3-1 Pandas-概述 – Python量化投资

3-1 Pandas-概述

 Pandas章节应用的数据可以在以下链接下载:

https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip

 












           Pandas:数据分析处理库







In [1]:


import pandas as pd









In [4]:


df=pd.read_csv('./Titanic_Data-master/Titanic_Data-master/train.csv')








 

.head():可以读取前几条数据,或指定前几条都可以







In [5]:


df.head(6)









Out[5]:
 























 PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th…female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ









 

.info():返回当前的信息







In [6]:


df.info()









 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB








 

查看表格的各项属性和细节







In [7]:


df.index#索引值的属性









Out[7]:

RangeIndex(start=0, stop=891, step=1)









In [8]:


df.columns#每一列的名字









Out[8]:

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')









In [9]:


df.dtypes#每一列的值的类型









Out[9]:

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object









In [10]:


df.values#每行的值









Out[10]:

array([[1, 0, 3, ..., 7.25, nan, 'S'],
       [2, 1, 1, ..., 71.2833, 'C85', 'C'],
       [3, 1, 3, ..., 7.925, nan, 'S'],
       ...,
       [889, 0, 3, ..., 23.45, nan, 'S'],
       [890, 1, 1, ..., 30.0, 'C148', 'C'],
       [891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)








 

自己创建data_frame数据







In [11]:


data={'country':['aaa','bbb','ccc'],'population':[10,12,14]}
df_data=pd.DataFrame(data)
df_data









Out[11]:
 




















 countrypopulation
0aaa10
1bbb12
2ccc14










In [12]:


df_data.info()









 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
country       3 non-null object
population    3 non-null int64
dtypes: int64(1), object(1)
memory usage: 128.0+ bytes









In [15]:


age=df['Age']#搜索对应的一列
age[:5]#显示前5行数据









Out[15]:

0    22.0
1    38.0
2    26.0
3    35.0
4    35.0
Name: Age, dtype: float64








 

series:dataframe中的一行/列







In [16]:


age.index









Out[16]:

RangeIndex(start=0, stop=891, step=1)









In [17]:


age.values[:5]









Out[17]:

array([22., 38., 26., 35., 35.])









In [18]:


df.head()









Out[18]:
 






















 PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th…female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS










In [19]:


df['Age'][:5]









Out[19]:

0    22.0
1    38.0
2    26.0
3    35.0
4    35.0
Name: Age, dtype: float64








 

改变索引对象







In [20]:


df=df.set_index('Name')
df.head()









Out[20]:
 






















 PassengerIdSurvivedPclassSexAgeSibSpParchTicketFareCabinEmbarked
Name           
Braund, Mr. Owen Harris103male22.010A/5 211717.2500NaNS
Cumings, Mrs. John Bradley (Florence Briggs Thayer)211female38.010PC 1759971.2833C85C
Heikkinen, Miss. Laina313female26.000STON/O2. 31012827.9250NaNS
Futrelle, Mrs. Jacques Heath (Lily May Peel)411female35.01011380353.1000C123S
Allen, Mr. William Henry503male35.0003734508.0500NaNS










In [21]:


df['Age'][:5]









Out[21]:

Name
Braund, Mr. Owen Harris                                22.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0
Heikkinen, Miss. Laina                                 26.0
Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0
Allen, Mr. William Henry                               35.0
Name: Age, dtype: float64









In [25]:


age=df['Age']
age[:5]









Out[25]:

Name
Braund, Mr. Owen Harris                                22.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer)    38.0
Heikkinen, Miss. Laina                                 26.0
Futrelle, Mrs. Jacques Heath (Lily May Peel)           35.0
Allen, Mr. William Henry                               35.0
Name: Age, dtype: float64









In [26]:


age['Allen, Mr. William Henry']#索引名字对应的值









Out[26]:

35.0









In [27]:


age=age+10
age[:5]









Out[27]:

Name
Braund, Mr. Owen Harris                                32.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer)    48.0
Heikkinen, Miss. Laina                                 36.0
Futrelle, Mrs. Jacques Heath (Lily May Peel)           45.0
Allen, Mr. William Henry                               45.0
Name: Age, dtype: float64








 

对值统计指标







In [28]:


age.mean()









Out[28]:

39.69911764705882









In [29]:


age.max()









Out[29]:

90.0









In [30]:


age.min()









Out[30]:

10.42









In [31]:


df.describe()####整体一次性统计各项的指标基本统计特性









Out[31]:
 
























 PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200













https://www.cnblogs.com/AI-robort/p/11636703.html

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