Visualization of datasets

Visualization of datasets¶

FIZ228 - Numerical Analysis
Dr. Emre S. Tasci, Hacettepe University

It’s always beneficial to check the data before and after we process it as it can offer some hidden relations or the picking of off values. Even though the matplotlib module offers elasticity, unfortunately it is not known for its practicality. Wrappers like the seaborn module provide functionality with ease.

“El Clasico”¶

Let’s try to do it old way, using numpy & matplotlib. As we have observed in our previous lecture, pandas were the go-to module when dealing with datasets, but for reference purposes, we’ll start with numpy arrays. As numpy arrays can not (by default) store elements of different types, our string timestamps are lost in import.

For the beginners, we are going to use the meteorological data of the Basel city, obtained from meteoblue.com

01_meteoblue_Basel_20230303T060433.csv

import numpy as np

data_np = np.genfromtxt("data/01_meteoblue_Basel_20230303T060433.csv", delimiter=',',
                        filling_values=0.0,skip_header=10)
data_np

array([[ 0.       ,  6.0602455, 94.19782  ,  1.5      ,  0.       ,
         0.       ],
       [ 0.       ,  5.5602455, 94.83262  ,  3.       ,  0.       ,
         0.       ],
       [ 0.       ,  4.630245 , 96.47203  ,  0.3      ,  0.       ,
         0.       ],
       ...,
       [ 0.       ,  0.       ,  0.       ,  0.       ,  0.       ,
         0.       ],
       [ 0.       ,  0.       ,  0.       ,  0.       ,  0.       ,
         0.       ],
       [ 0.       ,  0.       ,  0.       ,  0.       ,  0.       ,
         0.       ]])

data_np.shape

(10248, 6)

We’re going to implement meaningful indexes as the first column, by joining the year, month, day index with the hour.

Checking the timestamp of the top entries, we see that it goes from ‘20220101T0000’ to ‘20230303T2300’ (with most of the last entries being blank but we’ll deal with it later).

flag_break = False
for y in range(22,24):
    if(flag_break):
        break
    for m in range(1,13):
        if(flag_break):
            break
        for d in range(1,32):
            if(flag_break):
                break
            if((m==2) & (d>28)):
                continue
            if((m in [2,4,6,9,11]) & (d>30)):
                continue
            for h in range (0,24):
                print('{:2d}{:02d}{:02d}{:02d}'.format(y,m,d,h))
                date = '{:2d}{:02d}{:02d}{:02d}'.format(y,m,d,h)
                if(date == '23030323'):
                    flag_break = True
                    break

i = 0
flag_break = False
for y in range(22,24):
    if(flag_break):
        break
    for m in range(1,13):
        if(flag_break):
            break
        for d in range(1,32):
            if(flag_break):
                break
            if((m==2) & (d>28)):
                continue
            if((m in [2,4,6,9,11]) & (d>30)):
                continue
            for h in range (0,24):
                #print('{:2d}{:02d}{:02d}{:02d}'.format(y,m,d,h))
                date = '{:2d}{:02d}{:02d}{:02d}'.format(y,m,d,h)
                data_np[i,0] = date
                i += 1
                if(date == '23030323'):
                    flag_break = True
                    break
print(i)
data_np

array([[2.2010100e+07, 6.0602455e+00, 9.4197820e+01, 1.5000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.2010101e+07, 5.5602455e+00, 9.4832620e+01, 3.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.2010102e+07, 4.6302450e+00, 9.6472030e+01, 3.0000000e-01,
        0.0000000e+00, 0.0000000e+00],
       ...,
       [2.3030321e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.3030322e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.3030323e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00]])

data_np[-1000:,0]

