前文已经对数据分析的基本操作进行了学习，接下来要进行数据清洗、数据特征提取、数据重构以及数据可视化的学习。

1 Data cleaning

1.1 Load data

import numpy as np
import pandas as pd

df = pd.read_csv('./train.csv')
df.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

1.2 Statistic description

1.2.1 查看信息

>>> data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object
 4   Sex          891 non-null    object
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object
 11  Embarked     889 non-null    object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

1.2.2 缺失值

>>> data.isnull().sum()

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

1	>>> data[['Age','Cabin','Embarked']].head(3)

	Age	Cabin	Embarked
0	22.0	NaN	S
1	38.0	C85	C
2	26.0	NaN	S

Note：数值列读取数据后，空缺值的数据类型为 float64 所以用 None 一般索引不到，比较的时候最好用 np.nan

1.2.3 填充缺失值

处理缺失值一般有：

用数字 0 进行填充
用均值进行填充（针对数值型）
用众数进行填充（多用于非数值型）
用特定标识符进行填充（实际处理时特殊对待）
对于缺失值占比太大的列直接进行剔除

1 2	>>> data.fillna(0, inplace = True) data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	0	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	0	S

fillna 用法

从帮助文档可以知道，fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)，fillna 包含六个参数：
- Value: 用做填充的值
- Method: 包含 backfill, bfill, pad, ffill, None 五个值，默认为 None。
  - backfill/bfill: 用位于缺失值索引值上的有效值进行填充，直到下一个有效值。
  - pad/ffill: 用位于缺失值索引下的有效值进行填充，直到上一个有效值。
- axis: {0 or 'index', 1 or 'columns'}
- inplace : bool, default False

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [np.nan, np.nan, np.nan, 5],
                   [np.nan, 3, np.nan, 4]],
                  columns=list('ABCD'))
>>> df

	A	B	C	D
0	NaN	2.0	NaN	0
1	3.0	4.0	NaN	1
2	NaN	NaN	NaN	5
3	NaN	3.0	NaN	4

1	>>> df.fillna(method='ffill')

	A	B	C	D
0	NaN	2.0	NaN	0
1	3.0	4.0	NaN	1
2	3.0	4.0	NaN	5
3	3.0	3.0	NaN	4

1.2.4 去除空值

1	>>> data.dropna().head(3) # 未设置 inplace参数

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	17463	51.8625	E46	S

1.2.5 重复值

查看重复值
1
>>> data[data.duplicated()]
PassengerId

Survived

Pclass

Name

Sex

Age

SibSp

Parch

Ticket

Fare

Cabin

Embarked

剔除重复值

1 2	>>> data = data.drop_duplicates() >>> data.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

1.3 Feature analysis

对数据进行初步观察后可以发现，特征主要可以分为两大类：

数值型特征：Survived ，Pclass， Age ，SibSp， Parch， Fare，其中Survived， Pclass为离散型数值特征，Age，SibSp， Parch， Fare为连续型数值特征

文本型特征：Name， Sex， Cabin，Embarked， Ticket，其中Sex， Cabin， Embarked， Ticket为类别型文本特征。

数值型特征一般可以直接用于模型的训练，但有时候为了模型的稳定性及鲁棒性会对连续变量进行离散化。文本型特征往往需要转换成数值型特征才能用于建模分析。

1.3.1 分箱操作

按平均进行分箱

将连续变量Age平均分成5个年龄段，并分别用类别变量12345表示

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2
3

>>> data['AgeBand'] = pd.cut(data['Age'], 5, labels = [1, 2, 3, 4, 5])

>>> data.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	AgeBand
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	2
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	3

按数值分箱

将连续变量Age划分为[0,5) [5,15) [15,30) [30,50) [50,80)五个年龄段，并分别用类别变量12345表示。

1 2	>>> data['AgeBand'] = pd.cut(data['Age'], [0, 5, 15, 30, 50, 80], labels = [1, 2, 3, 4, 5]) >>> data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	AgeBand
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	3
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	4
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	3

按比例分箱

将连续变量Age按10% 30% 50% 70% 90%五个年龄段，并用分类变量12345表示

1 2	>>> data['AgeBand'] = pd.cut(data['Age'], [0, 0.1, 0.3, 0.5, 0.7, 0.9], labels = [1, 2, 3, 4, 5]) >>> data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	AgeBand
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	NaN
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	NaN
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	NaN

1.3.2 提取字符特征

从纯文本 Name 特征里提取出 Titles 的特征( Mr, Miss, Mrs 等)

