熟悉pandas的兩個工具資料結構:Series和DataFrame

Series

Series是一種一維的陣列物件，它包含了一個值序列（與Numpy中的型別相似），並且包含了資料標籤，稱為索引（index）

建立一個Series物件

import pandas as pd

obj = pd.Series([4,7,-5,3])
obj
0    4
1    7
2   -5
3    3
dtype: int64

• 左邊的是索引，右邊是值。預設索引從0到N-1（N是資料的長度）

• 可以通過values屬性和index屬性獲得值和索引

  obj.values
  array([ 4,  7, -5,  3], dtype=int64)
  obj.index
  RangeIndex(start=0, stop=4, step=1)
   
  

• 可以建立一個索引序列，用標籤標識每個資料點

  obj2 = pd.Series([4,7,-5,3],index=['d','b','a','c'])
  
  obj2
  d    4
  b    7
  a   -5
  c    3
  dtype: int64
  
  obj2.index
  Index(['d', 'b', 'a', 'c'], dtype='object')
  
  obj2['a']
  -5
  

• 使用布林值陣列進行過濾

  obj2[obj2 > 2]
  d    4
  b    7
  c    3
  dtype: int64
  
  np.exp(obj2)
  d      54.598150
  b    1096.633158
  a       0.006738
  c      20.085537
  dtype: float64
   
  

• 可以傳入字典生成Series物件

  sdata = {'Ohio':35000,'Texas':71000,'Oregon':16000,'Utah':5000}
  obj3 = pd.Series(sdata)
  obj3
  obj3
  Ohio      35000
  Texas     71000
  Oregon    16000
  Utah       5000
  dtype: int64
  Texas
  states = ['California','Ohio','Oregon','Texas']
  obj4 = pd.Series(sdata, index=states)
  4
  obj4
  California        NaN
  Ohio          35000.0
  Oregon        16000.0
  Texas         71000.0
  dtype: float64
   
  

• pandas中使用isnull和notnull函式來檢查缺失資料

  pd.isnull(obj4)
  California     True
  Ohio          False
  Oregon        False
  Texas         False
  dtype: bool
  
  pd.notnull(obj4)
  California    False
  Ohio           True
  Oregon         True
  Texas          True
  dtype: bool
  
  
  obj4.isnull()
  California     True
  Ohio          False
  Oregon        False
  Texas         False
  dtype: bool
  

• 自動對齊索引

  obj4
  California        NaN
  Ohio          35000.0
  Oregon        16000.0
  Texas         71000.0
  dtype: float64
      
  obj3
  Ohio      35000
  Texas     71000
  Oregon    16000
  Utah       5000
  dtype: int64
  
  obj3+obj4
  California         NaN
  Ohio           70000.0
  Oregon         32000.0
  Texas         142000.0
  Utah               NaN
  dtype: float64
  

• Series物件自身和其索引都有name屬性

  obj4.name = 'population'
  obj4.index.name = 'state'
  obj4
  state
  California        NaN
  Ohio          35000.0
  Oregon        16000.0
  Texas         71000.0
  Name: population, dtype: float64