How to Perform Different Types of Joins Using Pandas Merge?

Pandas Merging 101

Merging Basics - Basic Types of Joins

Pandas merge functionality offers different types of joins:

INNER JOIN

• Represented by below diagram:
  [Image of an INNER JOIN graphic]

• Use left.merge(right, on='key') to perform an INNER JOIN.

  • Example:

    left = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)})
    right = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': np.random.randn(4)})
    
    left.merge(right, on='key')
    # Output:
    #   key   value_x   value_y
    # 0   B  0.400157  1.867558
    # 1   D  2.240893 -0.977278

LEFT OUTER JOIN

• Represented by below diagram:
  [Image of a LEFT OUTER JOIN graphic]

• Use left.merge(right, on='key', how='left') to perform a LEFT OUTER JOIN.

  • Example:

    left.merge(right, on='key', how='left')
    # Output:
    #   key   value_x   value_y
    # 0   A  1.764052       NaN
    # 1   B  0.400157  1.867558
    # 2   C  0.978738       NaN
    # 3   D  2.240893 -0.977278

RIGHT OUTER JOIN

• Represented by below diagram:
  [Image of a RIGHT OUTER JOIN graphic]

• Use left.merge(right, on='key', how='right') to perform a RIGHT OUTER JOIN.

  • Example:

    left.merge(right, on='key', how='right')
    # Output:
    #   key   value_x   value_y
    # 0   B  0.400157  1.867558
    # 1   D  2.240893 -0.977278
    # 2   E       NaN  0.950088
    # 3   F       NaN -0.151357

FULL OUTER JOIN

• Represented by below diagram:
  [Image of a FULL OUTER JOIN graphic]

• Use left.merge(right, on='key', how='outer') to perform a FULL OUTER JOIN.

  • Example:

    left.merge(right, on='key', how='outer')
    # Output:
    #   key   value_x   value_y
    # 0   A  1.764052       NaN
    # 1   B  0.400157  1.867558
    # 2   C  0.978738       NaN
    # 3   D  2.240893 -0.977278
    # 4   E       NaN  0.950088
    # 5   F       NaN -0.151357

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