使用 merge_ordered()

使用 pandas 连接数据

Aaren Stubberfield

Instructor

merge_ordered()

merge_ordered() 的表格示例

方法对比

.merge() 方法：

连接列
- on、left_on、right_on
连接类型
- how（left、right、inner、outer）{{@}}
- 默认：inner
重名列
- suffixes
调用方式
- df1.merge(df2)

merge_ordered() 方法：

连接列
- on、left_on、right_on
连接类型
- how（left、right、inner、outer）
- 默认：outer
重名列
- suffixes
调用方式
- pd.merge_ordered(df1, df2)

金融数据集

带道琼斯指数图的报纸图片

¹ Photo by Markus Spiske on Unsplash

股票数据

表名：aapl

  date        close    
0 2007-02-01  12.087143
1 2007-03-01  13.272857
2 2007-04-01  14.257143
3 2007-05-01  17.312857
4 2007-06-01  17.434286

表名：mcd

  date        close    
0 2007-01-01  44.349998
1 2007-02-01  43.689999
2 2007-03-01  45.049999
3 2007-04-01  48.279999
4 2007-05-01  50.549999

合并股票数据

import pandas as pd
pd.merge_ordered(aapl, mcd, on='date', suffixes=('_aapl','_mcd'))

  date        close_aapl  close_mcd
0 2007-01-01  NaN         44.349998
1 2007-02-01  12.087143   43.689999
2 2007-03-01  13.272857   45.049999
3 2007-04-01  14.257143   48.279999
4 2007-05-01  17.312857   50.549999
5 2007-06-01  17.434286   NaN

前向填充（forward fill）

前向填充的前后对比图。列中的缺失值被上一有效值填充。

前向填充示例

pd.merge_ordered(aapl, mcd, on='date', 
                 suffixes=('_aapl','_mcd'), 
                 fill_method='ffill')

  date        close_aapl  close_mcd
0 2007-01-01  NaN         44.349998
1 2007-02-01  12.087143   43.689999
2 2007-03-01  13.272857   45.049999
3 2007-04-01  14.257143   48.279999
4 2007-05-01  17.312857   50.549999
5 2007-06-01  17.434286   50.549999

pd.merge_ordered(aapl, mcd, on='date', 
                 suffixes=('_aapl','_mcd'))

  date        close_aapl  close_mcd
0 2007-01-01  NaN         44.349998
1 2007-02-01  12.087143   43.689999
2 2007-03-01  13.272857   45.049999
3 2007-04-01  14.257143   48.279999
4 2007-05-01  17.312857   50.549999
5 2007-06-01  17.434286   NaN

何时使用 merge_ordered()？

有序数据 / 时间序列
填充缺失值

Vamos praticar!

使用 pandas 连接数据