In [1]:
import pandas as pd
In [2]:
cities = pd.Series(
    {'Dnepr': 1000000, 'Kiev': 3000000, 'Paris': 2300000, 'Berlin': 3800000},
    index=['Dnepr', 'Paris', 'Berlin', 'Milan']
)
cities
Out[2]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan           NaN
dtype: float64
In [3]:
len(cities)
Out[3]:
4
In [4]:
cities.shape
Out[4]:
(4,)
In [5]:
cities.size
Out[5]:
4
In [6]:
cities.count()
Out[6]:
3
In [7]:
cities2 = cities
In [8]:
cities2.at['Milan'] = 3200000
cities2
Out[8]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan     3200000.0
dtype: float64
In [9]:
cities
Out[9]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan     3200000.0
dtype: float64
In [11]:
cities3 = cities.copy()
cities3
Out[11]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan     3200000.0
dtype: float64
In [12]:
cities2.at['London'] = 9000000
In [13]:
cities
Out[13]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan     3200000.0
London    9000000.0
dtype: float64
In [14]:
cities3
Out[14]:
Dnepr     1000000.0
Paris     2300000.0
Berlin    3800000.0
Milan     3200000.0
dtype: float64
In [15]:
ds1 = pd.Series([1, 2, 3])
ds1
Out[15]:
0    1
1    2
2    3
dtype: int64
In [16]:
ds1.iat[2]
Out[16]:
3
In [17]:
ds1.iloc[2]
Out[17]:
3
In [18]:
cities3.loc['Berlin']
Out[18]:
3800000.0

Агрегация

In [21]:
cities.agg('sum')
Out[21]:
19300000.0
In [22]:
cities.agg('mean')
Out[22]:
3860000.0
In [23]:
19300000 / len(cities)
Out[23]:
3860000.0
In [24]:
cities.min()
Out[24]:
1000000.0
In [26]:
cities.agg(['min', 'max', 'mean', 'sum'])
Out[26]:
min      1000000.0
max      9000000.0
mean     3860000.0
sum     19300000.0
dtype: float64
In [ ]: