Example Analysis of python Air quality data
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After calculating the daily average AQI, according to the AQI data, calculate the corresponding daily pollution level, you need to add a new column to the one-month data, and directly add square brackets on the DataFrame.
Monthdata ['level']
In the use of map function, according to the data value of AQI, calculate the corresponding pollution level, map can use the function.
Monthdata ['level'] = monthdata [' AQI'] .map (ComputeLevel)
Aqilevels = np.array ([.])
Levels = np.array ([u 'excellent', u 'good', u 'mild pollution'
U' moderate pollution', u' heavy pollution', u' serious pollution', u' no data'])
Def ComputeLevel (x):
If (not math.isnan (x)):
Index = np.where (aqilevels > = x)
Return levels [index [0] [0]]
Else:
Return'no data'
Fill the part without data with no data, so that it can be easily viewed when the data is counted.
Monthdata ['level'] = monthdata [' level'] .fillna ('innumerable data')
Next, according to the pollution level, count the data in DataFrame and calculate the number of days of each pollution level per month, in which the level column name is converted to count, and level is used as the column name, not as an index.
Levelscount = pd.DataFrame (monthdata ['level'].
Groupby (monthdata ['level']) .count ()
Levelscount = levelscount.rename (columns= {'level':' count'})
Levelscount = levelscount.reset_index ('level')
Use matplotlib to count the number of days at each level per month and display it in a bar chart.
Fig = plt.figure (figsize= (8,5))
Dataax = fig.add_subplot (1,1,1)
Dataax.bar (inde, levelscount ['count'] .tolist ()
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