1 2	>>> data['Title'] = data.Name.str.extract('([A-Za-z]+)\.', expand=False) >>> data.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	Sex_female	Sex_male	Embarked_C	Embarked_S	Title
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	2	0	1	0	1	Mr
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	1	1	0	1	0	Mrs
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	2	1	0	0	1	Miss
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	2	1	0	0	1	Mrs
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	2	0	1	0	1	Mr

1.4 Type transformation

查看文本变量的信息。

value_counts

>>> data['Sex'].value_counts()

male      577
female    314
Name: Sex, dtype: int64

unique

1
2
3

>>> data['Sex'].unique()

array(['male', 'female'], dtype=object)

1.4.1 判别式方式

将性别转换未数值型数据，用 0 表示男性，1 表示女性。

>>> data = data_copy.copy()
    data.loc[data['Sex'] == 'male', 'Sex'] = 0 # 'male' -> 0
    data.loc[data['Sex'] == 'female', 'Sex'] = 1 # 'female' -> 1
    data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	0	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	1	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	1	26.0	0	STON/O2. 3101282	7.9250	NaN	S

1.4.2 `replace` 方法：

1
2
3

>>> data = data_copy.copy()
    data['Sex_num'] = data['Sex'].replace(['male','female'],[1,2])
    data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	Sex_num
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	1
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	2
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	2

1.4.3 `map` 方法

1
2
3

>>> data = data_copy.copy()
    data['Sex_num'] = data['Sex'].map({'male': 1, 'female': 2})
    data.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	Sex_num
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	1
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	2
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	2
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S	2
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S	1

1.4.4 `labelEncoder`

使用sklearn.preprocessing.labelEncoder

>>> from sklearn.preprocessing import LabelEncoder

    data = data_copy.copy()
    for feat in ['Cabin', 'Ticket']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(data[feat].unique(), range(data[feat].nunique())))
        data[feat + "_labelEncode"] = data[feat].map(label_dict)
        # data[feat + "_labelEncode"] = lbl.fit_transform(data[feat].astype(str))

    data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	Cabin_labelEncode	Ticket_labelEncode
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	0.0	0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	1.0	1
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	0.0	2

其中，label_dict 是个字典类型，其 key 值表示真实值，即 data[feat].unique()，而对应的数值为 range(data[feat].nunique() 产生的序列。

一点小差异：

从上述代码可以发现，有两种实现方式：

map 方式

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>>> data[feat].map(label_dict).values[:5]

array([0, 1, 2, 3, 4], dtype=int64)

labelEncoder 方式

1
2
3

>>> lbl.fit_transform(data[feat].astype(str))[:5]

array([523, 596, 669,  49, 472])

Note:

可以发现，虽然两种方式都能产生同样的效果，当时生成数值编码的方式存在些许差异，第一种方式为按顺序生成，第二种会打乱顺序。

1.4.5 `ont-hot` 编码

>>> data = data_copy.copy()
    for feat in ["Sex", "Embarked"]:
        x = pd.get_dummies(data[feat], prefix=feat)
        data = pd.concat([data, x], axis=1)
        # data[feat] = pd.get_dummies(data[feat], prefix=feat)

    data.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	Sex_female	Sex_male	Embarked_C	Embarked_S
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	0	1	0	1
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	1	0	1	0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	1	0	0	1

对于 age，可考虑如下的方式进行处理。

整除

1	pd.get_dummies(data["Age"] // 6)[:5]

	3.0	4.0	5.0	6.0
0	1	0	0	0
1	0	0	0	1
2	0	1	0	0
3	0	0	1	0
4	0	0	1	0

分箱

1	pd.get_dummies(pd.cut(data['Age'],5))[:5]

	(16.336, 32.252]	(32.252, 48.168]
0	1	0
1	0	1
2	1	0
3	0	1
4	0	1

2 Data reconstruction

前面已经学习了 Pandas 基础和数据清洗，只有数据变得相对干净，之后对数据的分析才可以更有力。本节要做的是数据重构，数据重构依旧属于数据理解（准备）的范围。

2.1 Data merge

数据合并内容之前已经进行了详细的记录，此处便不进行赘述，具体可以查看本人的博客内容，链接： Pandas的Chaper 4 Merge dataframes

2.2 Aggregae operation

计算男女平均票价

>>> fare_sex  = data['Fare'].groupby(data['Sex'])
    fare_sex.mean()

Sex
female    44.479818
male      25.523893
Name: Fare, dtype: float64

计算男女存活人数

>>> data['Survived'].groupby(data['Sex']).sum()

Sex
female    233
male      109
Name: Survived, dtype: int64

结论：可以发现，女性票价远大于男性，其次，女性存活率大于男性。

2.2.1 Aggregate 函数

此外，上述操作还可以通过 agg 函数进行计算。

1 2	>>> data.groupby('Sex').agg({'Fare': 'mean', 'Pclass': 'count'}).rename(columns= {'Fare': 'mean_fare', 'Pclass': 'count_pclass'})

	mean_fare	count_pclass
Sex
female	44.479818	314
male	25.523893	577

Note:

agg() 函数主要有两个参数：

func: function, str, list or dict. Function to use for aggregating the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply. e.g. mean, median, prod, sum, std, var.
axis: default is 0 or 'index', operating based on all columns.

