How to use python to analyze historical data of air quality
This article mainly introduces "how to use python to analyze air quality historical data". In daily operation, I believe many people have doubts about how to use python to analyze air quality historical data. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubts about "how to use python to analyze air quality historical data". Next, please follow the editor to study!
Calculate the number of days according to the month, python provides an existing function to calculate the number of days per month according to the year and month, import the calendar library, use the monthrange to calculate the number of days in each month, and the return result is a tuple, the first is the day of the week, and the first parameter is the number of days of the month.
Import calendar
Def MonthDay (_ year, _ month):
Return (calendar.monthrange (_ year, _ month)) [1]
Each day of the cycle month, read the data in each hour, use timedelta plus the number of days, create a new empty DataFrame and store the data for the whole month. During the cycle, add the daily data to the df of the monthly data, and directly use the mean () function to calculate the daily hourly average. In order not to miss the daily data, the number of days without data will also be added to the sky data.
For idx in range (daycount):
Nowdate = startDate + timedelta (days=idx)
Monthdata = pd.DataFrame (columns= ["datetime", "type", "AQI", 'level'])
Monthdata = monthdata.append (
{'datetime': nowdate,' type': 'AQI',' AQI': daydata ['AQI'] .mean ()}
Ignore_index=True)
Monthdata = monthdata.append (
{'datetime': nowdate,' type': 'AQI',' AQI': np.nan}
Ignore_index=True)
Daily AQI data values:
Using the following code, you can automatically rotate the date to make the display better.
Plt.gcf () .autofmt_xdate ()
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