Python基礎算法庫及可視化庫使用實踐-大數據ML樣本集案例實戰

時間 2019-12-02

標籤 python 基礎法庫可視化使用實踐數據樣本案例實戰欄目 Python 简体版

原文原文鏈接

1 Numpy詳細使用

讀取txt文件數組

import numpy
  world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
  print(type(world_alcohol))

  world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",", dtype="U75", skip_header=1)
  print(world_alcohol)
  
  [[u'1986' u'Western Pacific' u'Viet Nam' u'Wine' u'0']
   [u'1986' u'Americas' u'Uruguay' u'Other' u'0.5']
   [u'1985' u'Africa' u"Cte d'Ivoire" u'Wine' u'1.62']
   ..., 
   [u'1987' u'Africa' u'Malawi' u'Other' u'0.75']
   [u'1989' u'Americas' u'Bahamas' u'Wine' u'1.5']
   [u'1985' u'Africa' u'Malawi' u'Spirits' u'0.31']]
複製代碼

建立一維和二維的Array數組數據結構

#The numpy.array() function can take a list or list of lists as input. When we input a list, we get a one-dimensional array as a result:
  
  #一維的Array數組[]
  vector = numpy.array([5, 10, 15, 20])
  
  #二維的Array數組[[],[],[]]
  matrix = numpy.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
  print vector
  print matrix
複製代碼

shape用法app

#We can use the ndarray.shape property to figure out how many elements are in the array
  vector = numpy.array([1, 2, 3, 4])
  print(vector.shape)
  
  #For matrices, the shape property contains a tuple with 2 elements.
  matrix = numpy.array([[5, 10, 15], [20, 25, 30]])
  print(matrix.shape)
  
  (4,)
  (2, 3)
複製代碼

dtype用法（numpy要求numpy.array內部元素結構相同）dom

numbers = numpy.array([1, 2, 3, 4])
  numbers.dtype
  
  dtype('int32')
  
  #改變其中一個值時，其餘值都會改變
  numbers = numpy.array([1, 2, 3, '4'])
  print(numbers)
  numbers.dtype
  
 
  ['1' '2' '3' '4']
   dtype('<U11')
複製代碼

索引定位ide

[[u'1986' u'Western Pacific' u'Viet Nam' u'Wine' u'0']
   [u'1986' u'Americas' u'Uruguay' u'Other' u'0.5']
   [u'1985' u'Africa' u"Cte d'Ivoire" u'Wine' u'1.62']
   ..., 
   [u'1987' u'Africa' u'Malawi' u'Other' u'0.75']
   [u'1989' u'Americas' u'Bahamas' u'Wine' u'1.5']
   [u'1985' u'Africa' u'Malawi' u'Spirits' u'0.31']]
   
  uruguay_other_1986 = world_alcohol[1,4]
  third_country = world_alcohol[2,2]
  print uruguay_other_1986
  print third_country
  
  0.5
  Cte d'Ivoire
複製代碼

索引切片函數

vector = numpy.array([5, 10, 15, 20])
  print(vector[0:3])  
  [ 5 10 15]
複製代碼

取某一列（：表示全部行）ui

matrix = numpy.array([
                      [5, 10, 15], 
                      [20, 25, 30],
                      [35, 40, 45]
                   ])
  print(matrix[:,1])
  
  [10 25 40]

  matrix = numpy.array([
                  [5, 10, 15], 
                  [20, 25, 30],
                  [35, 40, 45]
               ])
  print(matrix[:,0:2])
  
  [[ 5 10]
   [20 25]
   [35 40]]
   
  matrix = numpy.array([
              [5, 10, 15], 
              [20, 25, 30],
              [35, 40, 45]
           ])
  print(matrix[1:3,0:2])
  
  [[20 25]
  [35 40]]
複製代碼

對Array操做表示對內部全部元素進行操做this

import numpy
  #it will compare the second value to each element in the vector
  # If the values are equal, the Python interpreter returns True; otherwise, it returns False
  vector = numpy.array([5, 10, 15, 20])
  vector == 10
  
  array([False,  True, False, False], dtype=bool)
  
  matrix = numpy.array([
              [5, 10, 15], 
              [20, 25, 30],
              [35, 40, 45]
           ])
  matrix == 25
  
  array([[False, False, False],
 [False,  True, False],
 [False, False, False]], dtype=bool)
複製代碼

布爾值當索引（[False True False False]）spa

vector = numpy.array([5, 10, 15, 20])
  equal_to_ten = (vector == 10)
  print equal_to_ten
  print(vector[equal_to_ten])
  
  [False  True False False]
  [10]


  #矩陣表示索引
  matrix = numpy.array([
                  [5, 10, 15], 
                  [20, 25, 30],
                  [35, 40, 45]
               ])
  second_column_25 = (matrix[:,1] == 25)
  print second_column_25
  print(matrix[second_column_25, :])
  
  [False  True False]
  [[20 25 30]]
複製代碼

對數組進行與運算

#We can also perform comparisons with multiple conditions
  vector = numpy.array([5, 10, 15, 20])
  equal_to_ten_and_five = (vector == 10) & (vector == 5)
  print equal_to_ten_and_five
  
  [False False False False]
  
  
  vector = numpy.array([5, 10, 15, 20])
  equal_to_ten_or_five = (vector == 10) | (vector == 5)
  print equal_to_ten_or_five
  
  [ True  True False False]
複製代碼

值類型轉換

vector = numpy.array(["1", "2", "3"])
  print vector.dtype
  print vector
  vector = vector.astype(float)
  print vector.dtype
  print vector
  
  |S1
  ['1' '2' '3']
  float64
  [ 1.  2.  3.]
複製代碼

聚合求解

vector = numpy.array([5, 10, 15, 20])
  vector.sum()
複製代碼

按行維度（axis=1）

matrix = numpy.array([
                 [5, 10, 15], 
                 [20, 25, 30],
                 [35, 40, 45]
              ])
 matrix.sum(axis=1)
 array([ 30,  75, 120])
複製代碼

按列求和（axis=0）

matrix = numpy.array([
                  [5, 10, 15], 
                  [20, 25, 30],
                  [35, 40, 45]
               ])
  matrix.sum(axis=0)  
  
  array([60, 75, 90])
複製代碼

矩陣操做np.arange生成0-N的整數

import numpy as np
  a = np.arange(15).reshape(3, 5)
  a

  array([[ 0,  1,  2,  3,  4],
         [ 5,  6,  7,  8,  9],
         [10, 11, 12, 13, 14]])
         
  a.ndim
  2
  
  a.dtype.name
  'int32'
  
  a.size
  15
複製代碼

矩陣初始化

np.zeros ((3,4)) 
  
  array([[ 0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.]])
 