2.2.2 其他函数

统计不同等级的票中，不同年龄的船票花费的平均价格

>>> data.groupby(['Pclass','Age'])['Fare'].mean()

Pclass  Age  
1       0.92     151.5500
        2.00     151.5500
        4.00      81.8583
        11.00    120.0000
        14.00    120.0000
                   ...   
3       61.00      6.2375
        63.00      9.5875
        65.00      7.7500
        70.50      7.7500
        74.00      7.7750
Name: Fare, Length: 182, dtype: float64

不同年龄的存活人数

得出不同年龄的总的存活人数，然后找出存活人数的最高的年龄，最后计算存活人数最高的存活率（存活人数/总人数）

>>> survived_age = data['Survived'].groupby(data['Age']).sum()
    survived_age.head()

Age
0.42    1
0.67    1
0.75    2
0.83    2
0.92    1
Name: Survived, dtype: int64

>>> survived_age[survived_age.values == survived_age.max()]

Age
24.0    15
Name: Survived, dtype: int64

总人数：

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3

>>> data['Survived'].sum()

342

>>> sum_temp = data['Survived'].sum()
    print('最大存活率为：{}'.format(survived_age.max()/sum_temp))

最大存活率为：0.043859649122807015

2.3 Stack to series

>>> data.stack()

0    PassengerId                          1
     Survived                             0
     Pclass                               3
     Name           Braund, Mr. Owen Harris
     Sex                               male
                             ...           
890  Sex_male                             1
     Embarked_C                           0
     Embarked_Q                           1
     Embarked_S                           0
     Title                               Mr
Length: 15172, dtype: object

探究 stack 函数的功能：

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3

>>> data.stack().shape

(15172,)

>>> unit_result.head()

0  Unnamed: 0                           0
   PassengerId                          1
   Survived                             0
   Pclass                               3
   Name           Braund, Mr. Owen Harris
dtype: object

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>>> print(891*18)

16038

可以发现，stack 函数的作用是将数据表中的每一行都转化为一个 Series，共计 891 个 Series，其形状为 15172 行，我们注意到 \(891 \times 18 = 16038\)，之间相差一些，是因为 data 中存在部分缺失值。

3 Data visualization

前文中，已经对基本数据结构有了了解，也学了一些基本的统计方法、数据清洗和重构。本章节中主要学习 数据可视化，主要给大家介绍一下 Python 数据可视化库 Matplotlib。

本章节的内容可以通过查阅《Python for Data Analysis》第九章进行更深入的学习。

Import modules

1 2	>>> %matplotlib inline import matplotlib.pyplot as plt

3.1 柱状图

3.1.1 简单柱状图

可视化展示泰坦尼克号数据集中男女中生存人数分布情况。

生存情况

由于 Survived = 0 表示死亡，Survived = 1 表示生存，所以使用 sum 函数可以统计生存人数情况。

>>> sex = data.groupby('Sex')['Survived'].sum()
    sex.plot.bar()
    plt.title('survived_count')
    plt.show()

死亡情况

对于死亡人数，可以使用判别式 data['Survived'] != 1 来得到死亡人数的索引，之后用 count 函数进行人数统计。

>>> sex = data[data['Survived'] != 1].groupby('Sex').Survived.count()
    sex.plot.bar()
    plt.title('survived_count')
    plt.show()

合并分析

>>> data.groupby(['Sex','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')
    plt.title('survived_count')
    plt.ylabel('count')

Text(0, 0.5, 'count')

1	>>> data.groupby(['Sex','Survived'])['Survived'].count().unstack()

Survived	0	1
Sex
female	81	233
male	468	109

>>> data.groupby(['Sex','Survived'])['Survived'].count()

Sex     Survived
female  0            81
        1           233
male    0           468
        1           109
Name: Survived, dtype: int64

Note:

上文简单介绍了 stack 函数，是将整个数据表的每一行转化为一个 pd.Series，而在此处，通过对比上述两行代码，可以发现，unstack() 的作用是反向操作，将 count 函数得到的 Series 转化为 pd.DataFrame 类型。

