Summary
In this chapter, we examined the many ways in pandas to represent various units of time and time-series data. Understanding date and time-series as well as frequency conversion is critical to analyzing financial information. We examined several ways of manipulating time-series data represented by stock price information, working with dates, times, time zones, and calendars. In closing, the chapter examined the means of converting the data in time-series into different frequencies.
In the next chapter, we will dive deeper into an analysis of historical stock data using time-series in pandas, greatly expanding our knowledge of both pandas and using it to analyze financial data.
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