Знакомство с Python¶
библиотеки¶
In [1]:
import time
import math
from itertools import combinations
Стандартные типы¶
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!touch somefile
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a = 1
b = 1.1
c = 'abc'
d = True
e = True and False
f = open('somefile')
g = c+c
h = c*3
a+=1
a,b,c,d,e,f,g,h
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Ветвления¶
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for i in range(10):
print(i)
if True:
print('this code runs')
else:
print('This not')
функции¶
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def power_3(x):
return x**3
print(power_3(10))
Коллекции¶
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somelist = [5,5,9,2,3,5]
someotherlist = ['d','g',5,True,False, [5,3]]
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sometuple = (5,6,7)
someothertuple = ('a','b',6)
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somedict = {'a':5,
'b':9,
9:'g'
}
somedict
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In [9]:
print(id(somelist))
somelist.append(5)
print(id(somelist))
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[1,2,3] + [4,5,6]
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[x**2 for x in [7,6,5,3]]
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In [12]:
a = [4,5,6]
a.append(7)
a
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In [13]:
a = [x**2 for x in range(0,21,2)]
a
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a[2]
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a[3:6]
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In [16]:
a[::2]
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In [17]:
a,b,c = sometuple
print(sometuple)
print(a)
print(b)
print(c)
Numpy¶
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import numpy
numpy.__version__
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In [19]:
import numpy as np
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np.array([1, 4, 2, 5, 3])
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np.array([3.14, 4, 2, 3])
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np.zeros(10, dtype=int)
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np.ones((3, 5), dtype=float)
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np.linspace(0, 10, 101)
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np.random.random((5, 5))
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np.arange(10)
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a = np.linspace(0, 10, 101)
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a.shape
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a[5]
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a[3:10]
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a[-10:-1]
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arr = np.arange(16)
arr
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grid = arr.reshape((4, 4))
grid
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upper, lower = np.vsplit(grid, [2])
print(upper)
print(lower)
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left, right = np.hsplit(grid, [2])
print(left)
print(right)
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np.min(grid)
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np.max(grid)
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np.sqrt(grid)
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np.power(grid, 2)
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Pandas¶
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import pandas as pd
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s = pd.Series([1,3,5,np.nan,6,8])
s
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In [42]:
dates = pd.date_range('20130101',periods=6)
dates
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df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
df
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In [44]:
df.A
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df['A']
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df.loc['2013-01-01']
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df.applymap(lambda x: x+1)
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df.applymap(lambda x: x>0)
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df>0
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%matplotlib inline
df.plot(figsize=(15,15))
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df.groupby(pd.Grouper(freq='2d')).mean()
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df.groupby(pd.Grouper(freq='3d')).mean()
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In [53]:
df_1 = pd.concat([df,df], axis=1)
df_1
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In [54]:
df_1.columns = ['A','B','C','D','E','F','G','H']
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df_1.A+df_1.B
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df_1.A + df_1.B - df_1.C - df_1.D
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df_1.loc['2013-01-04', 'D']
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In [58]:
df_1.loc['2013-01-04', 'D'] = 100
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df_1
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df_1.describe()
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In [61]:
df_1.shape
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In [62]:
df_1.info(memory_usage='deep')