  np.ones( (2,3,4), dtype=np.int32 )
  
  array([[[1, 1, 1, 1],
  [1, 1, 1, 1],
  [1, 1, 1, 1]],

 [[1, 1, 1, 1],
  [1, 1, 1, 1],
  [1, 1, 1, 1]]])
複製代碼

按照間隔生成數據

np.arange( 10, 30, 5 )
  array([10, 15, 20, 25])

  np.arange( 0, 2, 0.3 )
  array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8])
複製代碼

隨機生成數據

np.random.random((2,3))
  
  array([[ 0.40130659,  0.45452825,  0.79776512],
 [ 0.63220592,  0.74591134,  0.64130737]])
複製代碼

linspace在0到2pi之間取100個數

from numpy import pi
  np.linspace( 0, 2*pi, 100 )

  array([ 0.    ,  0.06346652,  0.12693304,  0.19039955,  0.25386607,
      0.31733259,  0.38079911,  0.44426563,  0.50773215,  0.57119866,
      0.63466518,  0.6981317 ,  0.76159822,  0.82506474,  0.88853126,
      0.95199777,  1.01546429,  1.07893081,  1.14239733,  1.20586385,
      1.26933037,  1.33279688,  1.3962634 ,  1.45972992,  1.52319644,
      1.58666296,  1.65012947,  1.71359599,  1.77706251,  1.84052903,
      1.90399555,  1.96746207,  2.03092858,  2.0943951 ,  2.15786162,
      2.22132814,  2.28479466,  2.34826118,  2.41172769,  2.47519421,
      2.53866073,  2.60212725,  2.66559377,  2.72906028,  2.7925268 ,
      2.85599332,  2.91945984,  2.98292636,  3.04639288,  3.10985939,
      3.17332591,  3.23679243,  3.30025895,  3.36372547,  3.42719199,
      3.4906585 ,  3.55412502,  3.61759154,  3.68105806,  3.74452458,
      3.8079911 ,  3.87145761,  3.93492413,  3.99839065,  4.06185717,
      4.12532369,  4.1887902 ,  4.25225672,  4.31572324,  4.37918976,
      4.44265628,  4.5061228 ,  4.56958931,  4.63305583,  4.69652235,
      4.75998887,  4.82345539,  4.88692191,  4.95038842,  5.01385494,
      5.07732146,  5.14078798,  5.2042545 ,  5.26772102,  5.33118753,
      5.39465405,  5.45812057,  5.52158709,  5.58505361,  5.64852012,
      5.71198664,  5.77545316,  5.83891968,  5.9023862 ,  5.96585272,
      6.02931923,  6.09278575,  6.15625227,  6.21971879,  6.28318531])
複製代碼

矩陣基本操做

#the product operator * operates elementwise in NumPy arrays
  a = np.array( [20,30,40,50] )
  b = np.arange( 4 )
  print (a)
  print (b)
  #b
  c = a-b
  print (c)
  b**2
  print (b**2)
  print (a<35)
  
  [20 30 40 50]
  [0 1 2 3]
  [20 29 38 47]
  [ True  True False False]
複製代碼

矩陣相乘

#The matrix product can be performed using the dot function or method
  A = np.array([[1,1],
                 [0,1]] )
  B = np.array([[2,0],
                 [3,4]])
  print (A)
  print (B)
  print (A*B)
  
  print (A.dot(B))
  print (np.dot(A, B) )
  
  [[1 1]
   [0 1]]
   
  [[2 0]
   [3 4]]
   
  [[2 0]
   [0 4]]
   
  [[5 4]
   [3 4]]
   
  [[5 4]
   [3 4]]
複製代碼

矩陣操做floor向下取整

import numpy as np
  B = np.arange(3)
  print (B)
  #print np.exp(B)
  print (np.sqrt(B))
  
  [0 1 2]
  [0.         1.         1.41421356]
  
  #Return the floor of the input
  a = np.floor(10*np.random.random((3,4)))
  #print a
  
  #Return the floor of the input
  a = np.floor(10*np.random.random((3,4)))
  print (a)
  
  print(a.reshape(2,-1))
  
  [[0. 4. 2. 2.]
   [8. 1. 5. 7.]
   [0. 9. 7. 4.]]
   
  [[0. 4. 2. 2. 8. 1.]
   [5. 7. 0. 9. 7. 4.]]
複製代碼

hstack矩陣拼接

a = np.floor(10*np.random.random((2,2)))
  b = np.floor(10*np.random.random((2,2)))
  print a
  print '---'
  print b
  print '---'
  print np.hstack((a,b))
  
  [[ 5.  6.]
   [ 1.  5.]]
  ---
  [[ 8.  6.]
   [ 9.  0.]]
  ---
  [[ 5.  6.  8.  6.]
   [ 1.  5.  9.  0.]]

  a = np.floor(10*np.random.random((2,2)))
  b = np.floor(10*np.random.random((2,2)))
  print (a)
  print ('---')
  print (b)
  print ('---')
  #print np.hstack((a,b))
  np.vstack((a,b))
  
  [[7. 7.]
   [2. 6.]]
  ---
  [[0. 6.]
   [0. 3.]]
  ---
 array([[1., 0.],
 [3., 6.],
 [4., 2.],
 [8., 7.]])

  a = np.floor(10*np.random.random((2,12)))
  print (a)
  print (np.hsplit(a,3))
  
  [[6. 5. 2. 4. 2. 4. 9. 4. 4. 6. 8. 9.]
   [8. 4. 0. 2. 6. 5. 2. 5. 0. 4. 1. 6.]]
  [array([[6., 5., 2., 4.],
         [8., 4., 0., 2.]]), array([[2., 4., 9., 4.],
         [6., 5., 2., 5.]]), array([[4., 6., 8., 9.],
         [0., 4., 1., 6.]])]
複製代碼

任意選擇切分位置

print ( np.hsplit(a,(3,4)))   # Split a after the third and the fourth column
  
  [[2. 8. 4.    7.    6. 6. 5. 8. 8. 3. 0. 1.]
   [3. 5. 9.    4.    5. 8. 7. 6. 2. 3. 8. 4.]]
  
