目錄
- 1 Series
- 1.1創造一個serise數據
- 1.2 指定index
- 1.3 用dictionary構造一個series
- 1.4 用numpy ndarray構造一個Series
- 1.5 選擇數據
- 1.6 操作數據
- 1.7 查找
- 1.8 Series賦值
- 1.9 滿足條件的統一賦值
1 Series
線性的數據結構, series是一個一維數組
Pandas 會默然用0到n-1來作為series的index, 但也可以自己指定index( 可以把index理解為dict里面的key )
1.1創造一個serise數據
import pandas as pd
import numpy as np
s = pd.Series([9, 'zheng', 'beijing', 128])
print(s)
打印
0 9
1 zheng
2 beijing
3 128
dtype: object
訪問其中某個數據
print(s[1:2])
# 打印
1 zheng
dtype: object
Series類型的基本操作：
Series類型包括index和values兩部分
In [14]: a = pd.Series({'a':1,'b':5})
In [15]: a.index
Out[15]: Index(['a', 'b'], dtype='object')
In [16]: a.values #返回一個多維數組numpy對象
Out[16]: array([1, 5], dtype=int64)
Series類型的操作類似ndarray類型
#自動索引和自定義索引并存，但不能混用
In [17]: a[0] #自動索引
Out[17]: 1
#自定義索引
In [18]: a['a']
Out[18]: 1
#不能混用
In [20]: a[['a',1]]
Out[20]:
a 1.0
1 NaN
dtype: float64
Series類型的操作類似Python字典類型
#通過自定義索引訪問
#對索引保留字in操作，值不可以
In [21]: 'a' in a
Out[21]: True
In [22]: 1 in a
Out[22]: False
Series類型在運算中會自動對齊不同索引的數據
In [29]: a = pd.Series([1,3,5],index = ['a','b','c'])
In [30]: b = pd.Series([2,4,5,6],index = ['c,','d','e','b'])
In [31]: a+b
Out[31]:
a NaN
b 9.0
c NaN
c, NaN
d NaN
e NaN
dtype: float64
Series對象可以隨時修改并即刻生效
In [32]: a.index = ['c','d','e']
In [33]: a
Out[33]:
c 1
d 3
e 5
dtype: int64
In [34]: a+b
Out[34]:
b NaN
c NaN
c, NaN
d 7.0
e 10.0
dtype: float64
1.2 指定index
import pandas as pd
import numpy as np
s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])
print(s)
打印
1 9
2 zheng
3 beijing
e 128
f usa
g 990
dtype: object
根據索引找出值
1.3 用dictionary構造一個series
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "car": None}
sa = pd.Series(s, name="age")
print(sa)
打印
car NaN
jack 19.0
mary 18.0
ton 20.0
Name: age, dtype: float64
檢測類型
print(type(sa)) # class 'pandas.core.series.Series'>
1.4 用numpy ndarray構造一個Series
生成一個隨機數
import pandas as pd
import numpy as np
num_abc = pd.Series(np.random.randn(5), index=list('abcde'))
num = pd.Series(np.random.randn(5))
print(num)
print(num_abc)
# 打印
0 -0.102860
1 -1.138242
2 1.408063
3 -0.893559
4 1.378845
dtype: float64
a -0.658398
b 1.568236
c 0.535451
d 0.103117
e -1.556231
dtype: float64
1.5 選擇數據
import pandas as pd
import numpy as np
s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])
print(s[1:3]) # 選擇第1到3個, 包左不包右 zheng beijing
print(s[[1,3]]) # 選擇第1個和第3個, zheng 128
print(s[:-1]) # 選擇第1個到倒數第1個, 9 zheng beijing 128 usa
1.6 操作數據
import pandas as pd
import numpy as np
s = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])
sum = s[1:3] + s[1:3]
sum1 = s[1:4] + s[1:4]
sum2 = s[1:3] + s[1:4]
sum3 = s[:3] + s[1:]
print(sum)
print(sum1)
print(sum2)
print(sum3)
打印
2 zhengzheng
3 beijingbeijing
dtype: object
2 zhengzheng
3 beijingbeijing
e 256
dtype: object
2 zhengzheng
3 beijingbeijing
e NaN
dtype: object
1 NaN
2 zhengzheng
3 beijingbeijing
e NaN
f NaN
g NaN
dtype: object
1.7 查找
是否存在
范圍查找
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(sa[sa>19])
中位數
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(sa.median()) # 20
判斷是否大于中位數
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(sa>sa.median())
找出大于中位數的數
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(sa[sa > sa.median()])
中位數
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
more_than_midian = sa>sa.median()
print(more_than_midian)
print('---------------------')
print(sa[more_than_midian])
1.8 Series賦值
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(s)
print('----------------')
sa['ton'] = 99
print(sa)
1.9 滿足條件的統一賦值
import pandas as pd
import numpy as np
s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None}
sa = pd.Series(s, name="age")
print(s) # 打印原字典
print('---------------------') # 分割線
sa[sa>19] = 88 # 將所有大于19的同一改為88
print(sa) # 打印更改之后的數據
print('---------------------') # 分割線
print(sa / 2) # 將所有數據除以2
到此這篇關于pandas的Series類型與基本操作詳解的文章就介紹到這了,更多相關pandas Series基本操作內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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