Add DataFrame.merge() term entry

content/pandas/concepts/dataframe/terms/merge/merge.md

@@ -0,0 +1,117 @@
+---
+Title: '.merge()'
+Description: 'Merges two DataFrames based on a common key or index.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Join'
+  - 'Pandas'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In Pandas, the **`.merge()`** method combines two DataFrames using a common key column or index, similar to a SQL `JOIN` operation. It's essential for integrating datasets that share related fields.
+
+## Syntax
+
+The `.merge()` method provides a flexible way to combine DataFrames using different types of joins. The syntax shows all available parameters:
+
+```pseudo
+DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, suffixes=('_x', '_y'))
+```
+
+**Parameters:**
+
+- `right`: The DataFrame or named Series to merge with.
+- `how`: Type of merge to perform:
+  - `'inner'`: Include only matching rows from both DataFrames.
+  - `'outer'`: Include all rows from both DataFrames, with `NaN`s where no match is found.
+  - `'left'`: Include all rows from the left DataFrame and matching ones from the right.
+  - `'right'`: Include all rows from the right DataFrame and matching ones from the left.
+- `on`: Index level or column names to join on. Must exist in both DataFrames.
+- `left_on`: Column(s) or index level(s) in the left DataFrame to use as join keys.
+- `right_on`: Column(s) or index level(s) in the right DataFrame to use as join keys.
+- `left_index`: Use the index from the left DataFrame as the join key.
+- `right_index`: Use the index from the right DataFrame as the join key.
+- `suffixes`: The suffixes to apply to overlapping column names from the left and right DataFrames.
+
+## Example
+
+This example demonstrates a basic merge operation between two DataFrames using a common `'id'` column:
+
+```py
+import pandas as pd
+
+# Create two sample DataFrames
+df1 = pd.DataFrame({
+  'id': [1, 2, 3, 4],
+  'name': ['Alice', 'Bob', 'Charlie', 'David']
+})
+
+df2 = pd.DataFrame({
+  'id': [1, 2, 3, 5],
+  'age': [25, 30, 35, 40]
+})
+
+# Merge the DataFrames on the 'id' column
+merged_df = df1.merge(df2, on='id')
+
+# Print the merged DataFrame
+print(merged_df)
+```
+
+The code produces this output:
+
+```shell
+   id    name  age
+0   1   Alice   25
+1   2     Bob   30
+2   3  Charlie   35
+```
+
+> **Notes:**
+>
+> - Only rows with matching `id` values (1, 2, and 3) are included in the result.
+> - The row with `id=4` from `df1` is excluded because it has no match in `df2`.
+> - The row with `id=5` from `df2` is excluded because it has no match in `df1`.
+
+## Codebyte Example
+
+This codebyte example demonstrates different types of merges and their effects on the resulting DataFrame:
+
+```codebyte/python
+import pandas as pd
+
+# Create two sample DataFrames
+df1 = pd.DataFrame({
+  'id': [1, 2, 3, 4],
+  'name': ['Alice', 'Bob', 'Charlie', 'David']
+})
+
+df2 = pd.DataFrame({
+  'id': [1, 2, 3, 5],
+  'age': [25, 30, 35, 40]
+})
+
+# Display original DataFrames
+print("Original DataFrames:")
+print("\nDataFrame 1:")
+print(df1)
+print("\nDataFrame 2:")
+print(df2)
+
+# Demonstrate different merge types
+print("\n1. Inner Merge (default):")
+print(df1.merge(df2, on='id'))
+
+print("\n2. Left Merge:")
+print(df1.merge(df2, on='id', how='left'))
+
+print("\n3. Right Merge:")
+print(df1.merge(df2, on='id', how='right'))
+
+print("\n4. Outer Merge:")
+print(df1.merge(df2, on='id', how='outer'))
+```