array([23012108., 23012109., 23012110., 23012111., 23012112., 23012113.,
       23012114., 23012115., 23012116., 23012117., 23012118., 23012119.,
       23012120., 23012121., 23012122., 23012123., 23012200., 23012201.,
       23012202., 23012203., 23012204., 23012205., 23012206., 23012207.,
       23012208., 23012209., 23012210., 23012211., 23012212., 23012213.,
       23012214., 23012215., 23012216., 23012217., 23012218., 23012219.,
       23012220., 23012221., 23012222., 23012223., 23012300., 23012301.,
       23012302., 23012303., 23012304., 23012305., 23012306., 23012307.,
       23012308., 23012309., 23012310., 23012311., 23012312., 23012313.,
       23012314., 23012315., 23012316., 23012317., 23012318., 23012319.,
       23012320., 23012321., 23012322., 23012323., 23012400., 23012401.,
       23012402., 23012403., 23012404., 23012405., 23012406., 23012407.,
       23012408., 23012409., 23012410., 23012411., 23012412., 23012413.,
       23012414., 23012415., 23012416., 23012417., 23012418., 23012419.,
       23012420., 23012421., 23012422., 23012423., 23012500., 23012501.,
       23012502., 23012503., 23012504., 23012505., 23012506., 23012507.,
       23012508., 23012509., 23012510., 23012511., 23012512., 23012513.,
       23012514., 23012515., 23012516., 23012517., 23012518., 23012519.,
       23012520., 23012521., 23012522., 23012523., 23012600., 23012601.,
       23012602., 23012603., 23012604., 23012605., 23012606., 23012607.,
       23012608., 23012609., 23012610., 23012611., 23012612., 23012613.,
       23012614., 23012615., 23012616., 23012617., 23012618., 23012619.,
       23012620., 23012621., 23012622., 23012623., 23012700., 23012701.,
       23012702., 23012703., 23012704., 23012705., 23012706., 23012707.,
       23012708., 23012709., 23012710., 23012711., 23012712., 23012713.,
       23012714., 23012715., 23012716., 23012717., 23012718., 23012719.,
       23012720., 23012721., 23012722., 23012723., 23012800., 23012801.,
       23012802., 23012803., 23012804., 23012805., 23012806., 23012807.,
       23012808., 23012809., 23012810., 23012811., 23012812., 23012813.,
       23012814., 23012815., 23012816., 23012817., 23012818., 23012819.,
       23012820., 23012821., 23012822., 23012823., 23012900., 23012901.,
       23012902., 23012903., 23012904., 23012905., 23012906., 23012907.,
       23012908., 23012909., 23012910., 23012911., 23012912., 23012913.,
       23012914., 23012915., 23012916., 23012917., 23012918., 23012919.,
       23012920., 23012921., 23012922., 23012923., 23013000., 23013001.,
       23013002., 23013003., 23013004., 23013005., 23013006., 23013007.,
       23013008., 23013009., 23013010., 23013011., 23013012., 23013013.,
       23013014., 23013015., 23013016., 23013017., 23013018., 23013019.,
       23013020., 23013021., 23013022., 23013023., 23013100., 23013101.,
       23013102., 23013103., 23013104., 23013105., 23013106., 23013107.,
       23013108., 23013109., 23013110., 23013111., 23013112., 23013113.,
       23013114., 23013115., 23013116., 23013117., 23013118., 23013119.,
       23013120., 23013121., 23013122., 23013123., 23020100., 23020101.,
       23020102., 23020103., 23020104., 23020105., 23020106., 23020107.,
       23020108., 23020109., 23020110., 23020111., 23020112., 23020113.,
       23020114., 23020115., 23020116., 23020117., 23020118., 23020119.,
       23020120., 23020121., 23020122., 23020123., 23020200., 23020201.,
       23020202., 23020203., 23020204., 23020205., 23020206., 23020207.,
       23020208., 23020209., 23020210., 23020211., 23020212., 23020213.,
       23020214., 23020215., 23020216., 23020217., 23020218., 23020219.,
       23020220., 23020221., 23020222., 23020223., 23020300., 23020301.,
       23020302., 23020303., 23020304., 23020305., 23020306., 23020307.,
       23020308., 23020309., 23020310., 23020311., 23020312., 23020313.,
       23020314., 23020315., 23020316., 23020317., 23020318., 23020319.,
       23020320., 23020321., 23020322., 23020323., 23020400., 23020401.,
       23020402., 23020403., 23020404., 23020405., 23020406., 23020407.,
       23020408., 23020409., 23020410., 23020411., 23020412., 23020413.,
       23020414., 23020415., 23020416., 23020417., 23020418., 23020419.,
       23020420., 23020421., 23020422., 23020423., 23020500., 23020501.,
       23020502., 23020503., 23020504., 23020505., 23020506., 23020507.,
       23020508., 23020509., 23020510., 23020511., 23020512., 23020513.,
       23020514., 23020515., 23020516., 23020517., 23020518., 23020519.,
       23020520., 23020521., 23020522., 23020523., 23020600., 23020601.,
       23020602., 23020603., 23020604., 23020605., 23020606., 23020607.,
       23020608., 23020609., 23020610., 23020611., 23020612., 23020613.,
       23020614., 23020615., 23020616., 23020617., 23020618., 23020619.,
       23020620., 23020621., 23020622., 23020623., 23020700., 23020701.,
       23020702., 23020703., 23020704., 23020705., 23020706., 23020707.,
       23020708., 23020709., 23020710., 23020711., 23020712., 23020713.,
       23020714., 23020715., 23020716., 23020717., 23020718., 23020719.,
       23020720., 23020721., 23020722., 23020723., 23020800., 23020801.,
       23020802., 23020803., 23020804., 23020805., 23020806., 23020807.,
       23020808., 23020809., 23020810., 23020811., 23020812., 23020813.,
       23020814., 23020815., 23020816., 23020817., 23020818., 23020819.,
       23020820., 23020821., 23020822., 23020823., 23020900., 23020901.,
       23020902., 23020903., 23020904., 23020905., 23020906., 23020907.,
       23020908., 23020909., 23020910., 23020911., 23020912., 23020913.,
       23020914., 23020915., 23020916., 23020917., 23020918., 23020919.,
       23020920., 23020921., 23020922., 23020923., 23021000., 23021001.,
       23021002., 23021003., 23021004., 23021005., 23021006., 23021007.,
       23021008., 23021009., 23021010., 23021011., 23021012., 23021013.,
       23021014., 23021015., 23021016., 23021017., 23021018., 23021019.,
       23021020., 23021021., 23021022., 23021023., 23021100., 23021101.,
