Python Visualization

Question 1

import matplotlib.pyplot as plt

Answer 1

Ikeliame matplot biblioteka

Question 2

%matplotlib inline

Answer 2

Naudojama Jupyter Notebook ,kad iskart isvestu diagramas

Question 3

plt.show

Answer 3

Rasome norint isvesti diagrama

Question 4

functional ,nerekomenduojamas budas

plt.plot(x,y)
plt.xlabel(‘X Label’)
plt.ylabel(‘Y Label’)
plt.title(‘Title’)
plt.show()

Answer 4

Funkcionalus budas isvesti paprasta tiesine diagrama

Question 5

plt.xlabel(‘X Label’)

Answer 5

Sukuriamas pavadinimas x asiai

Question 6

plt.ylabel(‘Y Label’)

Answer 6

Sukuriamas pavadinimas y asiai

Question 7

plt.title(‘Title’)

Answer 7

Sukuriamas pavadinimas diagramai

Question 8

Object oriantated
fig=plt.figure()

axes = fig.add_axes([0.1,0.1,0.8,0.8])

axes.plot(x,y)
axes.set_xlabel(‘X Label’)
axes.set_ylabel(‘Y Label’)
axes.set_title(‘Title’)

Answer 8

Rekomenduojamas budas kuriant diagramas

Question 9

fig=plt.figure()

Answer 9

Sukuriama diagramos kintamasis

Question 10

axes = fig.add_axes([0.1,0.1,0.8,0.8])

Answer 10

Pridedamos kordinaciu asys ir ju matmenys [x pradzia,y pradzia,ilgis,plotis]

Question 11

axes.plot(x,y)

Answer 11

I kordinaciu plokstuma iterpiama diagrama

Question 12

fig,axes=plt.subplots(nrows=3,ncols=3)

Answer 12

Naudojama norint sukurti kelias vienodas kordinaciu plokstumas

Question 13

plt.tight_layout()

Answer 13

Neleidzia overlappinti

Question 14

fig,axes=plt.subplots(nrows=1,ncols=2)
for current_ax in axes:
current_ax.plot(x,y)

Answer 14

Galima iteruoti per cikla

Question 15

fig,axes=plt.subplots(nrows=1,ncols=2)
axes[0].plot(x,y)
axes[0].set_title(‘First Plot’)
axes[1].plot(y,x)
axes[1].set_title(“Second Plot”)
plt.tight_layout

Answer 15

Galima pasirinkti i kuria kordinaciu plokstuma iterpti diagrama

Question 16

fig=plt.figure(figsize=(8,2))

Answer 16

Leidzia nustatyti diagramos dydi

Question 17

ax.legend()

fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.plot(x,x2,label=’X squared’)
ax.plot(x,x3,label=”X cubed”)
ax.legend()

Answer 17

Norint iterpti legenda butinai reikia diagramas uzvadinti label

Question 18

import seaborn as sns

Answer 18

Importuojam seaborn biblioteka

Question 19

sns.displot(tips[‘total_bill’],kde=False,bins=40)

Answer 19

Sukuriame paprasta diagrama
kde= false nuima linija
bins nustato diagramos bar dydi

Question 20

sns.jointplot(x=’total_bill’,y=’tip’,data=tips,kind=’hex’)

Answer 20

Sukuriamos viena didele diagrama ant kurios yra 3 mazos .ant x ir y asies paprasta stulpeline diagrama o viduje scatter plot .Scatter plot galima pakeisti i hex,kde ,reg… naudojant kind=’ ‘

Question 21

sns.pairplot(tips,hue=’sex’,palette=’coolwarm’)

Answer 21

Sukuria 9 diagramas 6 scatter ir 3 paprastas
galima pakonkretinti kokius duomenis naudoti su hue ,o palette parinkti spalvas

Question 22

sns.rugplot(tips[‘total_bill’])

Answer 22

Sukuria diagrama su mazais stulpeliais ant x asies

Question 23

sns.barplot(x=’sex’,y=’total_bill’,data=tips,estimator=np.std)

Answer 23

Sukuria bar diagrama
estimator =’ ‘ Galima panaudoti kazkokia sukurta funkcija

Question 24

sns.boxplot(x=’day’,y=’total_bill’,data=tips,hue=’smoker’)

Answer 24

Sukuria box diagrama

Question 25

sns.violinplot(x=’day’,y=’total_bill’,data=tips,hue=’sex’,split=True)

Answer 25

Sukuria violin diagrama .split=’ ‘ sulygina stulpeliu duomenis

Question 26

sns.stripplot(x=’day’,y=’total_bill’,data=tips,jitter=True,hue=’sex’,split=True)

Answer 26

Sukuria diagrama is taskeliu

Question 27

sns.swarmplot(x=’day’,y=’total_bill’,data=tips,color=’black’)
sns.violinplot(x=’day’,y=’total_bill’,data=tips)

Answer 27

Sukuria dvi diagramos ,kurios susijungia

Question 28

sns.factorplot(x=’day’,y=’total_bill’,data=tips,kind=’bar’)

Answer 28

Sukuria tam tikra diagrama naudojant kind = ‘ ‘

Question 29

sns.heatmap(tc,annot=True,cmap=’coolwarm’)

Answer 29

Sukuria heatmap

Question 30

fp=flights.pivot_table(index=’month’,columns=’year’,values=’passengers’)

Answer 30

Sukuriama isrusiuota matrixa

Question 31

sns.heatmap(fp,cmap=’magma’,linecolor=’green’,linewidths=1)

Answer 31

Sukuria heatmap
cmap =’ ‘ spalva
linecolor=’ ‘ atskirimo liniju spalva

Question 32

sns.clustermap(fp,cmap=’coolwarm’,standard_scale=1)

Answer 32

Sukuriama clustermap

Question 33

g=sns.PairGrid(iris)
g.map_diag(sns.distplot)
g.map_upper(plt.scatter)
g.map_lower(sns.kdeplot)

Answer 33

Sukuria 16 diagramu
g=sns.PairGrid() sukuria tesiog 16 tusciu grid
g.map_diag(sns.distplot) uzpildo grid istrizaine pasirinktom diagramom
g.map_upper(plt.scatter) uzpildo desni virsutini trikampi(6 desinies puses grid laukelius)
g.map_lower(sns.kdeplot) uzplido kairinius apatini trikampio laukelius

Question 34

g= sns.FacetGrid(data=tips,col=’time’,row=’smoker’)
g.map(sns.distplot,’total_bill’)

Answer 34

Sukuriamas diagramu grid ,pagal pasirinkta stulpeli ir eilute

Question 35

g= sns.FacetGrid(data=tips,col=’time’,row=’smoker’)
g.map(plt.scatter,’total_bill’,’tip’)

Answer 35

Sukuriamas diagramu grid ,pagal pasirinkta stulpeli ir eilute ,tik kad uzpildome scatter diagrama

Question 36

sns.lmplot(x=’total_bill’,y=’tip’,data=tips,hue=’sex’,markers=[‘o’,’v’],scatter_kws={‘s’:100})

Answer 36

Sukuria linear regression rodancia diagrama .

Question 37

sns.lmplot(x=’total_bill’,y=’tip’,data=tips,col=’day’,row=’time’,hue=’sex’,aspect=0.6,size=8)

Answer 37

Sukuria kelias linear regression rodancia diagrama .

Question 38

sns.set_style(‘ticks’)

Answer 38

Nustato ,kad diagrama turetu remus

Question 39

sns.despine(left=True,bottom=True)

Answer 39

Nuema remus

Question 40

sns.set_context(‘poster’,font_scale=1)

Answer 40

Leidzia pasirinkti dizaina (notebook,poster)