我有一个包含气象数据的数据框，每行是一个位置一天的数据。我想计算3天的平均值并将它们添加为列。自然（至少对我来说）这样做的方法是使用df.apply;但它很慢并且占用大量内存（目前使用大约3Gb的内存，然后上升）。我的函数如下所示:(合并的是数据框，它按行号索引）
def three_day_stats(row):
total_snowfall = 0
total_sunshine = 0
mean_wind = 0
mean_temp = 0
days = range(max(0, row.name-3), row.name+1)
for i in days:
day = merged.loc[i]
total_snowfall += day['Snowfall']
total_sunshine += day['Sunshine duration']
mean_wind += (1/len(days))*(day['10 metre U wind component']**2 + day['10 metre V wind component']**2)**0.5
mean_temp += (1/len(days))*day['2 metre temperature']
return pd.Series({'3 day snowfall': total_snowfall,
'3 day sunshine': total_sunshine,
'3 day wind': mean_wind,
'3 day temperature': mean_temp})
有没有办法在不使用申请的情况下执行此操作？或者至少使其更有效？
编辑：一行数据
10 metre U wind component 2.13432
10 metre V wind component -0.932907
2 metre temperature 3.88357
Date 1996-11-01 00:00:00
Latitude 46.3975
Longitude 7.8515
Snow density 269.103
Snow depth 0.000514924
Snowfall 0
Sunshine duration 2.87365
Temperature of snow layer -0.677888
winter 2015/16
canton VS
community Baltschieder
elevation 3440
aspect_string E
Avalanche 0
Name: 0, dtype: object
答案 0 :(得分：1)
您可以rolling使用aggregate总和，并且首先创建列3 day wind：
np.random.seed(100)
start = pd.to_datetime('2015-02-24')
rng = pd.date_range(start, periods=10)
cols = ['Snowfall','Sunshine duration','10 metre U wind component','10 metre V wind component','2 metre temperature']
merged = pd.DataFrame(np.random.randint(10,size=(10,5)), columns=cols, index=rng).reset_index()
print (merged)
index Snowfall Sunshine duration 10 metre U wind component \
0 2015-02-24 8 8 3
1 2015-02-25 0 4 2
2 2015-02-26 2 2 1
3 2015-02-27 4 0 9
4 2015-02-28 4 1 5
5 2015-03-01 4 3 7
6 2015-03-02 7 7 0
7 2015-03-03 9 3 2
8 2015-03-04 1 0 7
9 2015-03-05 0 8 2
10 metre V wind component 2 metre temperature
0 7 7
1 5 2
2 0 8
3 6 2
4 3 4
5 1 1
6 2 9
7 5 8
8 6 2
9 5 1
merged['3 day wind'] = (merged['10 metre U wind component']** 2 +
merged['10 metre V wind component']** 2)**0.5
df = merged.rolling(4, min_periods=1).agg({'Snowfall': 'sum',
'Sunshine duration':'sum',
'2 metre temperature':'mean',
'3 day wind':'mean'})
d = {"Snowfall":"3 day snowfall",
"Sunshine duration":"3 day sunshine",
"2 metre temperature":"2 metre temperature"}
df = df.rename(columns = d)
print (df)
3 day wind 3 day sunshine 3 day snowfall 2 metre temperature
0 7.615773 8.0 8.0 7.000000
1 6.500469 12.0 8.0 4.500000
2 4.666979 14.0 10.0 5.666667
3 6.204398 14.0 14.0 4.750000
4 5.758193 7.0 10.0 4.000000
5 6.179668 6.0 14.0 3.750000
6 6.429668 11.0 19.0 4.000000
7 5.071796 14.0 24.0 5.500000
8 5.918944 13.0 21.0 5.000000
9 5.497469 18.0 17.0 5.000000