       23021102., 23021103., 23021104., 23021105., 23021106., 23021107.,
       23021108., 23021109., 23021110., 23021111., 23021112., 23021113.,
       23021114., 23021115., 23021116., 23021117., 23021118., 23021119.,
       23021120., 23021121., 23021122., 23021123., 23021200., 23021201.,
       23021202., 23021203., 23021204., 23021205., 23021206., 23021207.,
       23021208., 23021209., 23021210., 23021211., 23021212., 23021213.,
       23021214., 23021215., 23021216., 23021217., 23021218., 23021219.,
       23021220., 23021221., 23021222., 23021223., 23021300., 23021301.,
       23021302., 23021303., 23021304., 23021305., 23021306., 23021307.,
       23021308., 23021309., 23021310., 23021311., 23021312., 23021313.,
       23021314., 23021315., 23021316., 23021317., 23021318., 23021319.,
       23021320., 23021321., 23021322., 23021323., 23021400., 23021401.,
       23021402., 23021403., 23021404., 23021405., 23021406., 23021407.,
       23021408., 23021409., 23021410., 23021411., 23021412., 23021413.,
       23021414., 23021415., 23021416., 23021417., 23021418., 23021419.,
       23021420., 23021421., 23021422., 23021423., 23021500., 23021501.,
       23021502., 23021503., 23021504., 23021505., 23021506., 23021507.,
       23021508., 23021509., 23021510., 23021511., 23021512., 23021513.,
       23021514., 23021515., 23021516., 23021517., 23021518., 23021519.,
       23021520., 23021521., 23021522., 23021523., 23021600., 23021601.,
       23021602., 23021603., 23021604., 23021605., 23021606., 23021607.,
       23021608., 23021609., 23021610., 23021611., 23021612., 23021613.,
       23021614., 23021615., 23021616., 23021617., 23021618., 23021619.,
       23021620., 23021621., 23021622., 23021623., 23021700., 23021701.,
       23021702., 23021703., 23021704., 23021705., 23021706., 23021707.,
       23021708., 23021709., 23021710., 23021711., 23021712., 23021713.,
       23021714., 23021715., 23021716., 23021717., 23021718., 23021719.,
       23021720., 23021721., 23021722., 23021723., 23021800., 23021801.,
       23021802., 23021803., 23021804., 23021805., 23021806., 23021807.,
       23021808., 23021809., 23021810., 23021811., 23021812., 23021813.,
       23021814., 23021815., 23021816., 23021817., 23021818., 23021819.,
       23021820., 23021821., 23021822., 23021823., 23021900., 23021901.,
       23021902., 23021903., 23021904., 23021905., 23021906., 23021907.,
       23021908., 23021909., 23021910., 23021911., 23021912., 23021913.,
       23021914., 23021915., 23021916., 23021917., 23021918., 23021919.,
       23021920., 23021921., 23021922., 23021923., 23022000., 23022001.,
       23022002., 23022003., 23022004., 23022005., 23022006., 23022007.,
       23022008., 23022009., 23022010., 23022011., 23022012., 23022013.,
       23022014., 23022015., 23022016., 23022017., 23022018., 23022019.,
       23022020., 23022021., 23022022., 23022023., 23022100., 23022101.,
       23022102., 23022103., 23022104., 23022105., 23022106., 23022107.,
       23022108., 23022109., 23022110., 23022111., 23022112., 23022113.,
       23022114., 23022115., 23022116., 23022117., 23022118., 23022119.,
       23022120., 23022121., 23022122., 23022123., 23022200., 23022201.,
       23022202., 23022203., 23022204., 23022205., 23022206., 23022207.,
       23022208., 23022209., 23022210., 23022211., 23022212., 23022213.,
       23022214., 23022215., 23022216., 23022217., 23022218., 23022219.,
       23022220., 23022221., 23022222., 23022223., 23022300., 23022301.,
       23022302., 23022303., 23022304., 23022305., 23022306., 23022307.,
       23022308., 23022309., 23022310., 23022311., 23022312., 23022313.,
       23022314., 23022315., 23022316., 23022317., 23022318., 23022319.,
       23022320., 23022321., 23022322., 23022323., 23022400., 23022401.,
       23022402., 23022403., 23022404., 23022405., 23022406., 23022407.,
       23022408., 23022409., 23022410., 23022411., 23022412., 23022413.,
       23022414., 23022415., 23022416., 23022417., 23022418., 23022419.,
       23022420., 23022421., 23022422., 23022423., 23022500., 23022501.,
       23022502., 23022503., 23022504., 23022505., 23022506., 23022507.,
       23022508., 23022509., 23022510., 23022511., 23022512., 23022513.,
       23022514., 23022515., 23022516., 23022517., 23022518., 23022519.,
       23022520., 23022521., 23022522., 23022523., 23022600., 23022601.,
       23022602., 23022603., 23022604., 23022605., 23022606., 23022607.,
       23022608., 23022609., 23022610., 23022611., 23022612., 23022613.,
       23022614., 23022615., 23022616., 23022617., 23022618., 23022619.,
       23022620., 23022621., 23022622., 23022623., 23022700., 23022701.,
       23022702., 23022703., 23022704., 23022705., 23022706., 23022707.,
       23022708., 23022709., 23022710., 23022711., 23022712., 23022713.,
       23022714., 23022715., 23022716., 23022717., 23022718., 23022719.,
       23022720., 23022721., 23022722., 23022723., 23022800., 23022801.,
       23022802., 23022803., 23022804., 23022805., 23022806., 23022807.,
       23022808., 23022809., 23022810., 23022811., 23022812., 23022813.,
       23022814., 23022815., 23022816., 23022817., 23022818., 23022819.,
       23022820., 23022821., 23022822., 23022823., 23030100., 23030101.,
       23030102., 23030103., 23030104., 23030105., 23030106., 23030107.,
       23030108., 23030109., 23030110., 23030111., 23030112., 23030113.,
       23030114., 23030115., 23030116., 23030117., 23030118., 23030119.,
       23030120., 23030121., 23030122., 23030123., 23030200., 23030201.,
       23030202., 23030203., 23030204., 23030205., 23030206., 23030207.,
       23030208., 23030209., 23030210., 23030211., 23030212., 23030213.,
       23030214., 23030215., 23030216., 23030217., 23030218., 23030219.,
       23030220., 23030221., 23030222., 23030223., 23030300., 23030301.,
       23030302., 23030303., 23030304., 23030305., 23030306., 23030307.,
       23030308., 23030309., 23030310., 23030311., 23030312., 23030313.,
       23030314., 23030315., 23030316., 23030317., 23030318., 23030319.,
       23030320., 23030321., 23030322., 23030323.])

