与数据帧中的前一行相比，如何识别一行中的字符串变化？

问题描述

我有一个来自熊猫的 DataFrame ：

import pandas as pd
inp = [{'Name': 'John', 'Year':2018, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2018, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2019, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2019, 'Address':'Orange county'}, {'Name': 'John', 'Year':2019, 'Address':'New York'}, {'Name': 'Steve', 'Year':2018, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2019, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2019, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2020, 'Address':'California'}, {'Name': 'Steve', 'Year':2020, 'Address':'Canada'}]
df = pd.DataFrame(inp)
print (df)

如果与上一行相比，行的字符串值发生了变化，我想在单独的行 Cng-Address中进行标识，并且如果行的数值发生变化，则在 Cng-Year列中进行标识。如果没有更改，则将其标识为零。

If a change in a row's string value occurs comparing to previous row, I want to identify it in a separate row "Cng-Address", and if row's numeric value changes identify it in "Cng-Year" column. If there is no change identify it as zero.

索引为名称，这意味着应对与人员姓名相关的所有行进行上述计算。如果名称更改了（例如，约翰更改为史蒂夫），则应重置 Cng-Address和 Cng-Year的计算。列年份按升序排列。

The index is "Name" meaning that the above calculations should be done for all rows associated to person name. If a "Name" changes (i.e. John to Steve) then calculations for "Cng-Address" and "Cng-Year" should reset. Column year sorted ascending.

作为最终报告，我想获得：

As a final report I want to get:

• 约翰更改年份 1次并更改位置 2次


• 史蒂夫更改年份 2次并更改位置 2次


• 2019年的总更改地址是 2次

当前输出：

+-------+------+---------------+
| Name  | Year | Address       |
+-------+------+---------------+
| John  | 2018 | Beverly hills |
+-------+------+---------------+
| John  | 2018 | Beverly hills |
+-------+------+---------------+
| John  | 2019 | Beverly hills |
+-------+------+---------------+
| John  | 2019 | Orange county |
+-------+------+---------------+
| John  | 2019 | New York      |
+-------+------+---------------+
| Steve | 2018 | Canada        |
+-------+------+---------------+
| Steve | 2019 | Canada        |
+-------+------+---------------+
| Steve | 2019 | Canada        |
+-------+------+---------------+
| Steve | 2020 | California    |
+-------+------+---------------+
| Steve | 2020 | Canada        |
+-------+------+---------------+

理想的输出：

+-------+------+---------------+----------+-------------+
| Name  | Year | Address       | Cng-Year | Cng-Address |
+-------+------+---------------+----------+-------------+
| John  | 2018 | Beverly hills | 0        | 0           |
+-------+------+---------------+----------+-------------+
| John  | 2018 | Beverly hills | 0        | 0           |
+-------+------+---------------+----------+-------------+
| John  | 2019 | Beverly hills | 1        | 0           |
+-------+------+---------------+----------+-------------+
| John  | 2019 | Orange county | 0        | 1           |
+-------+------+---------------+----------+-------------+
| John  | 2019 | New York      | 0        | 1           |
+-------+------+---------------+----------+-------------+
| Steve | 2018 | Canada        | 0        | 0           |
+-------+------+---------------+----------+-------------+
| Steve | 2019 | Canada        | 1        | 0           |
+-------+------+---------------+----------+-------------+
| Steve | 2019 | Canada        | 0        | 0           |
+-------+------+---------------+----------+-------------+
| Steve | 2020 | California    | 1        | 1           |
+-------+------+---------------+----------+-------------+
| Steve | 2020 | Canada        | 0        | 1           |
+-------+------+---------------+----------+-------------+

推荐答案

你可以用groupby来做：

YOu can do with groupby:

groups = df.groupby('Name')

for col in ['Year', 'Address']:
    df[f'cng-{col}'] = groups[col].shift().fillna(df[col]).ne(df[col]).astype(int)

输出：

    Name  Year        Address  cng-Year  cng-Address
0   John  2018  Beverly hills         0            0
1   John  2018  Beverly hills         0            0
2   John  2019  Beverly hills         1            0
3   John  2019  Orange county         0            1
4   John  2019       New York         0            1
5  Steve  2018         Canada         0            0
6  Steve  2019         Canada         1            0
7  Steve  2019         Canada         0            0
8  Steve  2020     California         1            1
9  Steve  2020         Canada         0            1

这篇关于与数据帧中的前一行相比，如何识别一行中的字符串变化？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！