我有一个自定义函数,它返回一个 dict
并将其存储到每行的每个单元格中:
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})
def custom_rolling_apply(arr):
return {'sum': np.sum(arr), 'mean': np.mean(arr)}
df['rolling_dict'] = np.NaN
df['rolling_dict'] = df['rolling_dict'].astype('object')
df['rolling_dict'] = df['A'].rolling(window=3).apply(custom_rolling_apply, raw=True)
为什么这样说:
TypeError: must be real number, not dict
pandas
版本:1.5.3
最佳答案
您应该使用rolling.aggregate
而不是应用
;
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})
df['rolling_dict'] = np.NaN
df['rolling_dict'] = df['rolling_dict'].astype('object')
df['A'].rolling(window=3).aggregate({'sum': np.sum, 'mean': np.mean}, raw=True)
输出
sum mean
0 NaN NaN
1 NaN NaN
2 6.0 2.0
3 9.0 3.0
4 12.0 4.0
来自rolling.apply
documentation :
func function Must produce a single value from an
ndarray
input ifraw=True
or a single value from aSeries
ifraw=False
. Can also accept a Numba JIT function withengine='numba'
specified
请注意,如果数据很大,apply
会带来性能损失:
import numpy as np
import timeit
import matplotlib.pyplot as plt
import pandas as pd
def custom_rolling_apply(arr):
q={'sum':np.sum(arr), 'mean': np.mean(arr)}
return q
def rolling_with_aggregate(arr):
q=arr.rolling(window=3).aggregate({'sum': np.sum, 'mean': np.mean}, raw=True)
return q
def profile_rolling_operation(data_size):
rolling_times_1 = []
rolling_times_2 = []
data_sizes = []
for i in range(1, data_size + 1):
data_sizes.append(i)
df = pd.DataFrame({'A': np.random.randint(1, 10, i)})
elapsed_time_1 = timeit.timeit(lambda: [custom_rolling_apply(arr) for arr in df['A'].rolling(window=3)], number=2)
rolling_times_1.append(elapsed_time_1)
elapsed_time_2 = timeit.timeit(lambda: rolling_with_aggregate(df['A']), number=2)
rolling_times_2.append(elapsed_time_2)
return data_sizes, rolling_times_1, rolling_times_2
max_data_size = 1000
data_sizes, rolling_times_1, rolling_times_2 = profile_rolling_operation(max_data_size)
plt.plot(data_sizes, rolling_times_1, label='Custom Rolling Apply')
plt.plot(data_sizes, rolling_times_2, label='Rolling with Aggregate')
plt.xlabel('Data Size')
plt.ylabel('Execution Time (seconds)')
plt.title('Comparison')
plt.legend()
plt.show()
关于pandas - 滚动应用返回字典,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/76613293/