Data Merging in Python
Combining multiple datasets.
Data Merging in Python is a free Learn AI with Python lesson on CoddyKit — lesson 4 of 5. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 5 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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Data Merging in Python
In data analysis, combining datasets is a common task. Python provides powerful tools in Pandas to merge, join, and concatenate datasets efficiently. This lesson covers key techniques for merging data in Python.
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Concatenation
Concatenation combines datasets by stacking them along rows or columns. Use the pd.concat() function for this.
Example:
import pandas as pd
data1 = {'Name': ['Alice'], 'Age': [25]}
data2 = {'Name': ['Bob'], 'Age': [30]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
df = pd.concat([df1, df2])3
Merging DataFrames
Merge combines datasets based on common columns or indices. Use pd.merge() to merge DataFrames.
Example:
data1 = {'ID': [1, 2], 'Name': ['Alice', 'Bob']}
data2 = {'ID': [1, 2], 'Age': [25, 30]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
df = pd.merge(df1, df2, on='ID')4
Types of Joins
The merge() function supports different join types:
- Inner Join: Returns rows with matching keys in both DataFrames.
- Outer Join: Returns all rows, filling missing values with NaN.
- Left Join: Keeps all rows from the left DataFrame.
- Right Join: Keeps all rows from the right DataFrame.
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Joining on Multiple Keys
You can merge DataFrames using multiple keys by passing a list of column names to the on parameter.
Example:
pd.merge(df1, df2, on=['Key1', 'Key2'])6
Concatenating Along Columns
You can concatenate DataFrames side-by-side (column-wise) using axis=1.
Example:
df = pd.concat([df1, df2], axis=1)7
Combining Overlapping Data
The combine_first() method combines overlapping data, filling in missing values from another DataFrame.
Example:
df1.combine_first(df2)8
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Recap
In this lesson, you learned about:
- Concatenation: Stacking DataFrames using
pd.concat(). - Merging: Combining DataFrames based on common keys using
pd.merge(). - Join Types: Inner, outer, left, and right joins.
- Combining Overlapping Data: Using
combine_first().
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Congratulations!
You’ve completed the lesson on Data Merging in Python. Continue to the next lesson to learn about Feature Extraction from Data.
Frequently asked questions
Is the “Data Merging in Python” lesson free?
Yes — the full text of “Data Merging in Python” is free to read here on the web, and the Learn AI with Python course includes 5 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.
What will I learn in “Data Merging in Python”?
Combining multiple datasets. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Learn AI with Python?
No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 5, so you can start here or from the beginning and move at your own pace.
How long does the “Data Merging in Python” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Learn AI with Python lesson?
Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Types of Data
- Handling Missing Data
- Data Normalization
- Data Merging in Python
- Feature Extraction from Data