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Pandas how to quickly achieve weekly, monthly, quarterly date aggregation statistics, in response to this problem, this article details the corresponding analysis and solution, hoping to help more small partners who want to solve this problem find a simpler and easier way.

A large amount of data is organized according to date, such as stock data, weather data, day-granularity traffic statistics, Pandas provides a large space to support date processing, compared to their own implementation of date processing is very convenient and fast.

Pandas date processing has two core objects, pd.TimeStamp can express a date object of a single string, pd.DatetimeIndex can express a sequence of date objects, the schematic diagram is as follows:

About Pandas how to quickly achieve weekly, monthly, quarterly date aggregation statistical questions to share the answer here, I hope the above content can be of some help to everyone, if you still have a lot of doubts not solved, you can pay attention to the industry information channel to learn more related knowledge.

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