Let’s get rid of those without any temperature information (col #1):

data_np[data_np[:,1] == 0,:]

array([[23022401.,        0.,        0.,        0.,        0.,        0.],
       [23022402.,        0.,        0.,        0.,        0.,        0.],
       [23022403.,        0.,        0.,        0.,        0.,        0.],
       ...,
       [23030321.,        0.,        0.,        0.,        0.,        0.],
       [23030322.,        0.,        0.,        0.,        0.,        0.],
       [23030323.,        0.,        0.,        0.,        0.,        0.]])

Turns out that from Feb 24th, 2023 and forward, so:

a = data_np.copy()
a

array([[2.2010100e+07, 6.0602455e+00, 9.4197820e+01, 1.5000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.2010101e+07, 5.5602455e+00, 9.4832620e+01, 3.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.2010102e+07, 4.6302450e+00, 9.6472030e+01, 3.0000000e-01,
        0.0000000e+00, 0.0000000e+00],
       ...,
       [2.3030321e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.3030322e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.3030323e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00]])

a = np.delete(a,np.arange(0,a.shape[0])[a[:,0]>23022400],0)

a.shape[0]

np_data = a.copy()

… and here comes the basic plot:

import matplotlib.pyplot as plt

data_2022 = data_np[data_np[:,0]<23010100,:]
data_2022[-10:,:]

array([[2.2123114e+07, 1.6970245e+01, 6.9917000e+01, 2.9700000e+01,
        4.1042553e+01, 2.4500000e+02],
       [2.2123115e+07, 1.6590246e+01, 7.0449690e+01, 3.0000002e+01,
        4.0851063e+01, 1.7500000e+02],
       [2.2123116e+07, 1.5720245e+01, 7.4489390e+01, 2.8800001e+01,
        4.1617020e+01, 9.0000000e+01],
       [2.2123117e+07, 1.4240245e+01, 7.8803360e+01, 2.9100000e+01,
        3.4130035e+01, 1.4000000e+01],
       [2.2123118e+07, 1.3750245e+01, 7.8834650e+01, 2.2800001e+01,
        0.0000000e+00, 0.0000000e+00],
       [2.2123119e+07, 1.3550245e+01, 7.9496610e+01, 7.0000000e+00,
        0.0000000e+00, 0.0000000e+00],
       [2.2123120e+07, 1.3410245e+01, 7.9422300e+01, 2.3000000e+01,
        0.0000000e+00, 0.0000000e+00],
       [2.2123121e+07, 1.2940246e+01, 8.0106064e+01, 3.2000000e+01,
        0.0000000e+00, 0.0000000e+00],
       [2.2123122e+07, 1.2350245e+01, 8.1497530e+01, 5.1000000e+01,
        0.0000000e+00, 0.0000000e+00],
       [2.2123123e+07, 1.2340245e+01, 8.0781570e+01, 9.0000000e+00,
        0.0000000e+00, 0.0000000e+00]])

plt.plot(data_2022[:,0],data_2022[:,1],"b-s")
plt.title("Graph via Matplotlib")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.show()

_images/FIZ228_02_DataVisualization_20_0.png

plt.plot(data_2022[:,1])
plt.show()

_images/FIZ228_02_DataVisualization_21_0.png

filter_1 = (data_np[:,0]>=23010100) & (data_np[:,0]<23020100)
plt.plot(data_np[filter_1,0],data_np[filter_1,1],"b-s")
plt.title("Graph via Matplotlib")
plt.xlabel("January 2023")
plt.ylabel("Temperature")
plt.show()

_images/FIZ228_02_DataVisualization_22_0.png

Exporting a numpy array as a CSV file¶

While we are at it, here is how we can export a numpy array as CSV:

np.savetxt('del_this_file.csv', data_np, delimiter = ",")