  [array([[2., 8., 4.],
  [3., 5., 9.]]), array([[7.],
  [4.]]), array([[6., 6., 5., 8., 8., 3., 0., 1.],
  [5., 8., 7., 6., 2., 3., 8., 4.]])]
複製代碼

變量賦值
變量視圖

copy實現變量之間沒有關係

d = a.copy() 
  d is a
  d[0,0] = 9999
  print d 
  print a

  [[9999    1    2    3]
   [1234    5    6    7]
   [   8    9   10   11]]
  [[   0    1    2    3]
   [1234    5    6    7]
   [   8    9   10   11]]
複製代碼

尋找列最大值索引

行列按照倍數擴展（行3倍列5倍）

a = np.arange(0, 40, 10)
  b = np.tile(a, (3, 5)) 
  print b
  [[ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]
   [ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]
   [ 0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30  0 10 20 30]]
複製代碼

按照元素大小排序並給出索引值

a = np.array([4, 3, 1, 2])
  j = np.argsort(a)
  print j
  print a[j]
  
  [2 3 1 0]
  [1 2 3 4]
複製代碼

對數組按照元素大小排序

a = np.array([[4, 3, 5], [1, 2, 1]])
  #print a
  b = np.sort(a, axis=1)
  print (b)
  
  [[3 4 5]
  [1 1 2]]
複製代碼

2 Pandas詳細使用（底層基於Numpy）

2.1 Pandas基本操做

Pandas核心結構（DataFrame）
Pandas 字符型表示爲Object
Pandas數據基本類型展現

import pandas
    food_info = pandas.read_csv("food_info.csv")
    print(type(food_info))
    <class 'pandas.core.frame.DataFrame'>
    col_names = food_info.columns.tolist()
    
    ['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)', 'Lipid_Tot_(g)', 'Ash_(g)',
    'Carbohydrt_(g)', 'Fiber_TD_(g)', 'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)',
    'Magnesium_(mg)', 'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)',
    'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)', 'Thiamin_(mg)',
    'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)', 'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE',
    'Vit_E_(mg)', 'Vit_D_mcg', 'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)',
    'FA_Poly_(g)', 'Cholestrl_(mg)']    

    print food_info.dtypes
    
    NDB_No               int64
    Shrt_Desc           object
    Water_(g)          float64
    Energ_Kcal           int64
    Protein_(g)        float64
    Lipid_Tot_(g)      float64
    Ash_(g)            float64
    Carbohydrt_(g)     float64
    Fiber_TD_(g)       float64
    Sugar_Tot_(g)      float64
    Calcium_(mg)       float64
    Iron_(mg)          float64
    Magnesium_(mg)     float64
    Phosphorus_(mg)    float64
    Potassium_(mg)     float64
    Sodium_(mg)        float64
    Zinc_(mg)          float64
    Copper_(mg)        float64
    Manganese_(mg)     float64
    Selenium_(mcg)     float64
    Vit_C_(mg)         float64
    Thiamin_(mg)       float64
    Riboflavin_(mg)    float64
    Niacin_(mg)        float64
    Vit_B6_(mg)        float64
    Vit_B12_(mcg)      float64
    Vit_A_IU           float64
    Vit_A_RAE          float64
    Vit_E_(mg)         float64
    Vit_D_mcg          float64
    Vit_D_IU           float64
    Vit_K_(mcg)        float64
    FA_Sat_(g)         float64
    FA_Mono_(g)        float64
    FA_Poly_(g)        float64
    Cholestrl_(mg)     float64
    dtype: object
複製代碼

Pandas基本操做

#能夠指定數量
  #first_rows = food_info.head()
  #print(food_info.head(3))
複製代碼

#print food_info.columns
複製代碼

#print food_info.shape
    （8618,36）
複製代碼

取數據操做

#pandas uses zero-indexing
  #Series object representing the row at index 0.
  #print food_info.loc[0]
  
  # Series object representing the seventh row.
  #food_info.loc[6]
  
  # Will throw an error: "KeyError: 'the label [8620] is not in the [index]'"
  #food_info.loc[8620]
  #The object dtype is equivalent to a string in Python
複製代碼

數據切片

# Returns a DataFrame containing the rows at indexes 3, 4, 5, and 6.
  #food_info.loc[3:6]
  
  # Returns a DataFrame containing the rows at indexes 2, 5, and 10. Either of the following approaches will work.
  # Method 1
  #two_five_ten = [2,5,10] 
  #food_info.loc[two_five_ten]
  
  # Method 2
  #food_info.loc[[2,5,10]]
複製代碼

經過列名取出數據

# Series object representing the "NDB_No" column.
  #ndb_col = food_info["NDB_No"]
  #print ndb_col
  # Alternatively, you can access a column by passing in a string variable.
  #col_name = "NDB_No"
  #ndb_col = food_info[col_name]
複製代碼

取出兩個列的值

#columns = ["Zinc_(mg)", "Copper_(mg)"]
  #zinc_copper = food_info[columns]
  #print zinc_copper
  #print zinc_copper
  # Skipping the assignment.
  #zinc_copper = food_info[["Zinc_(mg)", "Copper_(mg)"]]
複製代碼

endswith 定位取值

#print(food_info.columns)
  #print(food_info.head(2))
  col_names = food_info.columns.tolist()
  #print col_names
  gram_columns = []
  
  for c in col_names:
      if c.endswith("(g)"):
          gram_columns.append(c)
  gram_df = food_info[gram_columns]
  print(gram_df.head(3))
複製代碼

2.2 Series類型上場

Series 是一個帶有名稱和索引的一維數組，既然是數組，確定要說到的就是數組中的元素類型，在 Series 中包含的數據類型能夠是整數、浮點、字符串、Python對象等。

# 存儲了 4 個年齡：18/30/25/40
    user_age = pd.Series(data=[18, 30, 25, 40])
    user_age

    0    18
    1    30
    2    25
    3    40
    dtype: int64
複製代碼

指定索引

user_age.index = ["Tom", "Bob", "Mary", "James"]
  user_age
  
  Tom      18
  Bob      30
  Mary     25
  James    40
  dtype: int64
複製代碼

爲 index 起個名字

user_age.index.name = "name"
  user_age
  
  name
  Tom      18
  Bob      30
  Mary     25
  James    40
  dtype: int64
複製代碼

給 Series 起個名字

user_age.name="user_age_info"
  user_age

  name
  Tom      18
  Bob      30
  Mary     25
  James    40
  Name: user_age_info, dtype: int64
複製代碼

一個 Series 包括了 data、index 以及 name。

# 構建索引
  name = pd.Index(["Tom", "Bob", "Mary", "James"], name="name")
  # 構建 Series
  user_age = pd.Series(data=[18, 30, 25, 40], index=name, name="user_age_info")
  user_age

  name
  Tom      18
  Bob      30
  Mary     25
  James    40
  Name: user_age_info, dtype: int64
  
  # 指定類型爲浮點型
  user_age = pd.Series(data=[18, 30, 25, 40], index=name, name="user_age_info", dtype=float)
  user_age
  
  name
  Tom      18.0
  Bob      30.0
  Mary     25.0
  James    40.0
  Name: user_age_info, dtype: float64
複製代碼

