Python基础算法库及可视化库使用实践-大数据ML样本集案例实战-白红宇

1 Numpy详细使用

读取txt文件

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']]复制代码

创建一维和二维的Array数组

#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:    #一维的Array数组[]  vector = numpy.array([5, 10, 15, 20])    #二维的Array数组[[],[],[]]  matrix = numpy.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])  print vector  print matrix复制代码

shape用法

#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)复制代码

dtype用法（numpy要求numpy.array内部元素结构相同）

numbers = numpy.array([1, 2, 3, 4])  numbers.dtype    dtype('int32')    #改变其中一个值时，其他值都会改变  numbers = numpy.array([1, 2, 3, '4'])  print(numbers)  numbers.dtype     ['1' '2' '3' '4']   dtype('

索引定位

[[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复制代码

索引切片

vector = numpy.array([5, 10, 15, 20])  print(vector[0:3])    [ 5 10 15]复制代码

取某一列（：表示所有行）

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]]复制代码

对Array操作表示对内部所有元素进行操作

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)复制代码

布尔值当索引（[False True False False]）

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]  #矩阵表示索引  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]]复制代码

对数组进行与运算

#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]复制代码

值类型转换

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.]复制代码

聚合求解

vector = numpy.array([5, 10, 15, 20])  vector.sum()复制代码

按行维度（axis=1）

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

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])复制代码

矩阵操作np.arange生成0-N的整数

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复制代码

矩阵初始化

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]]])复制代码

按照间隔生成数据

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])复制代码

随机生成数据

np.random.random((2,3))    array([[ 0.40130659,  0.45452825,  0.79776512], [ 0.63220592,  0.74591134,  0.64130737]])复制代码

linspace在0到2pi之间取100个数

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])复制代码

矩阵基本操作

#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]复制代码

矩阵相乘

#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]]复制代码

矩阵操作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.]]复制代码

hstack矩阵拼接

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.]])]复制代码

任意选择切分位置

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.]])]复制代码

变量赋值

变量视图

copy实现变量之间没有关系

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]]复制代码

寻找列最大值索引

行列按照倍数扩展（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]]复制代码

按照元素大小排序并给出索引值

a = np.array([4, 3, 1, 2])  j = np.argsort(a)  print j  print a[j]    [2 3 1 0]  [1 2 3 4]复制代码

对数组按照元素大小排序

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]]复制代码

2 Pandas详细使用（底层基于Numpy）

2.1 Pandas基本操作

Pandas核心结构（DataFrame）

Pandas 字符型表示为Object

Pandas数据基本类型展示

import pandas    food_info = pandas.read_csv("food_info.csv")    print(type(food_info))    
    
         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复制代码

Pandas基本操作

#可以指定数量  #first_rows = food_info.head()  #print(food_info.head(3))复制代码

#print food_info.columns复制代码

#print food_info.shape    （8618,36）复制代码

取数据操作

#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复制代码

数据切片

# 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]]复制代码

通过列名取出数据

# 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]复制代码

取出两个列的值

#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)"]]复制代码

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))复制代码

2.2 Series类型上场

Series 是一个带有名称和索引的一维数组，既然是数组，肯定要说到的就是数组中的元素类型，在 Series 中包含的数据类型可以是整数、浮点、字符串、Python对象等。

# 存储了 4 个年龄：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复制代码

指定索引

user_age.index = ["Tom", "Bob", "Mary", "James"]  user_age    Tom      18  Bob      30  Mary     25  James    40  dtype: int64复制代码

为 index 起个名字

user_age.index.name = "name"  user_age    name  Tom      18  Bob      30  Mary     25  James    40  dtype: int64复制代码

给 Series 起个名字

user_age.name="user_age_info"  user_age  name  Tom      18  Bob      30  Mary     25  James    40  Name: user_age_info, dtype: int64复制代码

一个 Series 包括了 data、index 以及 name。

# 构建索引  name = pd.Index(["Tom", "Bob", "Mary", "James"], name="name")  # 构建 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    # 指定类型为浮点型  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复制代码

Series 包含了 dict 的特点，也就意味着可以使用与 dict 类似的一些操作。我们可以将 index 中的元素看成是 dict 中的 key。

# 获取 Tom 的年龄  user_age["Tom"]    18.0    user_age.get("Tom")  18.0  # 指定索引，获取第一个元素  user_age[0]  18.0    # 获取前三个元素  user_age[:3]    name  Tom     18.0  Bob     30.0  Mary    25.0  Name: user_age_info, dtype: float64    # 获取年龄大于30的元素  user_age[user_age > 30]  name  James    40.0  Name: user_age_info, dtype: float64  # 获取第4个和第二个元素  user_age[[3, 1]]  name  James    40.0  Bob      30.0  Name: user_age_info, dtype: float64复制代码

2.3 DataFrame隆重登场

DataFrame 是一个带有索引的二维数据结构，每列可以有自己的名字，并且可以有不同的数据类型。你可以把它想象成一个 excel 表格或者数据库中的一张表，DataFrame 是最常用的 Pandas 对象。