Importing a CSV file with Pandas¶

Now that we have experienced the pains of the “old” method, let’s revive the technique we have acquired last week: using Pandas to hold the data in a dataframe!

import pandas as pd

pd.set_option('display.min_rows', 10)
pd.set_option('display.max_rows', 10)
data1 = pd.read_csv("data/01_meteoblue_Basel_20230303T060433.csv",
                                         skiprows=9)
data1.columns = ['Timestamp','Temperature','Relative Humidity',
                 'Cloud Coverage', 'Sunshine Duration','Radiation']
data1 = data1.set_index('Timestamp')
data1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation
Timestamp
20220101T0000	6.060245	94.19782	1.5	0.0	0.0
20220101T0100	5.560245	94.83262	3.0	0.0	0.0
20220101T0200	4.630245	96.47203	0.3	0.0	0.0
20220101T0300	3.660245	97.11870	1.2	0.0	0.0
20220101T0400	3.880245	96.16575	1.0	0.0	0.0
...	...	...	...	...	...
20230303T1900	NaN	NaN	NaN	NaN	NaN
20230303T2000	NaN	NaN	NaN	NaN	NaN
20230303T2100	NaN	NaN	NaN	NaN	NaN
20230303T2200	NaN	NaN	NaN	NaN	NaN
20230303T2300	NaN	NaN	NaN	NaN	NaN

10248 rows × 5 columns

Even though, it is completely possible to plot dataframe using matplotlib there’s actually a much better way to do it: enter the seaborn module!

import seaborn as sns
sns.set_theme() # To make things appear "more cool" 8)

data1.loc[:,"Relative Humidity"].max()

100.06447

data1.loc[:,"Sunshine Duration"].max()

60.0

filter_202208w1 = ((data1.index>="20220801") & 
                 (data1.index<"20220808"))
data_202208w1 = data1.loc[filter_202208w1].copy()
data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0
...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0

168 rows × 5 columns

data_202208w1.shape

(168, 5)

Here, it’s as simple as it gets! We are just letting seaborne to figure out what we need:

plt1 = sns.relplot(data=data_202208w1)

_images/FIZ228_02_DataVisualization_35_0.png

Plotting a specific column¶

We can easily designate columns to be used for the x & y parameters for our graph:

plt2 = sns.relplot(data=data_202208w1,x="Temperature",y="Relative Humidity")

_images/FIZ228_02_DataVisualization_37_0.png

And here is a beauty: by hue and size parameters, we can classify using other column values, making it easier to investigate the dependencies wrt these columns:

plt3 = sns.relplot(data=data_202208w1,x="Temperature",y="Relative Humidity",
                  hue="Temperature",size="Relative Humidity")

_images/FIZ228_02_DataVisualization_39_0.png

And this is our attempt to further classify things by adding the style alas it kind of fails

plt3 = sns.relplot(data=data_202208w1,x="Temperature",y="Relative Humidity",
                  style="Temperature")

_images/FIZ228_02_DataVisualization_41_0.png

Seems that it doesn’t like so many classification wrt the values. Luckily we can work around it, by smoothing things out! 8)

import numpy as np

data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0
...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0

168 rows × 5 columns

print("T_min: {:.6f}C | T_max: {:.3f}C"
      .format(data_202208w1.Temperature.min(),data_202208w1.Temperature.max()))

T_min: 15.010245C | T_max: 37.380C

data_202208w1[data_202208w1.Temperature == data_202208w1.Temperature.min()]

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation
Timestamp
20220807T0500	15.010245	61.463127	3.0	0.0	0.0

print(data_202208w1.index[data_202208w1.Temperature == data_202208w1.Temperature.min()][0])

20220807T0500

data_202208w1.Temperature/10

Timestamp
20220801T0000    2.235025
20220801T0100    2.114025
20220801T0200    1.902025
20220801T0300    1.728025
20220801T0400    1.633025
                   ...   
20220807T1900    2.521025
20220807T2000    2.375025
20220807T2100    2.229024
20220807T2200    2.099025
20220807T2300    1.784025
Name: Temperature, Length: 168, dtype: float64

np.floor(data_202208w1.Temperature / 10.0) * 10

Timestamp
20220801T0000    20.0
20220801T0100    20.0
20220801T0200    10.0
20220801T0300    10.0
20220801T0400    10.0
                 ... 
20220807T1900    20.0
20220807T2000    20.0
20220807T2100    20.0
20220807T2200    20.0
20220807T2300    10.0
Name: Temperature, Length: 168, dtype: float64

data_202208w1.Temperature

Timestamp
20220801T0000    22.350245
20220801T0100    21.140245
20220801T0200    19.020245
20220801T0300    17.280245
20220801T0400    16.330246
                   ...    
20220807T1900    25.210245
20220807T2000    23.750246
20220807T2100    22.290245
20220807T2200    20.990246
20220807T2300    17.840246
Name: Temperature, Length: 168, dtype: float64

Here we add a new column TempFloor that stores the smoothed out temperature values:

tempsf = np.floor(data_202208w1.loc[:,"Temperature"] / 10.0) * 10
data_202208w1.loc[:,"TempFloored"] = tempsf.loc[:]

data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation	TempFloored
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0	20.0
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0	20.0
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0	10.0
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0	10.0
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0	10.0
...	...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0	20.0
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0	20.0
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0	20.0
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0	20.0
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0	10.0

