import datetime as dt
from dateutil.relativedelta import relativedelta
import pandas as pd
d1=dt.datetime(2021, 5, 16)
print ('d1==',d1)
d2=d1.replace(year=d1.year+1)
print ('d2==',d2)
d3=d1+relativedelta(years=1)
print ('d3==',d3)
d4=d1+pd.DateOffset(years=1)
print ('d4==',d4)d1== 2021-05-16 00:00:00
d2== 2022-05-16 00:00:00
d3== 2022-05-16 00:00:00
d4== 2022-05-16 00:00:00import matplotlib.ticker as ticker
ax.xaxis.set_major_locator(ticker.MultipleLocator(2))base=100
interest=0.1
years=1
repayment=base*(1+interest)**years
print(repayment)def deposite(base,years,interest=0.1):
return base*(1+interest)**years
print(deposite(100,1)A = np.arange(0, 15).reshape(5, 3)
B=A.ravel()D=A.flatten()df['С'] = [s.replace('..','.') for s in df['A']]def func(x):
return x.replace('..','.')
df['B']=df['A'].apply(func)/
A B С
0 25.05..2001 25.05.2001 25.05.2001
1 25.06.2001 25.06.2001 25.06.2001
2 25.43.2004 25.43.2004 25.43.2004
3 05.02.2005 05.02.2005 05.02.2005
4 27.02.2008 27.02.2008 27.02.2008df.ef[(df.Свыше==65) & (df.До==80)] import random
lt = ['q1','q2', 'q3']
res=[]
for i in range(10000):
rnd=random.random()
if rnd<0.2:
res.append(lt[0])
elif rnd<0.5:
res.append(lt[1])
else:
res.append(lt[2])
print(res.count(lt[0]),res.count(lt[1]),res.count(lt[2]))2018 3020 4962 s = 'ABCDE'
print(s.lower())
print(s.lower)abcde
<built-in method lower of str object at 0x00000201495A68B0>a=s.lower()
b=s.lowerb=s.lower
b()'abcde' 