Formatting the months and dates in a subplot-CodePudding

I am trying to create a time series of the sea surface temperature data over the whole year for six consecutive years and plot them using the subplots. I want to mark the x-ticks as the months. I tried using the matplotlib.dates option. However the years doesn't change on the subsequent subplots.

import numpy as np
import sys
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.dates import set_epoch

arrays14 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2014.ascii')] #loading the data
arrays15 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2015.ascii')]
arrays16 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2016.ascii')]
arrays17 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2017.ascii')]
arrays18 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2018.ascii')]
arrays19 = [np.asarray(list(map(str, line.split()))) for line in open('/home/swadhin/project/sst/daily/sst15n90e_dy_2019.ascii')]

arrays14 = np.delete(arrays14,[0,1,2,3,4],0) #deleting the headers
arrays15 = np.delete(arrays15,[0,1,2,3,4],0)
arrays16 = np.delete(arrays16,[0,1,2,3,4],0)
arrays17 = np.delete(arrays17,[0,1,2,3,4],0)
arrays18 = np.delete(arrays18,[0,1,2,3,4],0)
arrays19 = np.delete(arrays19,[0,1,2,3,4,215,216,217],0)
sst14 = []
for i in arrays14:
    d1 = i[0]
    d2 = i[2]
    sst1 = i[2]
    sst14.append(sst1)
    datetime1.append(d1)
    datetime2.append(d2)
sst14 = np.array(sst14,dtype = np.float64)
sst_14_m = np.ma.masked_equal(sst14,-9.99) #masking the fillvalues

sst15 = []
for i in arrays15:
    sst2 = i[2]
    sst15.append(sst2)
sst15 = np.array(sst15,dtype = np.float64)
sst_15_m = np.ma.masked_equal(sst15,-9.99)

sst16 = []
for i in arrays16:
    sst3 = i[2]
    sst16.append(sst3)
sst16 = np.array(sst16,dtype = np.float64)
sst_16_m = np.ma.masked_equal(sst16,-9.99)

sst17 = []
for i in arrays17:
    sst4 = i[2]
    sst17.append(sst4)
sst17 = np.array(sst17,dtype = np.float64)
sst_17_m = np.ma.masked_equal(sst17,-9.99)

sst18 = []
for i in arrays18:
    sst5 = i[2]
    sst18.append(sst5)

sst18 = np.array(sst18,dtype = np.float64)
sst_18_m = np.ma.masked_equal(sst18,-9.99)
np.shape(sst18)
sst19 = []
for i in arrays19:
    sst6 = i[2]
    sst19.append(sst6)
sst19 = np.array(sst19,dtype = np.float64)
sst19_u = np.zeros(len(sst14), dtype = np.float64)
sst19_fill = np.full([118],-9.99,dtype=np.float64)

sst19_u[0:211] = sst19[0:211]
sst19_u[211:329] = sst19_fill
sst19_u[329:365] = sst19[211:247]

sst_19_m = np.ma.masked_equal(sst19_u,-9.99)

##########Plotting
new_epoch = '2016-01-01T00:00:00'
mdates.set_epoch(new_epoch)
fig, axs=plt.subplots(3, 2, figsize=(12, 8),constrained_layout=True)
axs = axs.ravel()
axs[0].plot(sst_14_m)
axs[1].plot(sst_15_m)
axs[2].plot(sst_16_m)
axs[3].plot(sst_17_m)
axs[4].plot(sst_18_m)
axs[5].plot(sst_19_m)
for i in range(6):
    axs[i].xaxis.set_major_locator(mdates.MonthLocator())
    axs[i].xaxis.set_minor_locator(mdates.MonthLocator())
    axs[i].xaxis.set_major_formatter(mdates.ConciseDateFormatter(axs[i].xaxis.get_major_locator()))
    #axs[i].grid(True)
    axs[i].set_ylim(bottom=25, top=32)
    
    axs[i].set_ylabel('SST')
plt.show()

I got an output like the following:

I would like to change the xlabels as 2016,2017,2018,2019 etc.

The data can be found in the folder -

CodePudding user response：

I love NumPy as much as the next person but this is a good use case for

Let's convert that date to a 'datetime' object, and use it as the index instead of the default row numbers. We'll also deal with those -9.99 values.

import numpy as np

df['ds'] = pd.to_datetime(df['YYYYMMDD'], format='%Y%m%d')
df = df.set_index('ds')
df = df.sort_index()
df.loc[df['SST'] == -9.99, 'SST'] = np.nan

Now you have a dataset you can do all sorts of magic with, like df.resample('Y')['SST'].sum() shows you the annual sum of SST.

Anyway, now we can make plots in various ways. You can plot DataFrames directly, eg check out df.groupby(df.index.year)['SST'].plot(). Or you can use