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复制代码

通过索引名来访问某行，这种办法需要借助 loc 方法

user_info.loc["Tom"]      age          18   city    BeiJing   Name: Tom, dtype: object复制代码

通过这行所在的位置来选择这一行

user_info.iloc[0]  age          18  city    BeiJing  Name: Tom, dtype: object复制代码

如何访问多行
```
user_info.iloc[1:3]复制代码
```

访问列

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    #可以变换列的顺序  user_info[["city", "age"]]复制代码

2.4 DataFrame数据处理操作

info 函数(类型和缺失值统计)

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复制代码

通过 DataFrame 来获取它包含的原有数据

user_info.values  array([[18, 'BeiJing', 'male'],     [30, 'ShangHai', 'male'],     [25, 'GuangZhou', 'female'],     [40, 'ShenZhen', 'male']], dtype=object)复制代码

统计
```
user_info.age.max()复制代码
```

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复制代码

统计指标汇总（总数、平均数、标准差、最小值、最大值、25%/50%/75% 分位数）
```
user_info.describe()复制代码
```

user_info.describe(include=["object"])复制代码

统计下某列中每个值出现的次数

user_info.sex.value_counts()    male      3  female    1  Name: sex, dtype: int64复制代码

获取某列最大值或最小值对应的索引
```
user_info.age.idxmax()  'James'复制代码
```

离散化（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] < (25.333, 32.667] < (32.667, 40.0]]复制代码

自定义分桶

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] < (18, 30] < (30, 50]]复制代码

离散化之后，给每个区间起个名字

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 < youth < middle]复制代码

按照索引进行正序排的
```
user_info.sort_index()复制代码
```

按照列进行倒序排，可以设置参数 axis=1 和 ascending=False。
```
user_info.sort_index(axis=1, ascending=False)复制代码
```

按照实际值来排序

user_info.sort_values(by="age")复制代码

user_info.sort_values(by=["age", "city"])复制代码

获取最大的n个值或最小值的n个值

user_info.age.nlargest(2)    name  James    40  Bob      30  Name: age, dtype: int64 复制代码

函数应用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"  }  # 传入一个 map  user_info.city.map(city_map)    name  Tom      north  Bob      south  Mary     south  James    south  Name: city, dtype: object复制代码

函数应用apply

# 对 Series 来说，apply 方法 与 map 方法区别不大。  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    # 对 DataFrame 来说，apply 方法的作用对象是一行或一列数据（一个Series）  user_info.apply(lambda x: x.max(), axis=0)    age           40  city    ShenZhen  sex         male  dtype: object复制代码

作用于 DataFrame 中的每个元素applymap

user_info.applymap(lambda x: str(x).lower())复制代码

添加新列

user_info["height"] = ["178", "168", "178", "180cm"]  user_info复制代码

类型转换

默认情况下，errors='raise'，这意味着强转失败后直接抛出异常，设置 errors='coerce'  可以在强转失败时将有问题的元素赋值为 pd.NaT（对于datetime和timedelta）或  np.nan（数字）。设置 errors='ignore' 可以在强转失败时返回原有的数据。  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复制代码

2.5 缺失值处理

待补充

2.6 Pandas案例实战

2.6.1 案例实战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))复制代码

针对某一列进行四则运算    #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)"])复制代码

2.6.2 泰坦尼克案例实战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：仓位级别    #Cabin：船舱编号，NaN是缺失值（就是为空的值）    #Embarked 登船地点 S C Q 三个码头    titanic_survival.head()复制代码

控制空值判断及展示

#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 valuesage = 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     NaN17    NaN19    NaN26    NaN28    NaN29    NaN31    NaN32    NaN36    NaN42    NaN45    NaN46    NaN复制代码

含有空值时将无法计算

#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    #过滤出非空值，但是略显复杂  #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复制代码

每个船舱位的平均价格

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不同列之间的关系,pivot_table高级用法  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    # 默认求均值  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    #多列之间关系  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复制代码

丢掉缺失值 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   复制代码

通过索引和列名

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复制代码

按值排序(索引不变)，并重设索引

#按值排序(索引不变)  new_titanic_survival = titanic_survival.sort_values("Age",ascending=False)  #print (new_titanic_survival[0:10])    #索引发生变化  itanic_reindexed = new_titanic_survival.reset_index(drop=True)  print(itanic_reindexed.iloc[0:10])复制代码

返回第100行数据

# 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复制代码

自定义行数非空判断

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复制代码

定义级别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复制代码

自定义函数

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复制代码

分类类别

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复制代码

2.6.3 Series 案例实战3

#Pandas默认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复制代码

设置索引列，根据索引取得对应的值

# 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)  #根据索引取得对应的值  series_custom[['Minions (2015)', 'Leviathan (2014)']]     Minions (2015)      54  Leviathan (2014)    99  dtype: int64复制代码

