The series data type in pandas in detail

import pandas as pd
import numpy as np
import names

'''
Write in front of the words:
  1, series and array type of difference for the series has an index, while the other does not; series in the data must be one-dimensional, while the array type is not necessarily
  2, you can see the series as a fixed-length ordered dictionary, you can get the properties of the series through the shape, index, values and so on.
'''
# 1. series creation
'''
(1) Created from a list or numpy array
    The default index is an integer-type index from 0 to N-1, e.g. s1; the
    The index can be specified by setting the index parameter, such as s2;
    created in this way series, not a copy of the array, that is, the operation of the series at the same time also changed the original array array, such as s3
(2) created by the dictionary
    The key of the dictionary is called the index, the key value is the value, such as s4;
'''
n1 = ([1, 4, 5, 67, 7, 43, ])
s1 = (n1)
# print(s1)
'''
  1
  4
  5
  67
  7
  43
dtype: int32
'''
s2 = (n1, index=['a', 'b', 'c', 'd', 'e', 'f'])
# print(s2)
'''
a   1
b   4
c   5
d  67
e   7
f  43
dtype: int32
'''
# print(n1)
'''
[ 1 4 5 67 7 43]
'''
s1[2] = 100
s3 = s1
# print(s3)
'''
   1
   4
  100
  67
   7
  43
dtype: int32
'''
# print(n1)
'''
[ 1  4 100 67  7 43]
'''
dict1 = {}
for i in range(10, 15):
  # names.get_last_name(), randomly generate English names
  dict1[names.get_last_name()] = i
s4 = (dict1)
# print(s4)
'''
Poole   10
Allen   11
Davis   12
Roland  13
Brehm   14
dtype: int64
'''
# 2. indexing of series
'''
(1) through the index of the value, you can get through the subscript, you can also specify the index to get, such as s6, s7
(2) fetch by .loc[] (display index), this way can only fetch the displayed index, can not be obtained by subscript, such as s7 (recommended)
(3) implicit index, use integer as index value, use .icol[], such as s9 (recommended)
'''
s5 = (([1, 5, 9, 7, 6, 4, 52, 8]), index=[list('abcdefgh')])
# print(s5)
'''
a   1
b   5
c   9
d   7
e   6
f   4
g  52
h   8
dtype: int32
'''
s6 = s5[2]
# print(s6)
'''
'''
s7 = s5['c']
# print(s7)
'''
c  9
dtype: int32
'''
s8 = ['c']
# print(s8)
'''
c  9
dtype: int32
'''
s9 = [2]
# print(s9)
'''
'''
# 3, slicing of series
'''
  1, series slicing and list usage is similar, the difference is that it is recommended to use .loc[:] and .iloc[:], such as s10 and s11, of course, direct use of [:] can also be used.
  2, when encountered a particularly long series, we support the removal of the first 5 or the last 5 data can be directly used .head () or .tail ()
'''
s5 = (([1, 5, 9, 7, 6, 4, 52, 8]), index=[list('abcdefgh')])
# print(s5)
'''
a   1
b   5
c   9
d   7
e   6
f   4
g  52
h   8
dtype: int32
'''
s10 = ['b':'g']
# print(s10)
'''
b   5
c   9
d   7
e   6
f   4
g  52
dtype: int32
'''
s11 = [1:7]
# print(s11)
'''
b   5
c   9
d   7
e   6
f   4
g  52
dtype: int32
'''
# 4, on NaN
'''
  (1) NaN stands for null, but is not the same as None, which has a different data type, <class '.NoneType' > and NaN is of type <class 'float'>；
  （2）It is possible to use(),()，or bring your ownisnull(),notnull()Function detects missing data
'''
# print(type(None),type())
'''
<class 'NoneType'> <class 'float'>
'''
s12 = ([1,2,None,],index=list('Beacon Thunder and Lightning'))
# print(s12)
'''
Beacon 1.0
Fire 2.0
Thunder NaN
Electricity NaN
dtype: float64
'''
# print((s12))
'''
Beacon False
Fire False
Thunder True
Electricity True
dtype: bool
'''
# print((s12))
'''
Beacon True
Fire True
Thunder False
Electricity False
dtype: bool
'''
# print(())
'''
Beacon True
Fire True
Thunder False
Electricity False
dtype: bool
'''
# print(())
'''
Beacon False
Fire False
Thunder True
Electricity True
dtype: bool
'''
# Fetch the value in the series that is not null
# print(s12[()])
'''
Beacon 1.0
Fire 2.0
dtype: float64
'''
# The name attribute of a series
'''

'''
 = 'Feng Shui'
# print(s12)
'''
Beacon 1.0
Fire 2.0
Thunder NaN
Electricity NaN
Name: feng shui, dtype: float64
'''