3-1 Pandas-概述

 Pandas章節應用的數據能夠在如下連接下載：

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

 

           Pandas:數據分析處理庫¶

In [1]:

import pandas as pd

In [4]:

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

 

.head():能夠讀取前幾條數據，或指定前幾條均可以

In [5]:

df.head(6)

Out[5]:

 

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q

 

.info():返回當前的信息

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

 

查看錶格的各項屬性和細節¶

In [7]:

df.index#索引值的屬性

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#每一列的值的類型

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)

 

本身建立data_frame數據

In [11]:

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

Out[11]:

 

	country	population
0	aaa	10
1	bbb	12
2	ccc	14

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']#搜索對應的一列
age[:5]#顯示前5行數據

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]:

 

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

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

 

改變索引對象

In [20]:

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

Out[20]:

 

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

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']#索引名字對應的值

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

 

對值統計指標

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()####總體一次性統計各項的指標基本統計特性

Out[31]:

 

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200