Plot every year as line with months on Xaxis and variable on Y-axis from NetCDF - netcdf

Question

I have netcdf data with lat,lon,time as dimensions and temperature temp as variable. It has daily temperature data for 10 years.
For single location I can plot time series. But how to plot for every year, Year as hue and Months on Xaxis and temp on Y axis. So i want 10 lines as 10 years on my graph. Every line is an year which represents 12 monthly means or daily data. example is here.
And if possible please tell how to add mean and median of all the years as seperate line among these 10 yearly line plots. example picture image example

Answer 1

I'm tempted to agree with the comment that it would be good to show a little more effort in terms of what you've tried. It would also be good to mention what you've read (in e.g. the xarray documentation: https://xarray.pydata.org/en/stable/), which I believe has many of the components you need.
I'll start by setting up some mock data, like you mention, with four years of daily (random) data.
time = pd.date_range("2000-01-01", "2004-12-31")
base = xr.DataArray(
data=np.ones((time.size, 3, 2)),
dims=("time", "lat", "lon"),
coords={
"time": time,
"lat": [1, 2, 3],
"lon": [0.5, 1.5],
},
)
To make the data a bit more comparable with your example, I'm going to add yearly seasonality (based on day of year), and make every year increase by 0.1.
seasonality = xr.DataArray(
data=np.sin((time.dayofyear / 365.0) * (2 * np.pi)),
coords={"time": time},
dims=["time"],
)
trend = xr.DataArray(
data=(time.year - 2000) * 0.1,
coords={"time": time},
dims=["time"],
)
da = base + seasonality + trend
(You can obviously skip these two parts, in your case, you'd only do an xarray.open_dataset() or xarray.open_dataarray`)
I don't think your example is grouped by month: it's too smooth. So I'm going to group by day of year instead.
Let's start by getting a single locations, then using the dt accessor:
https://xarray.pydata.org/en/stable/time-series.html#datetime-components
In this case, it's also most convenient to store the data as a DataFrame, since it essentially becomes a table (month of dayofyear as the rows, separate years etc as columns). First we select one location, and calculate the minimum and maximum values and store them in a pandas DataFrame:
location = da.isel(lat=0, lon=0)
dataframe = location.groupby(da["time"].dt.dayofyear).min().drop(["lat", "lon"]).to_dataframe(name="min")
dataframe["max"] = location.groupby(da["time"].dt.dayofyear).max().values
Next, grab the year by year data, and add it to the DataFrame:
for year, yearda in location.groupby(location["time"].dt.year):
dataframe[year] = pd.Series(index=yearda["time"].dt.dayofyear, data=yearda.values)
If you want monthly values, add another groupby step:
for year, yearda in location.groupby(location["time"].dt.year):
monthly_mean = yearda.groupby(yearda["time"].dt.month).mean()
dataframe[year] = pd.Series(index=monthly_mean["month"], data=monthly_mean.values)
Note that by turning the data into a pandas Series first, it can add the values appriopriately, based on the values of the index (dayofyear here), even though we don't have 366 values for every year.
Next, plot it:
dataframe.plot()
It will automatically assign hue based on the columns.
(My minimum and maximum coincide with 2000 and 2004 due to the way I setup the mock data, ... you get the idea.)
In terms of styling, options, etc., you might like seaborn better:
https://seaborn.pydata.org/index.html
import seaborn as sns
sns.plot(data=dataframe)
If you want to use different styling, different kind of plots (e.g. the colored zones your example has), you'll have to combine different plot, e.g. as follows:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.fill_between(x=dataframe.index, y1=dataframe["min"], y2=dataframe["max"], alpha=0.5, color="orange")
dataframe.plot(ax=ax)
Note that seaborn, pandas, xarray, etc. all use matplotlib behind the scenes. Many of the plotting functions also accept an ax argument, to draw on top of an existing plot.