Series 包含了 dict 的特色，也就意味着可使用與 dict 相似的一些操做。咱們能夠將 index 中的元素當作是 dict 中的 key。

# 獲取 Tom 的年齡
  user_age["Tom"]
  
  18.0
  
  user_age.get("Tom")
  18.0


  # 指定索引，獲取第一個元素
  user_age[0]
  18.0
  
  # 獲取前三個元素
  user_age[:3]
  
  name
  Tom     18.0
  Bob     30.0
  Mary    25.0
  Name: user_age_info, dtype: float64
  
  # 獲取年齡大於30的元素
  user_age[user_age > 30]
  name
  James    40.0
  Name: user_age_info, dtype: float64

  # 獲取第4個和第二個元素
  user_age[[3, 1]]
  name
  James    40.0
  Bob      30.0
  Name: user_age_info, dtype: float64
複製代碼

2.3 DataFrame隆重登場

DataFrame 是一個帶有索引的二維數據結構，每列能夠有本身的名字，而且能夠有不一樣的數據類型。你能夠把它想象成一個 excel 表格或者數據庫中的一張表，DataFrame 是最經常使用的 Pandas 對象。

index = pd.Index(data=["Tom", "Bob", "Mary", "James"], name="name")
  
  data = {
      "age": [18, 30, 25, 40],
      "city": ["BeiJing", "ShangHai", "GuangZhou", "ShenZhen"]
  }
  
  user_info = pd.DataFrame(data=data, index=index)
  user_info
複製代碼

經過索引名來訪問某行，這種辦法須要藉助 loc 方法

user_info.loc["Tom"]
   
   age          18
   city    BeiJing
   Name: Tom, dtype: object
複製代碼

經過這行所在的位置來選擇這一行

user_info.iloc[0]
  age          18
  city    BeiJing
  Name: Tom, dtype: object
複製代碼

如何訪問多行
```
user_info.iloc[1:3]
複製代碼
```

訪問列

user_info.age
  name
  Tom      18
  Bob      30
  Mary     25
  James    40
  Name: age, dtype: int64
  
  user_info["age"]
  name
  Tom      18
  Bob      30
  Mary     25
  James    40
  Name: age, dtype: int64
  
  #能夠變換列的順序
  user_info[["city", "age"]]
複製代碼

2.4 DataFrame數據處理操做

info 函數(類型和缺失值統計)

user_info.info()
 
 Index: 4 entries, Tom to James
 Data columns (total 3 columns):
 age     4 non-null int64
 city    4 non-null object
 sex     4 non-null object
 dtypes: int64(1), object(2)
 memory usage: 128.0+ bytes
 
 user_info.head(2)
 
 user_info.shape
 (4, 3)

 user_info.T
複製代碼

經過 DataFrame 來獲取它包含的原有數據

user_info.values
  array([[18, 'BeiJing', 'male'],
     [30, 'ShangHai', 'male'],
     [25, 'GuangZhou', 'female'],
     [40, 'ShenZhen', 'male']], dtype=object)
複製代碼

統計
```
user_info.age.max()
複製代碼
```

累加求和

user_info.age.cumsum()
   name
   Tom       18
   Bob       48
   Mary      73
   James    113
   Name: age, dtype: int64
   
   user_info.sex.cumsum()
   
   name
   Tom                    male
   Bob                malemale
   Mary         malemalefemale
   James    malemalefemalemale
   Name: sex, dtype: object
複製代碼

統計指標彙總（總數、平均數、標準差、最小值、最大值、25%/50%/75% 分位數）
```
user_info.describe()
複製代碼
```

user_info.describe(include=["object"])
複製代碼

統計下某列中每一個值出現的次數

user_info.sex.value_counts()
  
  male      3
  female    1
  Name: sex, dtype: int64
複製代碼

獲取某列最大值或最小值對應的索引
```
user_info.age.idxmax()
  'James'
複製代碼
```

離散化（分桶）

pd.cut(user_info.age, 3)
  
 name
  Tom      (17.978, 25.333]
  Bob      (25.333, 32.667]
  Mary     (17.978, 25.333]
  James      (32.667, 40.0]
  Name: age, dtype: category
  Categories (3, interval[float64]): [(17.978, 25.333] &lt; (25.333, 32.667] &lt; (32.667, 40.0]]
複製代碼

自定義分桶

pd.cut(user_info.age, [1, 18, 30, 50])
  name
  Tom       (1, 18]
  Bob      (18, 30]
  Mary     (18, 30]
  James    (30, 50]
  Name: age, dtype: category
  Categories (3, interval[int64]): [(1, 18] &lt; (18, 30] &lt; (30, 50]]
複製代碼

離散化以後，給每一個區間起個名字

pd.cut(user_info.age, [1, 18, 30, 50], labels=["childhood", "youth", "middle"])
  
  name
  Tom      childhood
  Bob          youth
  Mary         youth
  James       middle
  Name: age, dtype: category
  Categories (3, object): [childhood &lt; youth &lt; middle]
複製代碼

按照索引進行正序排的
```
user_info.sort_index()
複製代碼
```

按照列進行倒序排，能夠設置參數 axis=1 和 ascending=False。
```
user_info.sort_index(axis=1, ascending=False)
複製代碼
```

按照實際值來排序

user_info.sort_values(by="age")
複製代碼

user_info.sort_values(by=["age", "city"])
複製代碼

獲取最大的n個值或最小值的n個值

user_info.age.nlargest(2)
  
  name
  James    40
  Bob      30
  Name: age, dtype: int64 
複製代碼

函數應用map

user_info.age.map(lambda x: "yes" if x >= 30 else "no")
  
  name
  Tom       no
  Bob      yes
  Mary      no
  James    yes
  Name: age, dtype: object
  
  city_map = {
      "BeiJing": "north",
      "ShangHai": "south",
      "GuangZhou": "south",
      "ShenZhen": "south"
  }
  # 傳入一個 map
  user_info.city.map(city_map)
  
  name
  Tom      north
  Bob      south
  Mary     south
  James    south
  Name: city, dtype: object
複製代碼

函數應用apply

# 對 Series 來講，apply 方法 與 map 方法區別不大。
  user_info.age.apply(lambda x: "yes" if x >= 30 else "no")
  name
  Tom       no
  Bob      yes
  Mary      no
  James    yes
  Name: age, dtype: object
  
  # 對 DataFrame 來講，apply 方法的做用對象是一行或一列數據（一個Series）
  user_info.apply(lambda x: x.max(), axis=0)
  
  age           40
  city    ShenZhen
  sex         male
  dtype: object
複製代碼

做用於 DataFrame 中的每一個元素applymap

user_info.applymap(lambda x: str(x).lower())
複製代碼

添加新列

user_info["height"] = ["178", "168", "178", "180cm"]
  user_info
複製代碼

類型轉換

默認狀況下，errors='raise'，這意味着強轉失敗後直接拋出異常，設置 errors='coerce'
  能夠在強轉失敗時將有問題的元素賦值爲 pd.NaT（對於datetime和timedelta）或
  np.nan（數字）。設置 errors='ignore' 能夠在強轉失敗時返回原有的數據。

  pd.to_numeric(user_info.height, errors="coerce")
  
  name
  Tom      178.0
  Bob      168.0
  Mary     178.0
  James      NaN
  Name: height, dtype: float64
  
  pd.to_numeric(user_info.height, errors="ignore")
  name
  Tom        178
  Bob        168
  Mary       178
  James    180cm
  Name: height, dtype: object
複製代碼

