在Python

时间：2017-01-12 20:37:46

标签： python csv

我有一个CSV文件，其中包含多天的每一分钟的行数。它由数据采集系统生成，有时会错过几行。

数据看起来像这样 - 日期时间字段后跟一些整数

"2017-01-07 03:00:02","7","3","2","13","0"
"2017-01-07 03:01:02","7","3","2","13","0"
"2017-01-07 03:02:02","7","3","2","12","0"
"2017-01-07 03:07:02","7","3","2","12","0"
"2017-01-07 03:08:02","6","3","2","12","1"
"2017-01-07 03:09:02","7","3","2","12","0"
"2017-01-07 03:10:02","6","3","2","11","1"

上面（实际数据）示例中缺少行。由于样本之间的数据变化不大，我只想将最后的有效数据复制到缺失的行中。我遇到的问题是检测哪些行丢失。

我正在使用我拼凑在一起的python程序处理CSV（我对python很新）。 这可以处理我拥有的数据。 

import csv
import datetime

with open("minutedata.csv", 'rb') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
    date = datetime.datetime.strptime (row [0],"%Y-%m-%d %H:%M:%S")
    v1 = int(row[1])
    v2 = int(row[2])
    v3 = int(row[3])
    v4 = int(row[4])
    v5 = int(row[5])
    ...(process values)...

...(save data)...

我不确定如何检查当前行是按顺序排列，还是在丢失一些行之后。

编辑添加：

我正在尝试使用Pandas，感谢jeremycg指向它。

我在CSV中添加了标题行，现在它看起来像：

time,v1,v2,v3,v4,v5
"2017-01-07 03:00:02","7","3","2","13","0"
"2017-01-07 03:01:02","7","3","2","13","0"
"2017-01-07 03:02:02","7","3","2","12","0"
"2017-01-07 03:07:02","7","3","2","12","0"
"2017-01-07 03:08:02","6","3","2","12","1"
"2017-01-07 03:09:02","7","3","2","12","0"
"2017-01-07 03:10:02","6","3","2","11","1"

处理代码现在是：

import pandas as pd
import io
z = pd.read_csv('minutedata.csv')
z['time'] = pd.to_datetime(z['time'])
z.set_index('time').reindex(pd.date_range(min(z['time']), max(z['time']),freq="1min")).ffill()
for row in z:
    date = datetime.datetime.strptime (row [0],"%Y-%m-%d %H:%M:%S")
    v1 = int(row[1])
    v2 = int(row[2])
    v3 = int(row[3])
    v4 = int(row[4])
    v5 = int(row[5])
    ...(process values)...

...(save data)...

但是出错了：

Traceback (most recent call last):
File "process_day.py", line 14, in <module>
z.set_index('time').reindex(pd.date_range(min(z['time']), max(z['time']), freq="1min")).ffill()
File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 2821, in reindex
**kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py", line 2259, in reindex fill_value, copy).__finalize__(self)
File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 2767, in _reindex_axes
fill_value, limit, tolerance)
File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 2778, in _reindex_index allow_dups=False)
File "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py", line 2371, in _reindex_with_indexers copy=copy)
File "/usr/local/lib/python2.7/site-packages/pandas/core/internals.py", line 3839, in reindex_indexer self.axes[axis]._can_reindex(indexer)
File "/usr/local/lib/python2.7/site-packages/pandas/indexes/base.py", line 2494, in _can_reindex raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis

我迷失了现在声称被打破的东西。

请参阅下面的评论，了解此修复程序。

现在的工作代码是：

import pandas as pd
import datetime

z = pd.read_csv('minutedata1.csv')
z = z[~z.time.duplicated()]
z['time'] = pd.to_datetime(z['time'])
z.set_index('time').reindex(pd.date_range(min(z['time']), max(z['time']),freq="1min")).ffill()
for index,row in z.iterrows():
    date = datetime.datetime.strptime (row [0],"%Y-%m-%d %H:%M:%S")
    v1 = int(row[1])
    v2 = int(row[2])
    v3 = int(row[3])
    v4 = int(row[4])
    v5 = int(row[5])
    ...(process values)...

...(save data)...

