# Merge

> Combine data from multiple datasets into a single dataset.

Source: https://dagster.io/glossary/data-merging

## Data merging definition:

Data merging is the process of combining two or more datasets into a single dataset. It is a critical step in modern data pipelines when working with data from multiple sources or with different formats that need to be merged for analysis.

## Data merging example using Python:

*Please note that you need to* [***have the Pandas library installed in your Python environment***](https://dagster.io/glossary) *to run the following code examples.*

In Python, one of the most popular libraries for merging data are Pandas and Polars. The Pandas library provides several functions for merging data, including **`merge()`** , **`concat()`** , and **`join()`** .

### The merge() function

The **`merge()`** function allows you to merge two data frames on one or more common columns. For example, let's say we have two data frames:`‍`

```python
import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': [1, 2, 3, 4]})
df2 = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': [5, 6, 7, 8]})
```

We can merge these two data frames on the 'key' column like this:`‍`

```python
merged_df = pd.merge(df1, df2, on='key', how='inner')
print(merged_df)
```

For an output of:`‍`

```plaintext
  key  value_x  value_y
0   B        2        5
1   D        4        6
```

### The concat() function

The **`concat()`** function allows you to concatenate two or more data frames vertically or horizontally. For example, let's say we have two data frames:`‍`

```python
df1 = pd.DataFrame({'col1': ['A', 'B', 'C', 'D'], 'col2': [1, 2, 3, 4]})
df2 = pd.DataFrame({'col1': ['E', 'F', 'G', 'H'], 'col2': [5, 6, 7, 8]})
```

We can concatenate these two data frames horizontally like this:`‍`

```python
concatenated_df = pd.concat([df1, df2], axis=1)
print(concatenated_df)
```

This will yield an output of:`‍`

```plaintext
  key  value key  value
0   A      1   B      5
1   B      2   D      6
2   C      3   E      7
3   D      4   F      8
```

### The join() function

The **`join()`** function is similar to **`merge()`** , but it joins two data frames on their index rather than on a column. For example, let's say we have two data frames:`‍`

```python
df1 = pd.DataFrame({'value1': [1, 2, 3, 4]}, index=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame({'value2': [5, 6, 7, 8]}, index=['B', 'D', 'E', 'F'])
```

We can join these two data frames like this:`‍`

```python
joined_df = df1.join(df2, how='inner')
print(joined_df)
```

In this example, we join **`df1`** and **`df2`** on their index and keep only the rows that are present in both data frames (specified by **`how='inner'`** ).

This will yield an output of:`‍`

```plaintext
   value1  value2
B       2       5
D       4       6
```

## Data merge vs. data join vs. data matching

It's worth disambiguating a few terms here as these refer to different ways of combining datasets:

- Data merging combines datasets by simply appending columns from different datasets
- Data join combines datasets based on a common key or column.
- Data matching combines records or data elements from different datasets based on their similarity or matching criteria.

### Data merge vs. data join

As opposed to data merging discussed above, data *join* is a specific type of data merging that involves combining two or more datasets **based on a common column or key**. In a data join, the resulting dataset will only include rows where the key matches in both datasets. This means that any rows that do not have a matching key in both datasets will not be included in the final output. Data join is often used in relational databases, where tables can be joined based on a common key.

Data join is more selective in its output than data merging. Data join only includes rows where the key matches in both datasets, while data merging includes all rows from both datasets.

## Data merge vs. data match

Data *matching* involves comparing two or more datasets to identify and extract common records or data elements. The goal of data matching is to find matching records or data elements in different datasets and combine them into a single, unified record or dataset. Data matching can be done using a variety of methods, including fuzzy matching, exact matching, and probabilistic matching.

While data merging is a relatively straightforward process that involves identifying a common variable or column and combining datasets based on that variable, data matching can be a more complex process that involves comparing multiple datasets and identifying common records or data elements based on complex algorithms and rules.