范围查找

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复制代码

索引排序，并重设

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复制代码

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复制代码

Series对应索引相加

#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)复制代码

Series对应Lambda表达式（求标准差）

#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复制代码

对应两列通过Lambda表达式求标准差

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复制代码

3 matplotlib使用实践

折线图

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()复制代码

first_twelve = unrate[0:12]    plt.plot(first_twelve['DATE'], first_twelve['VALUE'])    plt.show()复制代码

plt.plot(first_twelve['DATE'], first_twelve['VALUE'])    plt.xticks(rotation=45)    #print help(plt.xticks)    plt.show()复制代码

#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()复制代码

多条折线图展示

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()复制代码

柱状图竖型展示

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])    
      
       复制代码

import matplotlib.pyplot as plt    from numpy import arange        #取出第一行指定列num_cols的数据    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    #横轴位置    bar_positions = arange(5) + 0.75    #横轴标识的位置（1到6之间）    tick_positions = range(1,6)        fig, ax = plt.subplots()    #0.5标识柱状图宽度    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() 复制代码

柱状图横向表示

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   #横轴标识名的位置（1到6之间）  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()复制代码

散点图

#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()复制代码

Hist的bins区间统计

import pandas as pdimport matplotlib.pyplot as pltreviews = 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])#按照列进行分组聚合fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts()fandango_distribution = fandango_distribution.sort_index()#按照列进行分组聚合imdb_distribution = norm_reviews['IMDB_norm'].value_counts()imdb_distribution = imdb_distribution.sort_index()print(fandango_distribution)2.7     22.8     22.9     53.0     43.1     33.2     53.3     43.4     93.5     93.6     83.7     93.8     53.9    124.0     74.1    164.2    124.3    114.4     74.5     94.6     44.8     3Name: Fandango_Ratingvalue, dtype: int64print(imdb_distribution)2.00     12.10     12.15     12.20     12.30     22.45     22.50     12.55     12.60     22.70     42.75     52.80     22.85     12.90     12.95     33.00     23.05     43.10     13.15     93.20     63.25     43.30     93.35     73.40     13.45     73.50     43.55     73.60    103.65     53.70     83.75     63.80     33.85     43.90     93.95     24.00     14.05     14.10     44.15     14.20     24.30     1Name: IMDB_norm, dtype: int64fig, ax = plt.subplots()#ax.hist(norm_reviews['Fandango_Ratingvalue'])#ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)指定区间为20个，范围为4到5ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)plt.show()复制代码

4分图盒图

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']  fig, ax = plt.subplots()  #指定统计列取出对应值  ax.boxplot(norm_reviews[num_cols].values)  ax.set_xticklabels(num_cols, rotation=90)  ax.set_ylim(0,5)  plt.show()复制代码

4 Seaborn专业可视化库（基于matplot）

风格设置

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)复制代码

sns.set_style("dark")     sinplot()         sns.set_style("white")    sinplot()         sns.set_style("whitegrid")    sns.boxplot(data=data, palette="deep")    sns.despine(left=True)复制代码

调色板设置

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)复制代码

6个默认的颜色循环主题： deep, muted, pastel, bright, dark, colorblind        sns.palplot(sns.color_palette("hls", 8))复制代码

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))复制代码

区间直方图绘制（kde是否指定核密度估计）

x = np.random.gamma(6, size=200)  sns.distplot(x, kde=False, fit=stats.gamma)复制代码

线性回归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()复制代码

sns.regplot(x="total_bill", y="tip", data=tips)复制代码

线性回归2

sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips);复制代码

多分类问题

%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);复制代码

sns.stripplot(x="day", y="total_bill", data=tips, jitter=True)复制代码

树桩展示均匀展示

sns.swarmplot(x="day", y="total_bill", data=tips)复制代码

树桩展示均匀并分类展示

sns.swarmplot(x="day", y="total_bill", hue="sex",data=tips)复制代码

盒图

IQR即统计学概念四分位距，第一/四分位与第三/四分位之间的距离 N = 1.5IQR 如果一个值>Q3+N或 < Ｑ1-N,则为离群点 #横杠最小值和最大值 sns.boxplot(x="day", y="total_bill", hue="time", data=tips);  复制代码

小提琴图（越胖包含的数据越多）

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True);复制代码

葫芦图

sns.violinplot(x="day", y="total_bill", data=tips, inner=None) sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5)复制代码

柱状分类统计图

sns.barplot(x="sex", y="survived", hue="class", data=titanic);复制代码

点图可以更好的描述变化差异

sns.pointplot(x="sex", y="survived", hue="class", data=titanic);复制代码

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

多层面板分类图

sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips)复制代码

sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar")复制代码

sns.factorplot(x="day", y="total_bill", hue="smoker",              col="time", data=tips, kind="swarm")复制代码

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

FacetGrid 多参数网格面板

%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()复制代码

g = sns.FacetGrid(tips, col="time")    g.map(plt.hist, "tip");复制代码

g = sns.FacetGrid(tips, col="sex", hue="smoker")    g.map(plt.scatter, "total_bill", "tip", alpha=.7)    g.add_legend();复制代码

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);复制代码

热力图

%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 ]]复制代码

ax = sns.heatmap(flights, linewidths=.5)复制代码

5 总结

方便复习，整成笔记，内容粗略，勿怪，待完善。