Pandas DataFrame按时间戳分组 [英] Pandas DataFrame grouping by Timestamp

问题描述

我有一个用例，其中:

数据的格式为:Col1，Col2，Col3和时间戳.

Data is of the form: Col1, Col2, Col3 and Timestamp.

现在，我只想获取行数与时间戳箱的计数.

Now, I just want to get the counts of the rows vs Timestamp Bins.

即对于每半小时的时段(甚至没有相应行的时段)，我需要计算有多少行.

i.e. for every half hour bucket (even the ones which have no correponding rows), I need the counts of how many rows are there.

时间戳分布在一年内，因此我不能将其划分为24个存储桶.

Timestamps are spread over a one year period, so I can't divide it into 24 buckets.

我必须每隔30分钟对它们进行装箱一次.

I have to bin them at 30 minutes interval.

推荐答案

groupby通过pd.Grouper

groupby via pd.Grouper

# optionally, if needed
# df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')  
df.groupby(pd.Grouper(key='Timestamp', freq='30min')).count()

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resample

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resample

df.set_index('Timestamp').resample('30min').count()

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