![]() ![]() get_majorticklabels (), rotation = 20 ) # Season label text ax11. get_majorticklabels (), rotation = 20 ) plt. suptitle ( "2012-2013 Seasonal temperature observations" "- Helsinki-Vantaa airport" ) # Rotate the x-axis labels so they don't overlap plt. ![]() plot ( ax = ax22, c = "brown", lw = line_width, ylim =, xlabel = "Date", grid = True, ) # Set figure title fig. plot ( ax = ax21, c = "green", lw = line_width, ylim =, xlabel = "Date", ylabel = "Temperature ", grid = True, ) autumn_temps. plot ( ax = ax12, c = "orange", lw = line_width, ylim =, grid = True ) summer_temps. plot ( ax = ax11, c = "blue", lw = line_width, ylim =, ylabel = "Temperature ", grid = True, ) spring_temps. subplots ( nrows = 2, ncols = 2, figsize = ( 12, 8 )) # Define variables to more easily refer to individual axes ax11 = axs ax12 = axs ax21 = axs ax22 = axs # Set plot line width line_width = 1.5 # Plot data winter_temps. # Create the figure and subplot axes fig, axs = plt. We can do that below by calculating the minumum of each seasons minumum temperature and subtracting five degrees. In addition, we should consider that it would be beneficial to have some extra space (padding) between the y-axis limits and those values, such that, for example, the maximum y-axis limit is five degrees higher than the maximum temperature and the minimum y-axis limit is five degrees lower than the minimum temperature. In order to define y-axis limits that will include the data from all of the seasons and be consistent between subplots we first need to find the minimum and maximum temperatures from all of the seasons. This will help make it easier to visually compare the temperatures between seasons. One thing we might need to consider with this is that the y-axis range currently varies between the two plots and we may want to define axis ranges that ensure the data are plotted with the same y-axis ranges in all subplots. Summer temperatures for 2012-2013.īased on the plots above it looks that the correct seasons have been plotted and the temperatures between winter and summer are quite different, as we would expect. Interpreting topographic features from raster dataįigure 4.12. Multimodal spatial accessibility analysis with Python Inverse Distance Weighting interpolation with Python Retrieving data from Web Coverage Service (WCS) Retrieving data from Web Feature Service (WFS) Raster operations between multiple layers Introduction to raster processing with Python Preparing GeoDataFrames from geographic data Introduction to spatial data analysis with geopandas ![]() Introduction to geographic data objects in Python Part II - Introduction to GIS with Python Quickly getting started (without installing Python) ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |