Python Pandas

Question 1

df.loc[’ ‘]

Answer 1

Leidzia is lenteles pasirinkti tam tikra eilute stulpeli .Galima pakonkretinti
df.loc[’ ‘].loc[][]

Question 2

df.xs()

Answer 2

Kaip ir df.loc tik lengviau istraukti duomenis is lenteles vidaus
Galima pakonkretinti df.xs(1,level=’Num’)

Question 3

df.dropna()

Answer 3

Istrins visus null reiksmes ,bet galima pakonkretinti
df.dropna(thresh=2) dabar istrins tik tuos kurie turi 2 arba daugiau null

Question 4

df.fillna

Answer 4

Uzpildis nulines reiksmes
Galima pakonkretinti
df.fillna(value=’Fill Value’)
arba net uzpildyti pasirinkta nuline reiksme vidurkiu
df[‘A’].fillna(value=df[‘A’].mean())

Question 5

KintPav=df.groupby(‘stulpav’)

Answer 5

Leidzia grupuoti duomenis
sudeti pvz Kompanijos sales
Galima naudoti
byComp.mean()
byComp.sum()
byComp.std()
Pakonkretinti
byComp.sum().loc[‘FB’]
Arba aplamai kad parodytu apibendrinta lentele (vidurkis,kvartiles…)
df.groupby(‘Company’).describe()

Question 6

pd.concat([df1,df2,df3])

Answer 6

Sulipdo dataframe
Galima pakonkretinti
pd.concat([df1,df2,df3],axis=1)

Question 7

pd.merge(left,right,how=’inner’,on=’key’)

Answer 7

Sujungia datagrame per kazkuria reiksme kuria nurodome per on=’ ‘
Galima sujungti per kelias reiksmes
pd.merge(left,right,on=[‘key1’,’key2’])

Question 8

dfpavadinimas.join(kitoDfPav)

Answer 8

Sujungia df per atitinkamas eiluciu reiksmes

Question 9

df[‘col2’].unique()

Answer 9

Isveda visas reiksmes nekartojant ju
Taip pat norint suzinoti kiek ju yra galima naudoti
df[‘col2’].nunique()

Question 10

df[df[‘col1’]>2]
df[(df[‘col1’]>2)&(df[‘col2’]==444)]

Answer 10

Isfiltruoja pagal salyga

Question 11

def times2(x):
return x*2

Answer 11

Funkcijos pavyzdys

Question 12

df[‘col1’].apply(times2)
df[‘col2’].apply(lambda x:x*2)

Answer 12

Naudojant .apply galima panaudoti sukurta,lambda arba esama funkcija dataframe

Question 13

df.columns

Answer 13

Isveda dataframe stulpeliu pavadinimus

Question 14

df.sort_values(‘col2’)

Answer 14

Leidzia ruziuoti pagal pasirinkta stulpeli

Question 15

df.isnull()

Answer 15

Parodo ar dataframe yra nuliniu reiksmiu

Question 16

df.pivot_table(values=”D”,index=[‘A’,’B’],columns=[‘C’])

Answer 16

Sukuriamas pivot table pagal D reiksmes ,indeksuojam pagal a ir b stulpelius ,pasirinke stulpeli C

Question 17

pwd

Answer 17

Parodo kur kompiuteryje failas

Question 18

pd.read_csv(‘example’)

Answer 18

Nuskaito duomenis is csv failo

Question 19

pd.read_excel(‘Excel_Sample.xlsx’,sheet_name=’Sheet1’)

Answer 19

Nuskaito duomenis is excelio failo

Question 20

df.to_csv(‘My_output’,index=False)

Answer 20

Sukuria csv faila ir ten importuoja duomenis

Question 21

df.to_excel(‘Excel_sample2.xlsx’,sheet_name=”NewSheet”)

Answer 21

Sukuria excelio faila ir ten importuoja duomenis

Question 22

df = pd.read_html(‘https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list/’)
df[0]

Answer 22

Nuskaito duomenis is html svetaines

Question 23

from sqlalchemy import create_engine
engine= create_engine(‘sqlite:///:memory:’)
data.to_sql(‘my_table’,engine)
sqldf=pd.read_sql(‘my_table’,con=engine)
sqldf

Answer 23

Sukuriame minimalistine sql engine ,sukuriame faila kur importuosim duomenis ,naudojant ta engine nuskaitome duomenis

Question 24

df1[‘A’].hist(bins=30)

Answer 24

Sukuria histograma

Question 25

df2.plot.area(alpha=0.4)

Answer 25

Sukuria area diagrama

Question 26

df2.plot.bar(stacked=True)

Answer 26

Sukuria stulpeline diagrama

Question 27

df1.plot.scatter(x=’A’,y=’B’,c=’C’,cmap=’coolwarm’)

Answer 27

Sukuria scatter diagrama

Question 28

df2.plot.box()

Answer 28

Sukuria box diagrama

Question 29

df.plot.hexbin(x=’a’,y=’b’,gridsize=25)

Answer 29

Sukuria hex diagrama

Question 30

df2[‘a’].plot.kde()
df2.plot.density()

Answer 30

Diagrama pavaizduoti density

Question 31

df3[[‘a’,’b’]].plot.box()

Answer 31

Sukuriamos dvi boxplot diagramos