2.5 缺失值處理

待補充

2.6 Pandas案例實戰

2.6.1 案例實戰1

import pandas
    food_info = pandas.read_csv("C:\\ML\\MLData\\food_info.csv")
    col_names = food_info.columns.tolist()
    print(col_names)
    print(food_info.head(3))
複製代碼

針對某一列進行四則運算
    #print food_info["Iron_(mg)"]
    #div_1000 = food_info["Iron_(mg)"] / 1000
    #print div_1000
    # Adds 100 to each value in the column and returns a Series object.
    #add_100 = food_info["Iron_(mg)"] + 100
    
    # Subtracts 100 from each value in the column and returns a Series object.
    #sub_100 = food_info["Iron_(mg)"] - 100
    
    # Multiplies each value in the column by 2 and returns a Series object.
    #mult_2 = food_info["Iron_(mg)"]*2

    #It applies the arithmetic operator to the first value in both columns, the second value in both columns, and so on
    water_energy = food_info["Water_(g)"] * food_info["Energ_Kcal"]
    water_energy = food_info["Water_(g)"] * food_info["Energ_Kcal"]
    iron_grams = food_info["Iron_(mg)"] / 1000  
    food_info["Iron_(g)"] = iron_grams
    
    #追加新列
    max_calories = food_info["Energ_Kcal"].max()
    print(max_calories)
    # Divide the values in "Energ_Kcal" by the largest value.
    normalized_calories = food_info["Energ_Kcal"] / max_calories
    normalized_protein = food_info["Protein_(g)"] / food_info["Protein_(g)"].max()
    normalized_fat = food_info["Lipid_Tot_(g)"] / food_info["Lipid_Tot_(g)"].max()
    food_info["Normalized_Protein"] = normalized_protein
    food_info["Normalized_Fat"] = normalized_fat
    
    #排序
    #By default, pandas will sort the data by the column we specify in ascending order and return a new DataFrame
    # Sorts the DataFrame in-place, rather than returning a new DataFrame.
    #print food_info["Sodium_(mg)"]
    food_info.sort_values("Sodium_(mg)", inplace=True)
    #print (food_info["Sodium_(mg)"])
    #Sorts by descending order, rather than ascending.
    food_info.sort_values("Sodium_(mg)", inplace=True, ascending=False)
    print (food_info["Sodium_(mg)"])
複製代碼

2.6.2 泰坦尼克案例實戰2

import pandas as pd
    import numpy as np
    titanic_survival = pd.read_csv("C:\\ML\\MLData\\titanic_train.csv")
    
    #SibSp:老人和孩子
    #Parch：家人
    #Pclass：倉位級別
    #Cabin：船艙編號，NaN是缺失值（就是爲空的值）
    #Embarked 登船地點 S C Q 三個碼頭
    titanic_survival.head()
複製代碼

控制空值判斷及展現

#The Pandas library uses NaN, which stands for "not a number", to indicate a missing value.
#we can use the pandas.isnull() function which takes a pandas series and returns a series of True and False values
age = titanic_survival["Age"]
#print(age.loc[0:10])
age_is_null = pd.isnull(age)
#print (age_is_null)
age_null_true = age[age_is_null]
print (age_null_true)
age_null_count = len(age_null_true)
#print(age_null_count)

5     NaN
17    NaN
19    NaN
26    NaN
28    NaN
29    NaN
31    NaN
32    NaN
36    NaN
42    NaN
45    NaN
46    NaN
複製代碼

含有空值時將沒法計算

#The result of this is that mean_age would be nan. This is because any
   calculations we do with a null value also result in a null value
  mean_age = sum(titanic_survival["Age"]) / len(titanic_survival["Age"])
  print (mean_age)
  nan
  
  #過濾出非空值，可是略顯複雜
  #we have to filter out the missing values before we calculate the mean.
  good_ages = titanic_survival["Age"][age_is_null == False]
  #print good_ages
  correct_mean_age = sum(good_ages) / len(good_ages)
  print (correct_mean_age)
  29.69911764705882
  
  # missing data is so common that many pandas methods automatically filter for it
  correct_mean_age = titanic_survival["Age"].mean()
  print correct_mean_age
  29.6991176471
  
  #mean fare for each class
複製代碼

每一個船艙位的平均價格

passenger_classes = [1, 2, 3]
  fares_by_class = {}
  for this_class in passenger_classes:
      pclass_rows = titanic_survival[titanic_survival["Pclass"] == this_class]
      pclass_fares = pclass_rows["Fare"]
      fare_for_class = pclass_fares.mean()
      fares_by_class[this_class] = fare_for_class
  print (fares_by_class)
  
  {1: 84.15468749999992, 2: 20.66218315217391, 3: 13.675550101832997}
  
  
  # pandas不一樣列之間的關係,pivot_table高級用法
  passenger_survival = titanic_survival.pivot_table(index="Pclass", values="Survived", aggfunc=np.mean)
  print (passenger_survival)
  
          Survived
  Pclass          
  1       0.629630
  2       0.472826
  3       0.242363
  
  # 默認求均值
  passenger_age = titanic_survival.pivot_table(index="Pclass", values="Age")
  print(passenger_age)
  Pclass
  1    38.233441
  2    29.877630
  3    25.140620
  Name: Age, dtype: float64
  
  #多列之間關係
  port_stats = titanic_survival.pivot_table(index="Embarked", values=["Fare","Survived"], aggfunc=np.sum)
  print(port_stats)
  
                  Fare  Survived
  Embarked                      
  C         10072.2962        93
  Q          1022.2543        30
  S         17439.3988       217
複製代碼

丟掉缺失值 axis=1 表示行

#specifying axis=1 or axis='columns' will drop any columns that have null values
  drop_na_columns = titanic_survival.dropna(axis=1)
  new_titanic_survival = titanic_survival.dropna(axis=0,subset=["Age", "Sex"])
  #print new_titanic_survival   
複製代碼

經過索引和列名

row_index_83_age = titanic_survival.loc[83,"Age"]
  row_index_1000_pclass = titanic_survival.loc[766,"Pclass"]
  print (row_index_83_age)
  print (row_index_1000_pclass)
  28.0
  1
複製代碼

