pandas advanced processing-merge

pandas advanced processing-merge

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If your data consists of multiple tables, sometimes you need to merge different content together for analysis

1 pd.concat realizes data merging

• pd.concat([data1, data2], axis=1)
  • Combine according to row or column, axis=0 is column index, axis=1 is row index

For example, we merge the one-hot encoding we just processed with the original data

# 按照行索引进行
pd.concat([data, dummies], axis=1)

[Here is the data from the previous blog post.]

2 pd.merge

• pd.merge(left, right, how='inner', on=None)
  • You can specify to merge according to the common key-value pair of the two sets of data or to each other
  • left: DataFrame
  • right: Another DataFrame
  • on: Specified common key
  • how: In what way to connect

Merge method	SQL Join Name	Description
left	LEFT OUTER JOIN	Use keys from left frame only
right	RIGHT OUTER JOIN	Use keys from right frame only
outer	FULL OUTER JOIN	Use union of keys from both frames
inner	INNER JOIN	Use intersection of keys from both frames

2.1 pd.merge merge

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                        'key2': ['K0', 'K1', 'K0', 'K1'],
                        'A': ['A0', 'A1', 'A2', 'A3'],
                        'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                        'key2': ['K0', 'K0', 'K0', 'K0'],
                        'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']})

# 默认内连接
result = pd.merge(left, right, on=['key1', 'key2'])

• Left connect

result = pd.merge(left, right, how='left', on=['key1', 'key2'])

• Right connection

result = pd.merge(left, right, how='right', on=['key1', 'key2'])

• External link

result = pd.merge(left, right, how='outer', on=['key1', 'key2'])

3 summary

• pd.concat([data1, data2], axis=**)
• pd.merge(left, right, how=, on=)
  • how - how to connect
  • on - What are the basis of the connected keys