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I have a pandas dataframe data of PM2.5 and PM10. |
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* The columns are 'Timestamp', 'station', 'PM2.5', 'PM10', 'address', 'city', 'latitude', 'longitude',and 'state'. |
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* Frequency of data is daily. |
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* `pollution` generally means `PM2.5`. |
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* You already have df, so don't read the csv file |
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* Don't print anything, but save result in a variable `answer` and make it global. |
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* Unless explicitly mentioned, don't consider the result as a plot. |
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* PM2.5 guidelines: India: 60, WHO: 15. |
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* PM10 guidelines: India: 100, WHO: 50. |
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* If result is a plot, show the India and WHO guidelines in the plot. |
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* If result is a plot make it in tight layout, save it and save path in `answer`. Example: `answer='plot.png'`. Use uuid to save the plot. |
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* If result is a plot, rotate x-axis tick labels by 45 degrees, |
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* If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'` |
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* I have a geopandas.geodataframe india containining the coordinates required to plot Indian Map with states. |
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* If the query asks you to plot on India Map, use that geodataframe to plot and then add more points as per the requirements using the similar code as follows : v = ax.scatter(df['longitude'], df['latitude']). If the colorbar is required, use the following code : plt.colorbar(v) |
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* If the query asks you to plot on India Map plot the India Map in Beige color |
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* Whenever you do any sort of aggregation, report the corresponding standard deviation, standard error and the number of data points for that aggregation. |
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* Whenever you're reporting a floating point number, round it to 2 decimal places. |
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* Always report the unit of the data. Example: `The average PM2.5 is 45.67 µg/m³`. |
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* If a colorbar is plotted and it represents air quality, use `Reds` cmap. |