衷心感谢所有帮助过的人。 - 大卫

2 个答案:

答案 0 :(得分：2)

你可能应该使用pandas，因为它是为这种东西制作的。

首先阅读csv：

import pandas as pd
import io
x = '''
time,a,b,c,d,e
"2017-01-07 03:00:02","7","3","2","13","0"
"2017-01-07 03:01:02","7","3","2","13","0"
"2017-01-07 03:02:02","7","3","2","12","0"
"2017-01-07 03:07:02","7","3","2","12","0"
"2017-01-07 03:08:02","6","3","2","12","1"
"2017-01-07 03:09:02","7","3","2","12","0"
"2017-01-07 03:10:02","6","3","2","11","1"''' #your data, with added headers
z = pd.read_csv(io.StringIO(x)) #you can use your file name here

现在z是一个pandas数据帧：

z.head()

time    a   b   c   d   e
0   2017-01-07 03:00:02 7   3   2   13  0
1   2017-01-07 03:01:02 7   3   2   13  0
2   2017-01-07 03:02:02 7   3   2   12  0
3   2017-01-07 03:07:02 7   3   2   12  0
4   2017-01-07 03:08:02 6   3   2   12  1

我们希望： 将'time'列转换为pd.datetime：

z['time'] = pd.to_datetime(z['time'])

将数据框的'index'设置为时间，然后重新索引我们的范围：

z = z.set_index('time').reindex(pd.date_range(min(z['time']), max(z['time']), freq="1min"))
z

a   b   c   d   e
2017-01-07 03:00:02 7.0 3.0 2.0 13.0    0.0
2017-01-07 03:01:02 7.0 3.0 2.0 13.0    0.0
2017-01-07 03:02:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:03:02 NaN NaN NaN NaN NaN
2017-01-07 03:04:02 NaN NaN NaN NaN NaN
2017-01-07 03:05:02 NaN NaN NaN NaN NaN
2017-01-07 03:06:02 NaN NaN NaN NaN NaN
2017-01-07 03:07:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:08:02 6.0 3.0 2.0 12.0    1.0
2017-01-07 03:09:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:10:02 6.0 3.0 2.0 11.0    1.0

然后使用.ffill（）从前一个值填写：

z.ffill()

a   b   c   d   e
2017-01-07 03:00:02 7.0 3.0 2.0 13.0    0.0
2017-01-07 03:01:02 7.0 3.0 2.0 13.0    0.0
2017-01-07 03:02:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:03:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:04:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:05:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:06:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:07:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:08:02 6.0 3.0 2.0 12.0    1.0
2017-01-07 03:09:02 7.0 3.0 2.0 12.0    0.0
2017-01-07 03:10:02 6.0 3.0 2.0 11.0    1.0

或者，所有在一起：

z = pd.read_csv(io.StringIO(x))
z['time'] = pd.to_datetime(z['time'])
z.set_index('time').reindex(pd.date_range(min(z['time']), max(z['time']), freq="1min")).ffill()

答案 1 :(得分：2)

建议使用jeremycg建议使用的pandas。虽然如果你正在寻找没有大熊猫的解决方案，那么它就是：

import csv
import datetime

data = []

with open("minutedata.csv", newline='') as f:
    reader = csv.reader(f, delimiter=',')

    prev_date = None

    for row in reader:

        date = datetime.datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S")

        if prev_date:
            diff = date - prev_date

            if diff > datetime.timedelta(minutes=1):

                for i in range((int(diff.total_seconds() / 60) - 1)):
                    new_date = prev_date + datetime.timedelta(minutes=i + 1)
                    new_row = [str(new_date)] + row[1:]

                    data.append(",".join(new_row))

        prev_date = date

        data.append(",".join(row))

print(data)

说明： 我们遍历每一行并检查当前行的日期与前一行的日期

diff = date - prev_date

如果我们发现差异超过1分钟，我们会输入一个循环，该循环针对缺失数据的范围运行

if diff > datetime.timedelta(minutes=1):

    for i in range((int(diff.total_seconds() / 60) - 1)):
        ...

我们通过在上一个日期添加分钟来添加计算缺失值

new_date = prev_date + datetime.timedelta(minutes=i + 1)
new_row = [str(new_date)] + row[1:]

你完成了！

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