在数据框中,我想算一下过去10天的价格中有多少比今天的价格高。结果如下所示:
price ct>prev10
50.00
51.00
52.00
50.50
51.00
50.00
50.50
53.00
52.00
49.00
51.00 3
我已经看到DSM回答了这篇文章,但要求的不同之处在于比较基数是静态数字而不是当前行:
Achieving "countif" with pd.rolling_sum()
当然我想在没有循环1x1的情况下这样做。非常难过 - 提前感谢任何建议。
答案 0 :(得分:4)
您可以在系列中使用rolling_apply
功能。考虑到样本数据的小尺寸,我使用了5的窗口长度,但您可以轻松地更改它。
lambda
函数计算滚动组中的项目数(不包括最后一项)大于最后一项。
df = pd.DataFrame({'price': [50, 51, 52, 50.5, 51, 50, 50.5, 53, 52, 49, 51]})
window = 5 # Given that sample data only contains 11 values.
df['price_count'] = pd.rolling_apply(df.price, window,
lambda group: sum(group[:-1] > group[-1]))
>>> df
price price_count
0 50.0 NaN
1 51.0 NaN
2 52.0 NaN
3 50.5 NaN
4 51.0 1
5 50.0 4
6 50.5 2
7 53.0 0
8 52.0 1
9 49.0 4
10 51.0 2
在上面的示例中,第一组是索引值为0-4的价格。你可以看到发生了什么:
group = df.price[:window].values
>>> group
array([ 50. , 51. , 52. , 50.5, 51. ])
现在,将前四个价格与当前价格进行比较:
>>> group[:-1] > group[-1]
array([False, False, True, False], dtype=bool)
然后,你只是总结布尔值:
>>> sum(group[:-1] > group[-1])
1
这是放入索引4的第一个关闭窗口的值。
答案 1 :(得分:1)
这是一个带NumPy
模块的vectoized方法,支持broadcasting
实现矢量化方法 -
import numpy as np
import pandas as pd
# Sample input dataframe
df = pd.DataFrame({'price': [50, 51, 52, 50.5, 51, 50, 50.5, 53, 52, 49, 51]})
# Convert to numpy array for counting purposes
A = np.array(df['price'])
W = 5 # Window size
# Initialize another column for storing counts
df['price_count'] = np.nan
# Get counts and store as a new column in dataframe
C = (A[np.arange(A.size-W+1)[:,None] + np.arange(W-1)] > A[W-1:][:,None]).sum(1)
df['price_count'][W-1:] = C
示例运行 -
>>> df
price
0 50.0
1 51.0
2 52.0
3 50.5
4 51.0
5 50.0
6 50.5
7 53.0
8 52.0
9 49.0
10 51.0
>>> A = np.array(df['price'])
>>> W = 5 # Window size
>>> df['price_count'] = np.nan
>>>
>>> C=(A[np.arange(A.size-W+1)[:,None] + np.arange(W-1)] > A[W-1:][:,None]).sum(1)
>>> df['price_count'][W-1:] = C
>>> df
price price_count
0 50.0 NaN
1 51.0 NaN
2 52.0 NaN
3 50.5 NaN
4 51.0 1
5 50.0 4
6 50.5 2
7 53.0 0
8 52.0 1
9 49.0 4
10 51.0 2