Python-从csv中选择不同的行值，并将其合并到新的csv中 [英] Python - Select different row values from csv and combine them in new csv

问题描述

我有一个

下面的代码选择1行测量的正确行：

  df = pd.read_csv（csv，标头=无，名称= [调查，时间，潮汐， mwp， swh， mwd， data1， data2， data3， data4， data5 ]）
 xp = [datetime.strptime（d，％d /％m /％YT％H：％M）for d in df ['time']] 
 
 xs = mdates.date2num（xp）
日期= mdates.DateFormatter（％d /％m /％Y\n％H：％M）
 
＃选择行数据波
 survey01 = 26/03 / 2019T14：00 
 survey02 = 10/04 / 2019T14：00 
 survey03 = 11/04 / 2019T15：00 
 survey04 =  01/05 / 2019T09：00 
 
＃选择行数据波
 selected_survey = df.loc [df [ time]。eq（survey01）]。index [0] 
 wave = df.loc [selected_survey-6：selected_wave， time]。index [0] 
 result_wave = df.loc [wave，['survey'，'time'，'tides'，'mwp'，'swh'，'mwd']] 
 meas = df.loc [selected_survey：selected_meas，时间 ] .index [0] 
 result_meas = df.loc [meas，['data1'，'data2'，'data3'，'data4'，'data5']] 
 
 #Join他们在一起
 join_list = [] 
 join_list.extend（result_wave）
 join_list.extend（result_meas）
 print（joined_list）
 
＃导出到csv 
 data = pd.DataFrame（list（zip（*（[joined_list]）））。add_prefix（'Survey1'）
 data.to_csv（'Waves.csv'，index = False）
 print（data）
  

应该对所有测量（总计20个以上）进行此操作，并将其合并1个csv

我如何为所有这些对象执行此操作并将其导出到一个csv文件中？

 调查1 26/03 / 2019T08：00 1.2 9.34 0.509 1.080 25.5 18.4 31.64 27.3 24.2 
调查2 10/04 / 2019T08：00 1.1 8.06 1.232 1.155 24.64 19.46 31.844 28.83 25.357 
调查3 ... 
  

一种在csv文件中获取正确数据的简便方法？

解决方案

我无法完全理解代码。但是，如评论中所述，您可以使用 apply（）获得所需的结果。

  def process_data（i）：
 selected_survey = df.loc [df [ time]。eq（i）]。index [0] 
 wave = df.loc [selected_survey-3：selected_wave， time]。index [0] 
 result_wave = df.loc [wave，['survey'，'time'，'tides'，'mwp'，'swh'，' mwd']] 
 meas = df.loc [selected_survey：selected_meas， time]。index [0] 
 result_meas = df.loc [meas，['data1'，'data2'，'data3 '，'data4'，'data5']]] 
 
 join_list = [] 
 join_list.extend（result_wave）
 join_list.extend（result_meas）
 return join_list 
 
 join_list = df [ time]。apply（process_data）
 
 survey_index_list = [f'survey {i}'for i in range（len（joined_list））] 
 data = pd.DataFrame（list（zip（*（joind_list]）），index = survey_index_list）
打印（数据）
  

I have a csv file containing hourly data of wave conditions and data from measurements taken during certain times. I want to select wave conditions 6 hours before the measurement and the outcomes of the measurements. I want to export that to a new csv file for all the measurements.

The code below selects the right rows for 1 measurement:

df = pd.read_csv(csv, header=None, names=['survey', 'time', 'tides', 'mwp', 'swh', 'mwd', 'data1', 'data2', 'data3', 'data4', 'data5'])
xp = [datetime.strptime(d, "%d/%m/%YT%H:%M") for d in df['time']]

xs = mdates.date2num(xp)
date = mdates.DateFormatter ("%d/%m/%Y\n%H:%M")

#select row data waves
survey01 = "26/03/2019T14:00"
survey02 = "10/04/2019T14:00"
survey03 = "11/04/2019T15:00"
survey04 = "01/05/2019T09:00"

#Select row data waves
selected_survey = df.loc[df["time"].eq(survey01)].index[0]
wave = df.loc[selected_survey-6: selected_wave, "time"].index[0]
result_wave = df.loc[wave, ['survey', 'time', 'tides', 'mwp', 'swh', 'mwd']]
meas = df.loc[selected_survey: selected_meas, "time"].index[0]
result_meas = df.loc[meas, ['data1', 'data2', 'data3', 'data4', 'data5']]

#Join them together
joined_list = []
joined_list.extend (result_wave)
joined_list.extend (result_meas)
print (joined_list)

#Export to csv
data = pd.DataFrame(list(zip(*[joined_list]))).add_prefix('Survey1')
data.to_csv('Waves.csv', index=False)
print(data)

This should be done for all the measurements (20+ in total) and combined in 1 csv

How do I do this for all of them and export it to one csv file?

survey 1  26/03/2019T08:00  1.2 9.34    0.509   1.080  25.5  18.4  31.64    27.3    24.2
survey 2  10/04/2019T08:00  1.1 8.06    1.232   1.155  24.64 19.46 31.844   28.83   25.357
survey 3  ...

Or is there an easier way of getting the right data in a csv file?

解决方案

I wasn't able to comprehend the code completely. However, as discussed in the comments, you can use the apply() to get the required results.

def process_data(i):
    selected_survey = df.loc[df["time"].eq(i)].index[0]
    wave = df.loc[selected_survey-3: selected_wave, "time"].index[0]
    result_wave = df.loc[wave, ['survey', 'time', 'tides', 'mwp', 'swh', 'mwd']]
    meas = df.loc[selected_survey: selected_meas, "time"].index[0]
    result_meas = df.loc[meas, ['data1', 'data2', 'data3', 'data4', 'data5']]

    joined_list = []
    joined_list.extend (result_wave)
    joined_list.extend (result_meas)
    return joined_list

joined_list = df["time"].apply(process_data)

survey_index_list = [f'survey{i}' for i in range(len(joined_list))]
data = pd.DataFrame(list(zip(*[joined_list])), index=survey_index_list)
print(data)

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