168 rows × 6 columns

plt4 = sns.relplot(data=data_202208w1,x="Temperature",y="Relative Humidity",
                  style="TempFloored")

_images/FIZ228_02_DataVisualization_54_0.png

Enough with the scatter plots, lets connect the dots with the kind parameter:

plt4 = sns.relplot(data=data_202208w1,x="Timestamp",y="Temperature", 
                   kind="line", marker="^")

_images/FIZ228_02_DataVisualization_56_0.png

Here is the same thing without the markers:

plt4 = sns.relplot(data=data_202208w1,x="Timestamp",y="Temperature", 
                   kind="line")

_images/FIZ228_02_DataVisualization_58_0.png

data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation	TempFloored
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0	20.0
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0	20.0
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0	10.0
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0	10.0
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0	10.0
...	...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0	20.0
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0	20.0
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0	20.0
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0	20.0
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0	10.0

168 rows × 6 columns

Let’s further classify such that those entries with their humidity above the mean value will be labeled as “humid”, whereas those below will be “dry”.

Therefore, we have to start with calculating the mean:

data_202208w1["Relative Humidity"].mean()

52.46707050595234

and we define a new column for the job:

data_202208w1['RHClass'] = 0

data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation	TempFloored	RHClass
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0	20.0	0
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0	20.0	0
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0	10.0	0
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0	10.0	0
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0	10.0	0
...	...	...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0	20.0	0
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0	20.0	0
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0	20.0	0
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0	20.0	0
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0	10.0	0

168 rows × 7 columns

How do we single out the ones that have their humidity above the average? By filtering of course! 8)

filter_2 = data_202208w1['Relative Humidity']>52

data_202208w1.loc[filter_2,'RHClass']

Timestamp
20220801T0100    0
20220801T0200    0
20220801T0300    0
20220801T0400    0
20220801T0500    0
                ..
20220807T0700    0
20220807T0800    0
20220807T0900    0
20220807T1000    0
20220807T2300    0
Name: RHClass, Length: 82, dtype: int64

filter_2

Timestamp
20220801T0000    False
20220801T0100     True
20220801T0200     True
20220801T0300     True
20220801T0400     True
                 ...  
20220807T1900    False
20220807T2000    False
20220807T2100    False
20220807T2200    False
20220807T2300     True
Name: Relative Humidity, Length: 168, dtype: bool

np.invert(filter_2)

Timestamp
20220801T0000     True
20220801T0100    False
20220801T0200    False
20220801T0300    False
20220801T0400    False
                 ...  
20220807T1900     True
20220807T2000     True
20220807T2100     True
20220807T2200     True
20220807T2300    False
Name: Relative Humidity, Length: 168, dtype: bool

So, we fill the ‘RHClass’ column of the ones above the mean with “humid”; and with “dry” for the others (please observe how we invert the booleans with the “invert”).

data_202208w1.loc[filter_2,'RHClass'] = 'humid'
data_202208w1.loc[np.invert(filter_2),'RHClass'] = 'dry'

data_202208w1

	Temperature	Relative Humidity	Cloud Coverage	Sunshine Duration	Radiation	TempFloored	RHClass
Timestamp
20220801T0000	22.350245	48.665290	4.000000	0.000000	0.0	20.0	dry
20220801T0100	21.140245	52.629486	1.000000	0.000000	0.0	20.0	humid
20220801T0200	19.020245	61.299694	1.000000	0.000000	0.0	10.0	humid
20220801T0300	17.280245	68.006490	0.000000	0.000000	0.0	10.0	humid
20220801T0400	16.330246	72.687810	0.000000	0.000000	0.0	10.0	humid
...	...	...	...	...	...	...	...
20220807T1900	25.210245	45.069187	27.000002	42.765953	135.0	20.0	dry
20220807T2000	23.750246	45.444828	27.000002	41.138474	20.0	20.0	dry
20220807T2100	22.290245	48.713924	27.000002	0.000000	0.0	20.0	dry
20220807T2200	20.990246	51.926308	28.200000	0.000000	0.0	20.0	dry
20220807T2300	17.840246	68.711380	9.900001	0.000000	0.0	10.0	humid

168 rows × 7 columns

plt5 = sns.relplot(data=data_202208w1,x="Timestamp",y="Temperature",\
                   kind="line", 
                   style="RHClass", hue="RHClass")

_images/FIZ228_02_DataVisualization_73_0.png

(plt5.map(plt.axhline,y = 22.5, color=".5", dashes=(2, 1), zorder=0)
.set_axis_labels("Day Hour", "Temperature")
.fig.suptitle("Test Graph"))

Text(0.5, 0.98, 'Test Graph')

_images/FIZ228_02_DataVisualization_74_1.png

plt5 = sns.relplot(data=data_202208w1,x="Timestamp", y="Temperature", 
                   kind="line", col="RHClass")

_images/FIZ228_02_DataVisualization_75_0.png

Histogram Plots¶

Histogram bars are also essential - especially if we are dealing with distributions.

plt6 = sns.displot(data=data_202208w1,x="Temperature",
                   col="RHClass",bins=10)