按值排序(索引不變)，並重設索引

#按值排序(索引不變)
  new_titanic_survival = titanic_survival.sort_values("Age",ascending=False)
  #print (new_titanic_survival[0:10])
  
  #索引起生變化
  itanic_reindexed = new_titanic_survival.reset_index(drop=True)
  print(itanic_reindexed.iloc[0:10])
複製代碼

返回第100行數據

# This function returns the hundredth item from a series
 def hundredth_row(column):
     # Extract the hundredth item
     hundredth_item = column.iloc[99]
     return hundredth_item
 
 # Return the hundredth item from each column
 hundredth_row = titanic_survival.apply(hundredth_row)
 print (hundredth_row)

 PassengerId                  100
 Survived                       0
 Pclass                         2
 Name           Kantor, Mr. Sinai
 Sex                         male
 Age                           34
 SibSp                          1
 Parch                          0
 Ticket                    244367
 Fare                          26
 Cabin                        NaN
 Embarked                       S
 dtype: object
複製代碼

自定義行數非空判斷

def not_null_count(column):
      column_null = pd.isnull(column)
      null = column[column_null]
      return len(null)
  
  column_null_count = titanic_survival.apply(not_null_count)
  print (column_null_count)

  PassengerId      0
  Survived         0
  Pclass           0
  Name             0
  Sex              0
  Age            177
  SibSp            0
  Parch            0
  Ticket           0
  Fare             0
  Cabin          687
  Embarked         2
  dtype: int64
複製代碼

定義級別axis=1表示行

#By passing in the axis=1 argument, we can use the DataFrame.apply() method to iterate over rows instead of columns.
  def which_class(row):
      pclass = row['Pclass']
      if pd.isnull(pclass):
          return "Unknown"
      elif pclass == 1:
          return "First Class"
      elif pclass == 2:
          return "Second Class"
      elif pclass == 3:
          return "Third Class"
  
  classes = titanic_survival.apply(which_class, axis=1)
  print (classes)

  0       Third Class
  1       First Class
  2       Third Class
  3       First Class
  4       Third Class
  5       Third Class
  6       First Class
  7       Third Class
複製代碼

自定義函數

def is_minor(row):
      if row["Age"] < 18:
          return True
      else:
          return False
  
  minors = titanic_survival.apply(is_minor, axis=1)
  #print minors
  
  def generate_age_label(row):
      age = row["Age"]
      if pd.isnull(age):
          return "unknown"
      elif age < 18:
          return "minor"
      else:
          return "adult"
  
      age_labels = titanic_survival.apply(generate_age_label, axis=1)
      print age_labels
      
      0        adult
      1        adult
      2        adult
      3        adult
      4        adult
      5      unknown
      6        adult
      7        minor
      8        adult
      9        minor
      10       minor
      11       adult
      12       adult
複製代碼

分類類別

titanic_survival['age_labels'] = age_labels
 age_group_survival = titanic_survival.pivot_table(index="age_labels", values="Survived")
 print age_group_survival
 
 age_labels
 adult      0.381032
 minor      0.539823
 unknown    0.293785
 Name: Survived, dtype: float64
複製代碼

2.6.3 Series 案例實戰3

#Pandas默認其中一行或一列就是Series
    import pandas as pds
    fandango = pd.read_csv('C:\\ML\\MLData\\fandango_score_comparison.csv')
    series_film = fandango['FILM']
    print(series_film[0:5])
    series_rt = fandango['RottenTomatoes']
    print (series_rt[0:5])
    
    0    Avengers: Age of Ultron (2015)
    1                 Cinderella (2015)
    2                    Ant-Man (2015)
    3            Do You Believe? (2015)
    4     Hot Tub Time Machine 2 (2015)
    Name: FILM, dtype: object
    0    74
    1    85
    2    80
    3    18
    4    14
    Name: RottenTomatoes, dtype: int64
複製代碼

設置索引列，根據索引取得對應的值

# Import the Series object from pandas
  from pandas import Series
  
  film_names = series_film.values
  #print (type(film_names))
  #print (film_names)
  rt_scores = series_rt.values
  #print (rt_scores)
  series_custom = Series(rt_scores , index=film_names)
  #根據索引取得對應的值
  series_custom[['Minions (2015)', 'Leviathan (2014)']] 
  
  Minions (2015)      54
  Leviathan (2014)    99
  dtype: int64
複製代碼

範圍查找

series_custom = Series(rt_scores , index=film_names)
 #series_custom[['Minions (2015)', 'Leviathan (2014)']]
 fiveten = series_custom[5:10]
 print(fiveten)
 
 The Water Diviner (2015)        63
 Irrational Man (2015)           42
 Top Five (2014)                 86
 Shaun the Sheep Movie (2015)    99
 Love & Mercy (2015)             89
 dtype: int64
複製代碼

索引排序，並重設

original_index = series_custom.index.tolist()
  #print original_index
  sorted_index = sorted(original_index)
  sorted_by_index = series_custom.reindex(sorted_index)
  #print sorted_by_index
  
  '71 (2015)                                         97
  5 Flights Up (2015)                                52
  A Little Chaos (2015)                              40
  A Most Violent Year (2014)                         90
  About Elly (2015)                                  97
  Aloha (2015)                                       19
  American Sniper (2015)                             72
複製代碼

Series 值排序

sc2 = series_custom.sort_index()
  sc3 = series_custom.sort_values()
  #print(sc2[0:10])
  print(sc3[0:10])
  
  Paul Blart: Mall Cop 2 (2015)     5
  Hitman: Agent 47 (2015)           7
  Hot Pursuit (2015)                8
  Fantastic Four (2015)             9
  Taken 3 (2015)                    9
  The Boy Next Door (2015)         10
  The Loft (2015)                  11
  Unfinished Business (2015)       11
  Mortdecai (2015)                 12
  Seventh Son (2015)               12
複製代碼

Series對應索引相加

#The values in a Series object are treated as an ndarray, the core data type in NumPy
  import numpy as np
  # Add each value with each other
  print np.add(series_custom, series_custom)
  # Apply sine function to each value
  np.sin(series_custom)
  # Return the highest value (will return a single value not a Series)
  np.max(series_custom)
複製代碼

Series對應Lambda表達式（求標準差）

#The apply() method in Pandas allows us to specify Python logic
  #The apply() method requires you to pass in a vectorized operation 
  #that can be applied over each Series object.
  import numpy as np
  
  # returns the data types as a Series
  types = fandango_films.dtypes
  #print types
  # filter data types to just floats, index attributes returns just column names
  float_columns = types[types.values == 'float64'].index
  # use bracket notation to filter columns to just float columns
  float_df = fandango_films[float_columns]
  #print float_df
  # `x` is a Series object representing a column
  deviations = float_df.apply(lambda x: np.std(x))
  
  print(deviations)
  
