution Table

Step	Action	df1 content	df2 content	Merge type	Resulting rows	Resulting data
1	Create df1	[{'ID':1,'Name':'Amy'},{'ID':2,'Name':'Bob'},{'ID':3,'Name':'Cara'}]	N/A	N/A	3	N/A
2	Create df2	N/A	[{'ID':2,'Age':25},{'ID':3,'Age':30},{'ID':4,'Age':22}]	N/A	3	N/A
3	Merge df1 and df2 on 'ID' with outer join	Same as step 1	Same as step 2	outer	4	[{'ID':1,'Name':'Amy','Age':null},{'ID':2,'Name':'Bob','Age':25},{'ID':3,'Name':'Cara','Age':30},{'ID':4,'Name':null,'Age':22}]
4	Print combined dataset	N/A	N/A	N/A	4	Displayed combined data with all IDs and info

💡 Merge completes with all unique IDs included, filling missing info with NaN

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df1	undefined	[{'ID':1,'Name':'Amy'},{'ID':2,'Name':'Bob'},{'ID':3,'Name':'Cara'}]	[{'ID':1,'Name':'Amy'},{'ID':2,'Name':'Bob'},{'ID':3,'Name':'Cara'}]	[{'ID':1,'Name':'Amy'},{'ID':2,'Name':'Bob'},{'ID':3,'Name':'Cara'}]	[{'ID':1,'Name':'Amy'},{'ID':2,'Name':'Bob'},{'ID':3,'Name':'Cara'}]
df2	undefined	undefined	[{'ID':2,'Age':25},{'ID':3,'Age':30},{'ID':4,'Age':22}]	[{'ID':2,'Age':25},{'ID':3,'Age':30},{'ID':4,'Age':22}]	[{'ID':2,'Age':25},{'ID':3,'Age':30},{'ID':4,'Age':22}]
combined	undefined	undefined	undefined	[{'ID':1,'Name':'Amy','Age':null},{'ID':2,'Name':'Bob','Age':25},{'ID':3,'Name':'Cara','Age':30},{'ID':4,'Name':null,'Age':22}]	[{'ID':1,'Name':'Amy','Age':null},{'ID':2,'Name':'Bob','Age':25},{'ID':3,'Name':'Cara','Age':30},{'ID':4,'Name':null,'Age':22}]

Key Moments - 2 Insights

Why do some rows have NaN values after merging?

NaN appears when a row exists in one dataset but not the other, as shown in step 3 of the execution_table where ID 1 has no Age and ID 4 has no Name.

What does 'outer' merge mean in this example?

'Outer' merge keeps all rows from both datasets, combining matching rows and filling missing parts with NaN, as seen in step 3 where all IDs 1,2,3,4 appear.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, how many rows does the combined dataset have?

A 4

B 3

C 2

D 5

💡 Hint

Check the 'Resulting rows' column at step 3 in the execution_table.

According to variable_tracker, what is the value of 'combined' after step 3?

A Only rows with matching IDs

B All rows from both datasets with NaN where data is missing

C Empty DataFrame

D Only rows from df1

💡 Hint

Look at the 'combined' row under 'After Step 3' in variable_tracker.

If we changed the merge type from 'outer' to 'inner', what would happen to the number of rows?

A It would increase

B It would stay the same

C It would decrease

D It would become zero

💡 Hint

Think about how 'inner' merge only keeps rows with matching keys, unlike 'outer' merge shown in execution_table step 3.

Concept Snapshot

Combine datasets by merging on a common key column.
Use merge types like 'inner' (only matches) or 'outer' (all rows).
Missing data appears as NaN.
Merging creates a fuller dataset for better analysis.

Full Transcript

We start with two datasets, each with some information and a common column called 'ID'. We create these datasets as data tables. Then, we merge them using the 'ID' column as the key. Using an 'outer' merge means we keep all rows from both datasets. If a row is missing in one dataset, its missing values show as NaN. The combined dataset has more complete information, helping us see the full picture. This process is shown step-by-step in the execution table and variable tracker. Key points include understanding why NaN appears and what 'outer' merge means. Changing merge type affects the number of rows in the result.