python - Pandas - 添加一列，其值是根据当前行和上一行中的另一列值计算的

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colNames = ["Time","Col2","Col3","Col4","Col5","Col6","Col7","Col8","Col9","Col10","Col11","Col12","Col13"]
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df = pd.DataFrame(colVals, columns=colNames)
df = df.set_index('Time')
df = df.apply(pd.to_numeric, errors='coerce')

需要添加新的Col14，使其从0开始，每当当前行Col11中的值小于Col11中的值时加1上一行的 - 如果上一行的 Col11 值为 NaN，则不应增加 Col14。

例如

+-------+-------+-------------------------------------------------------------------------------------------------+
| Col11 | Col14 |                                                                                                 |
+-------+-------+-------------------------------------------------------------------------------------------------+
| 900   |     0 | start with 0                                                                                    |
| NaN   |     0 |                                                                                                 |
| 900   |     0 |                                                                                                 |
| 903   |     0 |                                                                                                 |
| 904   |     0 |                                                                                                 |
| 904   |     0 |                                                                                                 |
| 8     |     1 | increment Col14 by 1 when current row's Col11 value is less than the previous row's Col11 value |
| 8     |     1 |                                                                                                 |
| 200   |     1 |                                                                                                 |
| 201   |     1 |                                                                                                 |
| NaN   |     1 | if Col11 is NaN, Col12 takes same value as prev row                                                                               |
| 0     |     2 | increment Col14 by 1 when current row's Col11 value is less than the previous row's Col11 value |
| 1     |     2 |                                                                                                 |
| NaN   |     2 |                                                                                                 |
| NaN   |     2 |                                                                                                 |
| 0     |     3 | increment Col14 by 1 when current row's Col11 value is less than the previous row's Col11 value |
+-------+-------+-------------------------------------------------------------------------------------------------+

有没有办法获取上一行的引用？

Answer 1

你能试试这个吗？

df['col14']=df.Col11.gt(df.Col11.shift(-1)).cumsum()

或

df['col14a']=df.Col11.gt(df.Col11.shift(-1)).shift().fillna(0).cumsum().astype(int)

两者之间的区别在于，计数在较低值结束时切换(第一个值)，而在新值开始时切换(第二个值)

如果您想将 NaN 视为与之前的非 Nan 值相同，请使用下面的代码。

df['col14b']=df.Col11.fillna(method='ffill').gt(df.Col11.fillna(method='ffill').shift(-1)).shift().fillna(0).cumsum().astype(int)

使用您提供的较小的虚拟数据集，输出如下

Col11   col14   col14a  col14b
0   900.0   0   0   0
1   NaN     0   0   0
2   900.0   0   0   0
3   900.0   0   0   0
4   903.0   0   0   0
5   904.0   0   0   0
6   904.0   1   0   0
7   8.0     1   1   1
8   8.0     1   1   1
9   200.0   1   1   1
10  201.0   1   1   1
11  NaN     1   1   1
12  0.0     1   1   2
13  1.0     1   1   2
14  NaN     1   1   2
15  NaN     1   1   2
16  0.0     1   1   3

详细信息 我这里解释的是最后一个，其他两个与此类似。

与 df.Col11.fillna(method='ffill')我们用以前的有效值填充 NaN，所以输出如下(我们没有更改 Coll11，我们只是在创建新列时使用它)

0     900.0
1     900.0
2     900.0
3     900.0
4     903.0
5     904.0
6     904.0
7       8.0
8       8.0
9     200.0
10    201.0
11    201.0
12      0.0
13      1.0
14      1.0
15      1.0
16      0.0

与 df.Col11.fillna(method='ffill').shift(-1)我们只是向上移动 1 行，以便我们可以在该行与上一行之间进行比较。

0     900.0
1     900.0
2     900.0
3     903.0
4     904.0
5     904.0
6       8.0
7       8.0
8     200.0
9     201.0
10    201.0
11      0.0
12      1.0
13      1.0
14      1.0
15      0.0
16      NaN

与 df.Col11.fillna(method='ffill').gt(df.Col11.fillna(method='ffill').shift(-1)) ，我们进行比较，结果为真或为假，如下。

0     False
1     False
2     False
3     False
4     False
5     False
6      True
7     False
8     False
9     False
10    False
11     True
12    False
13    False
14    False
15     True
16    False

我们使用shift将值向下移动一行，因为您希望在出现较小数字时切换数字。当我们向下移动行时，第一行变成 NaN ，我们使用 .fillna(0) 将其替换为 0(因为我们必须从零开始) 。然后我们使用 .cumsum() 。 .cumsum()仅添加 True这样，我们就得到了我们需要的值。最后，我们将此列指定为整数 .astype(int)使所有值都为整数。