熊猫数据框效率低下的列循环

时间：2019-07-03 11:35:14

标签： python pandas dataframe for-loop if-statement

每个单元格和日期都有降水数据（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

我想查找达到特定降雨量（> 15mm）的日期，并计算该事件发生后的天数少雨（<1.1mm）。连同雨量，开始和结束时间，单元格和其他信息一起存储在新的DataFrame中。

我编写了一个for循环来完成这项工作，但是花了几天的时间才能完成;（。我是python的初学者，所以也许有一些其他方法的技巧。

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

2 个答案:

答案 0 :(得分：3)

您可以避免在行上进行迭代，因为它不适用于大型数据框。

这是另一种方法，不确定对于您的完整数据框是否会更有效：

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)

答案 1 :(得分：2)

因为我没有您的全部数据集，所以我无法真正说出消耗时间的时间，但是我想这是由于索引访问，获取周期中执行的周期和排序操作所致。也许您想尝试以下代码。 在逻辑上，它应该与您的代码等效，除了一些更改之外：

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)

更改为：

  • 删除了index_list [iteration]访问，我认为这可能会花费一些时间
  • 删除了整个迭代计数器逻辑，因为与之关联的逻辑可以放在内部循环之外，这样内部循环就可以变小，尽管它实际上并不能真正提高性能
  • 比较计数==不需要为True，您可以只在if子句中写入计数
  • 将增量和求和逻辑从var = var + num更改为var + = num（这可能是个问题，您也可以跳过此操作，因为它不会对性能产生太大影响）
  • 然后我将您的dry_periods排序逻辑放在循环之外，因为在我看来，您的循环逻辑并不依赖于要排序的集合->也许这甚至对性能产生最大影响

顺便说一句。因为我不知道dry_periods的定义方式，所以我只是将其用作列表。请看看情况

elif count and (y >= pp_min or y >= pp_max_1):

以上。在我看来，这很可疑，但这只是您程序中的重写条件。如果可以，您可以删除其中一个比较，因为我猜是pp_min

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