  Metacritic_User               1.505529
  IMDB                          0.955447
  Fandango_Stars                0.538532
  Fandango_Ratingvalue          0.501106
  RT_norm                       1.503265
  RT_user_norm                  0.997787
  Metacritic_norm               0.972522
  Metacritic_user_nom           0.752765
  IMDB_norm                     0.477723
複製代碼

對應兩列經過Lambda表達式求標準差

rt_mt_user = float_df[['RT_user_norm', 'Metacritic_user_nom']]
  rt_mt_user.apply(lambda x: np.std(x), axis=1)
  
  FILM
  Avengers: Age of Ultron (2015)                    0.375
  Cinderella (2015)                                 0.125
  Ant-Man (2015)                                    0.225
  Do You Believe? (2015)                            0.925
  Hot Tub Time Machine 2 (2015)                     0.150
  The Water Diviner (2015)                          0.150
  Irrational Man (2015)                             0.575
  Top Five (2014)                                   0.100
  Shaun the Sheep Movie (2015)                      0.150
  Love & Mercy (2015)                               0.050
  Far From The Madding Crowd (2015)                 0.050
  Black Sea (2015)                                  0.150
複製代碼

3 matplotlib使用實踐

折線圖

import pandas as pd
  unrate = pd.read_csv("C:\\ML\\MLData\\unrate.csv")
  unrate['DATE'] = pd.to_datetime(unrate['DATE'])
  print(unrate.head(12))
  
        DATE      VALUE
  0  1948-01-01    3.4
  1  1948-02-01    3.8
  2  1948-03-01    4.0
  3  1948-04-01    3.9
  4  1948-05-01    3.5
  5  1948-06-01    3.6
  6  1948-07-01    3.6
  7  1948-08-01    3.9
  8  1948-09-01    3.8
  9  1948-10-01    3.7
  10 1948-11-01    3.8
  11 1948-12-01    4.0

  import matplotlib.pyplot as plt
  plt.plot()
  plt.show()
複製代碼

first_twelve = unrate[0:12]
    plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
    plt.show()
複製代碼

plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
    plt.xticks(rotation=45)
    #print help(plt.xticks)
    plt.show()
複製代碼

#xlabel(): accepts a string value, which gets set as the x-axis label.
#ylabel(): accepts a string value, which is set as the y-axis label.
#title(): accepts a string value, which is set as the plot title.

plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
plt.xticks(rotation=90)
plt.xlabel('Month')
plt.ylabel('Unemployment Rate')
plt.title('Monthly Unemployment Trends, 1948')
plt.show()
複製代碼

多條折線圖展現

fig = plt.figure(figsize=(10,6))
  colors = ['red', 'blue', 'green', 'orange', 'black']
  for i in range(5):
      start_index = i*12
      end_index = (i+1)*12
      subset = unrate[start_index:end_index]
      label = str(1948 + i)
      plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)
  plt.legend(loc='upper left')
  plt.xlabel('Month, Integer')
  plt.ylabel('Unemployment Rate, Percent')
  plt.title('Monthly Unemployment Trends, 1948-1952')
  
  plt.show()
複製代碼

柱狀圖豎型展現

import pandas as pd
  reviews = pd.read_csv('C:\\ML\\MLData\\fandango_scores.csv')
  cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
  norm_reviews = reviews[cols]
  print(type(reviews))
  #打印出第一行
  print(norm_reviews[:1])
  
  <class 'pandas.core.frame.DataFrame'>
複製代碼

import matplotlib.pyplot as plt
    from numpy import arange
    
    #取出第一行指定列num_cols的數據
    num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    bar_heights = norm_reviews.loc[0, num_cols].values
    print(bar_heights)
    
    [4.3 3.55 3.9 4.5 5.0]
    
    bar_heights = norm_reviews.loc[0, num_cols].values
    #橫軸位置
    bar_positions = arange(5) + 0.75
    #橫軸標識的位置（1到6之間）
    tick_positions = range(1,6)
    
    fig, ax = plt.subplots()
    #0.5標識柱狀圖寬度
    ax.bar(bar_positions, bar_heights, 0.5)
    ax.set_xticks(tick_positions)
    ax.set_xticklabels(num_cols, rotation=90)
    
    ax.set_xlabel('Rating Source')
    ax.set_ylabel('Average Rating')
    ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
    plt.show() 
複製代碼

柱狀圖橫向表示

import matplotlib.pyplot as plt
  from numpy import arange
  num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
  
  bar_widths = norm_reviews.loc[0, num_cols].values
  bar_positions = arange(5) + 0.75
   #橫軸標識名的位置（1到6之間）
  tick_positions = range(1,6)
  fig, ax = plt.subplots()
  ax.barh(bar_positions, bar_widths, 0.6)
  
  ax.set_yticks(tick_positions)
  ax.set_yticklabels(num_cols)
  ax.set_ylabel('Rating Source')
  ax.set_xlabel('Average Rating')
  ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
  plt.show()
複製代碼

散點圖

#Switching Axes
  fig = plt.figure(figsize=(5,10))
  ax1 = fig.add_subplot(2,1,1)
  ax2 = fig.add_subplot(2,1,2)
  ax1.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])
  ax1.set_xlabel('Fandango')
  ax1.set_ylabel('Rotten Tomatoes')
  ax2.scatter(norm_reviews['RT_user_norm'], norm_reviews['Fandango_Ratingvalue'])
  ax2.set_xlabel('Rotten Tomatoes')
  ax2.set_ylabel('Fandango')
  plt.show()
複製代碼

Hist的bins區間統計

import pandas as pd
import matplotlib.pyplot as plt
reviews = pd.read_csv('C:\\ML\\MLData\\fandango_scores.csv')
cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
norm_reviews = reviews[cols]
print(norm_reviews[:5])


#按照列進行分組聚合
fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts()
fandango_distribution = fandango_distribution.sort_index()

#按照列進行分組聚合
imdb_distribution = norm_reviews['IMDB_norm'].value_counts()
imdb_distribution = imdb_distribution.sort_index()

print(fandango_distribution)
2.7     2
2.8     2
2.9     5
3.0     4
3.1     3
3.2     5
3.3     4
3.4     9
3.5     9
3.6     8
3.7     9
3.8     5
3.9    12
4.0     7
4.1    16
4.2    12
4.3    11
4.4     7
4.5     9
4.6     4
4.8     3
Name: Fandango_Ratingvalue, dtype: int64


print(imdb_distribution)
2.00     1
2.10     1
2.15     1
2.20     1
2.30     2
2.45     2
2.50     1
2.55     1
2.60     2
2.70     4
2.75     5
2.80     2
2.85     1
2.90     1
2.95     3
3.00     2
3.05     4
3.10     1
3.15     9
3.20     6
3.25     4
3.30     9
3.35     7
3.40     1
3.45     7
3.50     4
3.55     7
3.60    10
3.65     5
3.70     8
3.75     6
3.80     3
3.85     4
3.90     9
3.95     2
4.00     1
4.05     1
4.10     4
4.15     1
4.20     2
4.30     1
Name: IMDB_norm, dtype: int64


fig, ax = plt.subplots()
#ax.hist(norm_reviews['Fandango_Ratingvalue'])
#ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)

