Raincloud of Irish Rain data. Using code from https://github.com/RainCloudPlots/RainCloudPlots and data from https://www.metoffice.gov.uk/pub/data/weather/uk/climate/stationdata/armaghdata.txt

armaghdata2.csv

raincloud.py

import pandas as pd
import seaborn as sns
import os
import matplotlib.pyplot as plt
#sns.set(style="darkgrid")
#sns.set(style="whitegrid")
#sns.set_style("white")
sns.set(style="whitegrid",font_scale=2)
import matplotlib.collections as clt


import ptitprince as pt

# Load the Pandas libraries with alias 'pd' 
import pandas as pd 
# Read data from file 'filename.csv' 
# (in the same directory that your python process is based)
# Control delimiters, rows, column names with read_csv (see later) 
#yyyy,mm,tmax,tmin, af,rain,sun
data = pd.read_csv("armaghdata2.csv",skiprows=0,sep=',',header=0, names=['Year', 'Month','tmax','tmin', 'af','rain','sun']) #\s+
# Preview the first 5 lines of the loaded data 
data.head()
#change month number into the name
import calendar
data['Month'] = data['Month'].apply(lambda x: calendar.month_abbr[x])

# plotting the clouds
f, ax = plt.subplots(figsize=(7, 5))
dy="Month"; dx="rain"; ort="h"; pal = [(0.596078431372549, 0.9607843137254902, 1.0)]

ax=pt.half_violinplot( x = dx, y = dy, data = data, palette = pal, bw = .2, cut = 0.,
                      scale = "area", width = .6, inner = None, orient = ort)
ax=sns.stripplot( x = dx, y = dy, data = data, palette = pal, edgecolor = "white",
                 size = 3, jitter = 1, zorder = 0, orient = ort)
ax=sns.boxplot( x = dx, y = dy, data = data, color = "black", width = .13,showcaps = False, boxprops = {'facecolor':'none'},showfliers=False, whiskerprops = {'linewidth':2},saturation = 1, orient = ort)

plt.title("Irish Rainfall Raincloud Graph")
ax.set_xlim(xmin=0)
ax.set_xlabel('Rain (mm)')
plt.annotate('Armagh Observatory\nData since 1853', (0,0), (-80,-20), fontsize=8, 
             xycoords='axes fraction', textcoords='offset points', va='top')



if savefigs:
    plt.savefig('figs/figureP06.png', bbox_inches='tight')

#get the wettest month
data.loc[data['rain'].idxmax()]