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