Series type of Pandas library

Series type consists of a groupdataAnd related data Index (automatic index, can also be set) composition.

Example 1 (list creation Series type):

import pandas as pd
a = pd.Series([9,8,7,6])
a

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Example 2:

If the index position is the second place,'index=' can be omitted

import pandas as pd
b = pd.Series([9,8,7,6],
		index=['a','b','c','d'])
b

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Series types can be created by the following types:

Python list
Scalar value
Python dictionary
ndarray
Other functions (range() function)

1. Scalar value creation

Cannot omit'index='

import pandas as pd
s = pd.Series(25,index=['a','b','c'])

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2. Dictionary type creation

import pandas as pd
d = pd.Series({
    
    'a':9,'b':8,'c':7})

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import pandas as pd
c = pd.Series({
    
    'a':9,'b':8,'c':7},
		index=('c','a','b','d'))
c

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3.ndarray creation

import pandas as pd
import numpy as np
n = pd.Series(np.arange(5))
n

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import pandas as pd
import numpy as np
m=pd.Series(np.arange(5),
	index=np.arange(9,4,-1))
m

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Basic operation of Series type

Similar to ndarray and python dictionary operations, value and index can be manipulated.

import pandas as pd
b = pd.Series([9,8,7,6],
              ['a','b','c','d'])
print(b)
print('b.index:\n', b.index)
print('b.values:\n', b.values)

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Automatic index and custom index coexist. Although the two sets of indexes coexist, they cannot be mixed.

1. After operating on the Series type, it is still the Series type. But when only one value is queried, one value is returned.

2. Similar to dictionary operations, the in, and get methods are retained. in is to query whether a certain key is in it,And in can only judge the automatic index, not the custom index. In the Series type, it is to determine whether an index is in it. get is to extract the value corresponding to an index.
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3.Series alignment operation.
Perform calculations with the same index value, and the final number of elements is the union of all elements.

import pandas as pd
a = pd.Series([1,2,3],['c','d','e'])
b = pd.Series([9,8,7,6],
              ['a','b','c','d'])
print(a+b)

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4.Series type name attribute

import pandas as pd
b = pd.Series([9,8,7,6],
              ['a','b','c','d'])
b.name = 'Series对象'
b.index.name = '索引列'
print(b)

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5.
Series type modification Series objects can be modified at any time and take effect immediately

import pandas as pd
b = pd.Series([9,8,7,6],
              ['a','b','c','d'])
b['a']=15
b.name = 'Series'
print(b)

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import pandas as pd
b = pd.Series([9,8,7,6],
              ['a','b','c','d'])
b['b','c'] = 20
b.name = 'New Series'
print(b)

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