指定區間爲20個，範圍爲4到5
ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)
plt.show()
複製代碼

4分圖盒圖

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
  fig, ax = plt.subplots()
  #指定統計列取出對應值
  ax.boxplot(norm_reviews[num_cols].values)
  ax.set_xticklabels(num_cols, rotation=90)
  ax.set_ylim(0,5)
  plt.show()
複製代碼

4 Seaborn專業可視化庫（基於matplot）

風格設置

import seaborn as sns
  import numpy as np
  import matplotlib as mpl
  import matplotlib.pyplot as plt
  %matplotlib inline
  
  sns.set_style("whitegrid")
  data = np.random.normal(size=(20, 6)) + np.arange(6) / 2
  sns.boxplot(data=data)
複製代碼

sns.set_style("dark")
     sinplot()
     
    sns.set_style("white")
    sinplot() 
    
    sns.set_style("whitegrid")
    sns.boxplot(data=data, palette="deep")
    sns.despine(left=True)
複製代碼

調色板設置

import numpy as np
  import seaborn as sns
  import matplotlib.pyplot as plt
  %matplotlib inline
  sns.set(rc={"figure.figsize": (6, 6)})
  
  current_palette = sns.color_palette()
  sns.palplot(current_palette)
複製代碼

6個默認的顏色循環主題： deep, muted, pastel, bright, dark, colorblind
    
    sns.palplot(sns.color_palette("hls", 8))
複製代碼

data = np.random.normal(size=(20, 8)) + np.arange(8) / 2
    sns.boxplot(data=data,palette=sns.color_palette("hls", 8))

    data = np.random.normal(size=(20, 8)) + np.arange(8) / 2
    #print(data)
    sns.boxplot(data=data,palette=sns.color_palette("hls", 8))
複製代碼

區間直方圖繪製（kde是否指定核密度估計）

x = np.random.gamma(6, size=200)
  sns.distplot(x, kde=False, fit=stats.gamma)
複製代碼

線性迴歸1

%matplotlib inline
  import numpy as np
  import pandas as pd
  import matplotlib as mpl
  import matplotlib.pyplot as plt
  
  import seaborn as sns
  sns.set(color_codes=True)
  np.random.seed(sum(map(ord, "regression")))
  tips = sns.load_dataset("tips")
  tips.head()
複製代碼

sns.regplot(x="total_bill", y="tip", data=tips)
複製代碼

線性迴歸2

sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips);
複製代碼

多分類問題

%matplotlib inline
  import numpy as np
  import pandas as pd
  import matplotlib as mpl
  import matplotlib.pyplot as plt
  import seaborn as sns
  sns.set(style="whitegrid", color_codes=True)
  
  np.random.seed(sum(map(ord, "categorical")))
  titanic = sns.load_dataset("titanic")
  tips = sns.load_dataset("tips")
  iris = sns.load_dataset("iris")
  sns.stripplot(x="day", y="total_bill", data=tips);
複製代碼

sns.stripplot(x="day", y="total_bill", data=tips, jitter=True)
複製代碼

樹樁展現均勻展現

sns.swarmplot(x="day", y="total_bill", data=tips)
複製代碼

樹樁展現均勻並分類展現

sns.swarmplot(x="day", y="total_bill", hue="sex",data=tips)
複製代碼

盒圖

IQR即統計學概念四分位距，第一/四分位與第三/四分位之間的距離
 N = 1.5IQR 若是一個值>Q3+N或&emsp;<&emsp;Ｑ1-N,則爲離羣點

 #橫槓最小值和最大值
 sns.boxplot(x="day", y="total_bill", hue="time", data=tips);  
複製代碼

小提琴圖（越胖包含的數據越多）

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True);
複製代碼

葫蘆圖

sns.violinplot(x="day", y="total_bill", data=tips, inner=None)
 sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5)
複製代碼

柱狀分類統計圖

sns.barplot(x="sex", y="survived", hue="class", data=titanic);
複製代碼

點圖能夠更好的描述變化差別

sns.pointplot(x="sex", y="survived", hue="class", data=titanic);
複製代碼

sns.pointplot(x="class", y="survived", hue="sex", data=titanic,
          palette={"male": "g", "female": "m"},
          markers=["^", "o"], linestyles=["-", "--"]);
複製代碼

多層面板分類圖

sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips)
複製代碼

sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar")
複製代碼

sns.factorplot(x="day", y="total_bill", hue="smoker",
              col="time", data=tips, kind="swarm")
複製代碼

sns.factorplot(x="time", y="total_bill", hue="smoker",
           col="day", data=tips, kind="box", size=4, aspect=.5)
複製代碼

FacetGrid 多參數網格面板

%matplotlib inline
  import numpy as np
  import pandas as pd
  import seaborn as sns
  from scipy import stats
  import matplotlib as mpl
  import matplotlib.pyplot as plt
  
  sns.set(style="ticks")
  np.random.seed(sum(map(ord, "axis_grids")))
  tips = sns.load_dataset("tips")
  tips.head()
複製代碼

g = sns.FacetGrid(tips, col="time")
    g.map(plt.hist, "tip");
複製代碼

g = sns.FacetGrid(tips, col="sex", hue="smoker")
    g.map(plt.scatter, "total_bill", "tip", alpha=.7)
    g.add_legend();
複製代碼

g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True)
    g.map(sns.regplot, "size", "total_bill", color=".1", fit_reg=False, x_jitter=.1);
複製代碼

熱力圖

%matplotlib inline
  import matplotlib.pyplot as plt
  import numpy as np; 
  np.random.seed(0)
  import seaborn as sns;
  sns.set()
  uniform_data = np.random.rand(3, 3)
  print (uniform_data)
  heatmap = sns.heatmap(uniform_data)

  [[ 0.0187898   0.6176355   0.61209572]
   [ 0.616934    0.94374808  0.6818203 ]
   [ 0.3595079   0.43703195  0.6976312 ]]
複製代碼

ax = sns.heatmap(flights, linewidths=.5)
複製代碼

5 總結

方便複習，整成筆記，內容粗略，勿怪，待完善。

相關標籤/搜索

每日一句

每一个你不满意的现在，都有一个你没有努力的曾经。