_images/FIZ228_02_DataVisualization_77_0.png

data_g = np.random.normal(0,10,1000)

data_g

array([ 6.23445374e+00, -1.39502855e+01, -8.23092866e+00,  1.06544818e+01,
       -3.75146300e+00,  3.32420517e+00,  6.64583624e-01, -4.68513881e+00,
       -4.98497977e+00, -1.26077991e+00, -7.64907626e+00, -9.72748545e+00,
        1.92263957e+01,  1.48667218e+01,  7.34159352e+00,  1.23832775e+01,
       -3.26892558e+00,  4.94547970e+00, -3.69063004e+00, -1.93219398e+01,
        2.14299666e+01, -6.57022644e+00,  9.80453668e+00, -7.18388209e+00,
       -4.04037061e+00,  6.08738067e+00, -5.22401181e+00, -1.42976641e+00,
        7.09410533e+00,  1.32947444e+00,  4.30973119e+00, -1.37321883e+01,
       -1.88733215e+01, -3.28504294e+00, -1.96628865e+00,  2.30622989e+01,
        1.39128422e+01,  1.40891370e+00, -9.33600543e+00,  1.04919787e+01,
       -9.02427975e+00,  8.98677166e+00, -1.14709717e+01,  1.87678364e+01,
       -6.94344393e+00,  8.81493228e+00, -1.49147997e+01, -3.77505525e+00,
        7.62177423e+00,  4.89495272e-01, -1.98673983e+01, -1.13192553e+01,
       -2.04586381e+01,  1.24302803e+01,  5.81493750e+00,  4.31605143e-01,
       -2.32056953e+00,  1.59693627e+00, -8.80748408e+00,  8.32248073e+00,
       -4.51372686e+00, -1.37569833e+01, -9.10321599e+00, -9.25602520e+00,
       -1.06978353e+01,  1.04163147e+01, -3.26675934e+00,  1.03983303e+01,
        1.72776001e+01, -1.27322339e+01, -1.85660091e+00, -1.26895705e+01,
       -1.00618572e+00,  2.58107169e+00, -4.27362432e-02, -7.36536684e+00,
        9.88352871e+00,  7.96049525e+00, -4.65054797e+00,  1.46904648e+01,
        5.48304537e+00, -1.57010321e+01,  1.37958376e+00, -2.07688520e+01,
       -1.11889429e+00,  1.18593076e+01, -4.12181483e+00,  1.85010028e+01,
       -6.06228920e-01, -1.49246517e+01, -2.18903475e+01,  1.59453018e+00,
        3.39793297e-01,  5.88182830e+00, -2.29622031e+00,  1.84857575e+01,
        9.53363234e+00, -4.48307416e+00,  2.18499596e+00, -1.03949179e+01,
       -1.08798819e+01, -1.72250150e+01,  1.59179725e+00, -5.86271494e+00,
        3.80143975e+00,  7.09630678e+00,  1.22733204e+01,  1.13708378e+01,
        1.32922762e+01,  6.75763570e+00, -3.04883073e+00, -1.81387446e+01,
        1.29978861e+01, -7.50085897e+00, -1.10276914e+00,  4.58489634e+00,
       -1.20272200e+01, -6.17350076e+00, -3.40748027e+00, -1.96507043e+01,
        5.79614538e+00,  1.37013808e+01,  1.11279589e+01,  4.38952976e-01,
        6.39552701e+00,  4.63781567e+00,  6.18426113e+00,  5.36920325e+00,
        3.01462809e+00,  5.27253941e+00, -9.42627943e+00,  7.13984464e-01,
       -1.29520355e+00,  6.68723285e+00, -7.88690003e-01,  8.32413459e+00,
       -4.98686028e+00,  5.96928063e-01,  1.15291352e+00, -2.37287850e+01,
        2.89397550e+00, -4.94805007e+00, -5.55666756e-01, -2.94290886e+00,
       -1.54423448e+01, -1.97460080e+00, -8.56213739e+00, -1.70638033e+00,
       -5.67241426e+00, -1.52613697e-01,  5.33160669e+00,  5.53327383e+00,
       -1.07286522e+00, -1.75252153e+01, -1.54690194e+01,  1.32502582e+01,
       -1.49219657e+01,  7.23499656e+00, -9.05140046e+00, -4.41562525e+00,
        2.36363061e+00, -2.46074923e+00, -1.48159253e+00,  2.89604166e+00,
        5.04506785e+00, -7.52560797e-02,  2.18125144e+00, -5.13521399e+00,
       -3.63279338e+00,  4.65087642e-01, -3.72920057e+00, -6.49326219e+00,
       -1.48942594e+00, -8.26748513e+00, -6.83742646e+00, -4.53323990e+00,
       -1.06030930e+01, -1.43407467e+01, -8.04754389e-01,  1.28691335e+00,
        3.24727378e+00,  2.77969193e+00,  2.96899631e+00, -1.10131693e+01,
       -9.82451617e+00,  3.71439650e+00,  2.65728033e+00,  3.80327885e+00,
       -2.51748873e+01,  2.75107233e+00,  6.45126081e+00, -3.14476119e+00,
       -9.07932543e-01, -1.10069723e+01, -4.56593800e+00, -3.07318308e+00,
        5.92096492e+00, -2.31219274e-01, -5.25823821e+00, -2.12843796e+00,
       -7.42733547e+00, -1.04222252e+01, -1.63070372e+00, -3.89481987e-01,
       -1.11398643e+01, -5.69436674e+00,  2.77683498e-01,  6.07046566e+00,
       -2.72743394e+00,  1.40848073e+01, -1.26833745e+01,  1.20830112e+01,
       -1.25331628e+01,  5.33466958e+00,  1.30678976e+01,  5.91722666e+00,
        1.94386192e+01,  1.76063534e+01,  8.72078886e-01, -3.38425189e+00,
        8.21145012e+00, -1.45629263e+01, -1.49651412e+01, -1.27445538e+00,
        5.87293131e+00, -4.14242936e+00,  2.43437999e+01,  1.15768116e+01,
        6.11726384e+00, -6.95070166e+00, -1.06027577e+01,  1.29018498e+01,
        4.31654406e+00,  4.51249794e+00, -1.23268120e+00, -1.95143103e+00,
        3.54720946e+00,  9.43812715e+00,  2.94924910e+01, -6.55034451e+00,
        2.90355822e+00, -4.02653559e+00,  1.94206781e+01,  1.28089318e+01,
        4.89834484e+00, -4.30300346e+00, -1.52139597e+01,  2.28107329e+00,
       -7.61286596e-01, -7.16331250e+00,  2.36225412e+00, -2.07704961e+01,
       -1.78871312e+01, -1.18206555e+00, -7.77102765e+00,  1.41174776e+01,
       -1.60406415e+01, -5.93171012e+00, -1.31208023e+01, -1.26026011e+01,
       -2.88341696e+00,  1.09211167e+01,  2.59879422e+01, -2.06777275e+01,
        9.31729128e+00, -2.23137818e+00,  8.90442244e+00, -3.88658302e+00,
        1.05975305e+01,  1.58745260e+01,  6.95951879e+00, -1.00482361e+01,
       -3.21159401e+00, -2.57481923e+00, -3.52200041e+00,  2.76162842e+00,
        9.51275084e+00, -6.79062236e+00,  8.04317307e+00,  3.89199955e+00,
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       -5.29097637e-01, -4.28305980e+00, -3.05932805e+00, -1.41077402e+01,
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       -8.38317988e-01,  1.30127432e+01, -5.61506272e+00, -6.28929183e+00])

