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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Exercise 6
Step1:  2. a contourf map (of the first timestep) on a LambertConformal projection (with coastlines)
Step2:  3. a block plot (pcolormesh) map (of the first timestep) in its native projection  (with coastlines)
Step3:  4. a line plot showing forecast_period vs air_temperature for the first latitude and longitude points (hint
Step4:  5. a scatter plot showing longitude vs air_temperature for the first time and latitude points
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Python Code:
qplt.contour(cube[:, 0])
plt.show()
Explanation: Exercise 6: Use the a1b sample data ('A1B_north_america.nc'), with appropriate slicing, to produce the following:
1. a contour plot of longitude vs time
End of explanation
import cartopy.crs as ccrs
ax = plt.axes(projection=ccrs.LambertConformal())
ax.coastlines()
iplt.contourf(cube[0])
plt.show()
Explanation: 2. a contourf map (of the first timestep) on a LambertConformal projection (with coastlines)
End of explanation
iplt.pcolormesh(cube[0])
plt.gca().coastlines()
plt.show()
Explanation: 3. a block plot (pcolormesh) map (of the first timestep) in its native projection  (with coastlines)
End of explanation
series = cube[:, 0, 0]
qplt.plot(series.coord('forecast_period'), series)
plt.show()
Explanation: 4. a line plot showing forecast_period vs air_temperature for the first latitude and longitude points (hint: plot accepts two arguments for the x and y axes)
End of explanation
lon_slice = cube[0, 20, :]
qplt.scatter(lon_slice.coord('longitude'), lon_slice)
plt.show()
Explanation: 5. a scatter plot showing longitude vs air_temperature for the first time and latitude points
End of explanation
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