pandas 从指定的行号遍历行 [英] Pandas Iterate through rows from specified row number

问题描述

我想通过遍历从特定行号开始的行来从pandas数据框中读取数据.我知道有 df.iterrows() ，但它不允许我指定要从何处开始迭代.

I want to read data from a pandas dataframe by iterating through the rows starting from a specific row number. I know there's df.iterrows(), but it doesn't let me specify from where I want to start iterating.

在我的特定情况下，我有一个csv文件，看起来可能像这样:

In my specific case, I have a csv file that might look something like this:

Date, Temperature
21/08/2017 17:00:00,5.53
21/08/2017 18:00:00,5.58
21/08/2017 19:00:00,4.80
21/08/2017 20:00:00,4.59
21/08/2017 21:00:00,3.72
21/08/2017 22:00:00,3.95
21/08/2017 23:00:00,3.11
22/08/2017 00:00:00,3.07
22/08/2017 01:00:00,2.80
22/08/2017 02:00:00,2.75
22/08/2017 03:00:00,2.79
22/08/2017 04:00:00,2.76
22/08/2017 05:00:00,2.76
22/08/2017 06:00:00,3.06
22/08/2017 07:00:00,3.88

我想遍历特定时间点的每一行(比如说8月22日午夜)，所以我尝试像这样实现它:

I want to loop through every row from a specific point in time on (let's say midnight of August 22nd), so I tried implementing it like this:

df = pandas.read_csv('file.csv')
start_date = '22/08/2017 00:00:00'

// since it's sorted, I figured I could use binary search
result = pandas.Series(df['Date']).searchsorted(start_date)

结果[0] 实际上给了我正确的数字.

result[0] actually gives me the correct number.

我想我能做的就是增加该数字并通过 df.iloc [[x]] 访问该行，但是我觉得这样做很脏.

I guess what I could do then is just increment that number and access the row through df.iloc[[x]], but I feel dirty doing that.

for x in range(result[0], len(df)):
    row = df.loc[[x]]

到目前为止，我发现的所有答案仅显示了如何迭代整个表.

All answers I've found so far only show how to iterate the whole table.

推荐答案

只需在调用 iterrows()之前过滤数据框:

Just filter your dataframe before calling iterrows():

df['Date'] = pandas.to_datetime(df['Date'])
for idx, row in df[df['Date'] >= '2017-08-22'].iterrows():
    #
    # Whatever you want to do in the loop goes here
    #

请注意，没有必要将过滤参数'2017-08-22'转换为 datetime 对象，因为熊猫可以处理

Note that it isn't necessary to convert the filtering argument '2017-08-22' to a datetime object, because Pandas can handle partial string indexing.

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