plt_gauss = sns.displot(data_g,bins=20,color="r",kde=True,rug=True,)

_images/FIZ228_02_DataVisualization_80_0.png

Summary / Practical Case¶

‘Old Style’ plot parameters¶

x_val = np.linspace(-4,5,20)
y_val = x_val**2-2*x_val-7

df_xy = pd.DataFrame({'xx':x_val,'yy':y_val})

plt_xy = sns.relplot(data=df_xy,x='xx',y='yy')

_images/FIZ228_02_DataVisualization_84_0.png

plt_xy = sns.relplot(data=df_xy,x='xx',y='yy',
                     kind="line",marker="d",
                    markersize=9,markerfacecolor="red",
                    markeredgecolor="green",
                    color="gray",linestyle="--",linewidth=3)
plt.xlabel('x values')
plt.ylabel('y values')
plt.title(r'$x^2-2x-7$')
plt.show()

_images/FIZ228_02_DataVisualization_85_0.png

Pretty much all the useful set¶

N  = 10
data2 = pd.DataFrame(np.empty((N*N,3),int),columns=['x','y','val'])
k = 0
for i in range(N):
    for j in range(N):
        data2.iloc[k,:] = [i,j,np.random.rand()]
        k += 1
data2

	x	y	val
0	0	0	0.431782
1	0	1	0.065295
2	0	2	0.447773
3	0	3	0.591480
4	0	4	0.202280
...	...	...	...
95	9	5	0.152586
96	9	6	0.630253
97	9	7	0.186234
98	9	8	0.898645
99	9	9	0.865413

100 rows × 3 columns

data2['xymod'] = np.mod(data2.x+data2.y,5)
data2

	x	y	val	xymod
0	0	0	0.431782	0
1	0	1	0.065295	1
2	0	2	0.447773	2
3	0	3	0.591480	3
4	0	4	0.202280	4
...	...	...	...	...
95	9	5	0.152586	4
96	9	6	0.630253	0
97	9	7	0.186234	1
98	9	8	0.898645	2
99	9	9	0.865413	3

100 rows × 4 columns

plt2 = sns.relplot(data=data2,x='x',y='y',hue='val',
                       size='val',style='xymod')
#k=plt.legend(bbox_to_anchor=(1.8,1.01),loc='upper right')
#plt.show()

_images/FIZ228_02_DataVisualization_89_0.png

plt3 = sns.relplot(data=data2,x='x',y='y',hue='val',
                       size='val',style='xymod',col=np.mod(data2.xymod,2))
plt.show()

_images/FIZ228_02_DataVisualization_90_0.png

References¶

meteoblue.com - Historical Weather Data for Basel

Importing, parsing, processing and exporting datasets

Least Squares Method & Error Estimations

FIZ228 - Numerical Analysis Lecture Notes