[英]Pandas Dataframe inefficient for loop through columns
我有每个单元格和日期的降水数据(1800行和15k列)。
486335 486336 486337
2019-07-03 13:35:54.445 0 2 22
2019-07-04 13:35:54.445 0 1 1
2019-07-05 13:35:54.445 16 8 22
2019-07-06 13:35:54.445 0 0 0
2019-07-07 13:35:54.445 0 11 0
我想找到达到特定降雨量(> 15毫米)的日期,并计算此事件发生后降雨量少(<1,1毫米)的天数。 连同雨量,开始和结束时段,单元格和其他信息存储在新的DataFrame中。
我编写了一个执行此工作的for循环但是花了几天才完成;(。我是python的初学者,所以也许有其他方法的一些提示。
from datetime import datetime, timedelta, date
import datetime
import pandas as pd
#Existing Data
index_dates = pd.date_range(pd.datetime.today(), periods=10).tolist()
df = pd.DataFrame({'486335':[0,0,16,0,0,0,2,1,8,2],'486336':[2,1,8,0,11,16,0,1,6,8],'486337':[22,1,22,0,0,0,5,3,6,1]},index=index_dates)
columns = df.columns
counter_columns = 0
iteration = -1 #Iterations Steps
counter = 10 #10 precipitation values per column
duration = 0 #days with no or less than pp_max_1 rain
count = False
index_list = df.index #Index for updating df / Integear
period_range = 0 #Amount of days after Event without much rain Integear
period_amount = 0 #Amount of PP in dry days except event Integear
event_amount = 0.0 #Amount of heavy rainfall on the event date Float
pp = 0 #actual precipitation
pp_sum = 0.0 #mm
pp_min = 15.0 #mm min pp for start to count dry days until duration_min_after
pp_max_1 = 0.11 #max pp for 1 day while counting dry days
dry_days = 0 #dry days after event
for x in df:
for y in df[x]:
iteration = iteration + 1
if iteration == counter:
iteration = 0
counter_columns = counter_columns + 1
print("column :",counter_columns, "finished")
if y >= pp_min and count == False:
duration = duration + 1
count = True
start_period = index_list[iteration]
event_amount = y
index = iteration
pp_sum = pp_sum + y
elif y >= pp_min and count == True or y >= pp_max_1 and count == True:
end_period = index_list[iteration]
dry_periods = dry_periods.append({"start_period":start_period ,"end_period":end_period,"period_range":duration,"period_amount":pp_sum ,"event_amount":event_amount, "cell":columns[counter_columns]},ignore_index=True).sort_values('period_range',ascending=False)
duration = 0
count = False
pp_sum = 0
elif pp <= pp_max_1 and count == True:
duration = duration + 1
pp_sum = pp_sum + y
else:
continue
print(dry_periods)
输出看起来像这样
start_period end_period period_range \
0 2019-07-05 13:15:05.545 2019-07-09 13:15:05.545 4
1 2019-07-05 13:15:05.545 2019-07-09 13:15:05.545 4
2 2019-07-05 13:15:36.569 2019-07-09 13:15:36.569 4
3 2019-07-05 13:15:36.569 2019-07-09 13:15:36.569 4
4 2019-07-05 13:16:16.372 2019-07-09 13:16:16.372 4
5 2019-07-05 13:16:16.372 2019-07-09 13:16:16.372 4
period_amount event_amount cell
0 16.0 16 486335
1 22.0 22 486337
2 16.0 16 486335
3 22.0 22 486337
4 16.0 16 486335
5 22.0 22 486337
您可以避免对行进行迭代,因为它不适合大型数据帧。
这是一种不同的方法,不确定它对您的完整数据帧是否更有效:
periods=[]
for cell in df.columns:
sub = pd.DataFrame({'amount': df[cell].values}, index=df.index)
sub['flag'] = pd.cut(sub['amount'], [0.11, 15, np.inf],
labels=[0, 1]).astype(np.float)
sub.loc[sub.flag>0, 'flag']=sub.loc[sub.flag>0, 'flag'].cumsum()
sub.flag.ffill(inplace=True)
x = sub[sub.flag>0].reset_index().groupby('flag').agg(
{'index':['min', 'max'], 'amount': 'sum'})
x.columns = ['start', 'end', 'amount']
x['period_range'] = (x.end - x.start).dt.days + 1
x['cell'] = cell
x.reindex(columns=['start', 'end', 'period_range', 'cell'])
periods.append(x)
resul = pd.concat(periods).reset_index(drop=True)
因为我没有你的整个数据集我真的不能说消耗时间是什么,但我想这是因为索引访问,当你获取你在循环中执行的句点和排序操作。 也许您想尝试以下代码。 它应该在逻辑上等同于您的代码,除了一些更改:
duration = 0 #days with no or less than pp_max_1 rain
count = False
index_list = df.index #Index for updating df / Integear
period_range = 0 #Amount of days after Event without much rain Integear
period_amount = 0 #Amount of PP in dry days except event Integear
event_amount = 0.0 #Amount of heavy rainfall on the event date Float
pp = 0 #actual precipitation
pp_sum = 0.0 #mm
pp_min = 15.0 #mm min pp for start to count dry days until duration_min_after
pp_max_1 = 0.11 #max pp for 1 day while counting dry days
dry_days = 0 #dry days after event
dry_periods= list()
for counter_columns, column in enumerate(df.columns, 1):
for period, y in df[column].items():
if not count and y >= pp_min:
duration += 1
count = True
start_period = period
event_amount = y
pp_sum += y
elif count and (y >= pp_min or y >= pp_max_1):
end_period = period
dry_periods.append({
"start_period": start_period ,
"end_period": end_period,
"period_range": duration,
"period_amount": pp_sum ,
"event_amount": event_amount,
"cell": column})
duration = 0
count = False
pp_sum = 0
elif count and pp <= pp_max_1:
duration += 1
pp_sum += y
print("column :",counter_columns, "finished")
dry_periods.sort(key=lambda record: record['period_range'])
print(dry_periods)
变化是:
顺便说一句。 因为我不知道dry_periods是如何精确定义的,所以我只是将它用作列表。 还请看看情况
elif count and (y >= pp_min or y >= pp_max_1):
以上。 它看起来很可疑,但它只是你程序中的重写条件。 如果没问题,可能你可以删除其中一个比较,因为我猜pp_min <pp_max_1,对吗?
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