3.2.1 分层柱状图

可视化展示泰坦尼克号数据集中不同仓位等级的人生存和死亡人员的分布情况。

>>> pclass_sur = text.groupby(['Pclass'])['Survived'].value_counts()
>>> pclass_sur

Pclass  Survived
1       1           136
        0            80
2       0            97
        1            87
3       0           372
        1           119
Name: Survived, dtype: int64

>>> import seaborn as sns
    sns.countplot(x="Pclass", hue="Survived", data=text)

<AxesSubplot:xlabel='Pclass', ylabel='count'>

3.2 折线图

3.2.1 普通折线图

可视化展示泰坦尼克号数据集中不同票价的人生存和死亡人数分布情况（横轴是不同票价，纵轴是存活人数）。

>>> fare_sur = data.groupby('Fare')['Survived'].value_counts().sort_values(ascending = False)
>>> fare_sur

Fare     Survived
8.0500   0           38
7.8958   0           37
13.0000  0           26
7.7500   0           22
13.0000  1           16
                     ..
7.7417   0            1
26.2833  1            1
7.7375   1            1
26.3875  1            1
22.5250  0            1
Name: Survived, Length: 330, dtype: int64

# 排序后绘折线图
>>> fig = plt.figure(figsize=(20, 18))
    fare_sur.plot(grid=True, fontsize = 15)
    plt.legend()
    plt.show()

3.2.2 核密度图

可视化展示泰坦尼克号数据集中不同年龄的人生存与死亡人数分布情况

>>> facet = sns.FacetGrid(text, hue="Survived",aspect=3)
    facet.map(sns.kdeplot,'Age',shade= True)
    facet.set(xlim=(0, text['Age'].max()))
    facet.add_legend()

<seaborn.axisgrid.FacetGrid at 0x1fe9191cc70>

3.2.3 多个图形

>>> data.Age[data.Pclass == 1].plot(kind='kde')
    data.Age[data.Pclass == 2].plot(kind='kde')
    data.Age[data.Pclass == 3].plot(kind='kde')
    plt.xlabel("age")
    plt.legend((1,2,3),loc="best")

<matplotlib.legend.Legend at 0x1fe919a5d00>

Note:

到这里，可视化就告一段落啦，后期可进一步了解其他可视化模块，如：pyecharts，bokeh 等。

独孤诗人的学习驿站

DataAnalysis-Cleaning

1 Data cleaning

1.1 Load data

1.2 Statistic description

1.2.1 查看信息

1.2.2 缺失值

1.2.3 填充缺失值

1.2.4 去除空值

1.2.5 重复值

1.3 Feature analysis

1.3.1 分箱操作

1.3.2 提取字符特征

1.4 Type transformation

1.4.1 判别式方式

1.4.2 `replace` 方法：

1.4.3 `map` 方法

1.4.4 `labelEncoder`

1.4.5 `ont-hot` 编码

2 Data reconstruction

2.1 Data merge

2.2 Aggregae operation

2.2.1 Aggregate 函数

2.2.2 其他函数

2.3 Stack to series

3 Data visualization

3.1 柱状图

3.1.1 简单柱状图

3.2.1 分层柱状图

3.2 折线图

3.2.1 普通折线图

3.2.2 核密度图

3.2.3 多个图形

	3.0	4.0	5.0	6.0
0	1	0	0	0
1	0	0	0	1
2	0	1	0	0
3	0	0	1	0
4	0	0	1	0

	3.0	4.0	5.0	6.0
0	1	0	0	0
1	0	0	0	1
2	0	1	0	0
3	0	0	1	0
4	0	0	1	0

1 Data cleaning

1.1 Load data

1.2 Statistic description

1.2.1 查看信息

1.2.2 缺失值

1.2.3 填充缺失值

1.2.4 去除空值

1.2.5 重复值

1.3 Feature analysis

1.3.1 分箱操作

1.3.2 提取字符特征

1.4 Type transformation

1.4.1 判别式方式

1.4.2 replace 方法：

1.4.3 map 方法

1.4.4 labelEncoder

1.4.5 ont-hot 编码

2 Data reconstruction

2.1 Data merge

2.2 Aggregae operation

2.2.1 Aggregate 函数

2.2.2 其他函数

2.3 Stack to series

3 Data visualization

3.1 柱状图

3.1.1 简单柱状图

3.2.1 分层柱状图

3.2 折线图

3.2.1 普通折线图

3.2.2 核密度图

3.2.3 多个图形

1.4.2 `replace` 方法：

1.4.3 `map` 方法

1.4.4 `labelEncoder`

1.4.5 `ont-hot` 编码

	3.0	4.0	5.0	6.0
0	1	0	0	0
1	0	0	0	1
2	0	1	0	0
3	0	0	1	0
4	0	0	1	0