导入pandas

import pandas as pd

1. 字典键将用作列标题，而每个列表中的值将用作的行DataFrame。

In [2]: df = pd.DataFrame({
   ...:     "Name": ["Braund, Mr. Owen Harris",
   ...:              "Allen, Mr. William Henry",
   ...:              "Bonnell, Miss. Elizabeth"],
   ...:     "Age": [22, 35, 58],
   ...:     "Sex": ["male", "male", "female"]}
   ...: )

df

	Name	Age	Sex
0	Braund, Mr. Owen Harris	22	male
1	Allen, Mr. William Henry	35	male
2	Bonnell, Miss. Elizabeth	58	female

2. DataFrame的每一列都是一个Series，并且每个Series都又是一个DataFrame

df["Age"]

0    22
1    35
2    58
Name: Age, dtype: int64

ages = pd.Series([22, 35, 58], name="Age")

ages

0    22
1    35
2    58
Name: Age, dtype: int64

3. 对数据进行操作

df["Age"].max()

ages.max()

df.describe()#提供了对数值数据的快速概述DataFrame。
#由于Name和Sex列是文本数据，因此默认情况下该describe()方法不考虑.

	Age
count	3.000000
mean	38.333333
std	18.230012
min	22.000000
25%	28.500000
50%	35.000000
75%	46.500000
max	58.000000

读取和写入表格数据

1.read_csv()将存储为csv文件的数据读入

titanic=pd.read_csv("data/titanic.csv") #read_csv()将存储为csv文件的数据读入

titanic

	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
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

2.使用head()查看前n行，tail（）查看后m行

titanic.head(8)

	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
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

titanic.tail(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.00	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

3. dtypes属性：解释每种列数据类型

titanic.dtypes#数据类型DataFrame为整数（int64），浮点数（float63）和字符串（object）。

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

4.数据存储

read_*函数用来读取数据
to_*方法用于存储数据
to_excel()方法将数据存储为excel文件。

titanic.to_excel('titanic.xlsx',sheet_name='passengers',index=False)#sheet_name名为passemgers,通过设置 index=False行索引标签不会保存在电子表格中。

titanic=pd.read_excel('titanic.xlsx',sheet_name='passengers')

titanic.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

5. 方法info()提供有关的技术信息

titanic.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

小结：

read_*函数支持将数据从许多不同的文件格式或数据源导入。
通过不同的to_*方法可以将数据导出。
head/ tail/ info方法和dtypes属性。

选择子集

1.选择单列

ages=titanic['Age']

ages.head()

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

type(titanic["Age"])#每一列DataFrame都是一个Series

pandas.core.series.Series

titanic["Age"].shape#单个列返回的对象是仍然是DataFrame

(891,)

2.选择多列

age_sex=titanic[["Age","Sex"]]
age_sex.head()

	Age	Sex
0	22.0	male
1	38.0	female
2	26.0	female
3	35.0	female
4	35.0	male

type(titanic[["Age","Sex"]])

pandas.core.frame.DataFrame

titanic[["Age","Sex"]].shape#返回了DataFrame891行和2列。请记住，a DataFrame是二维的，具有行和列的维

(891, 2)

过滤特定行

above_35=titanic[titanic["Age"]>35]

above_35.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S

titanic["Age"]>35

0      False
1       True
2      False
3      False
4      False
       ...  
886    False
887    False
888    False
889    False
890    False
Name: Age, Length: 891, dtype: bool

above_35.shape

(217, 12)

class_23=titanic[titanic["Pclass"].isin([2,3])]

class_23.head()

	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
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	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
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

1.isin()条件函数

与条件表达式类似,返回布尔型
为在列表中的每一行某个不为空的值都返回一个True
（ the isin() conditional function returns a True for each row the values are in the provided list.）

class_23=titanic[(titanic["Pclass"]==2)|(titanic["Pclass"]==3)]

class_23.head()

	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
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	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
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

注意

组合多个条件语句时，每个条件必须用括号括起来()。
不能使用 or，and只能使用|和&。

2.notna()：为每一行的某一个not Null的值返回一个True

age_no_na=titanic[titanic["Age"].notna()]

age_no_na.shape

(714, 12)

选择特定的行和列

1. loc[] ：行和列名

adult_names=titanic.loc[titanic["Age"]>35,"Name"]

adult_names.head()

1     Cumings, Mrs. John Bradley (Florence Briggs Th...
6                               McCarthy, Mr. Timothy J
11                             Bonnell, Miss. Elizabeth
13                          Andersson, Mr. Anders Johan
15                     Hewlett, Mrs. (Mary D Kingcome) 
Name: Name, dtype: object

2. iloc[]:基于位置

titanic.iloc[9:25,2:5]#取第10至25行和第3至5列

	Pclass	Name	Sex
9	2	Nasser, Mrs. Nicholas (Adele Achem)	female
10	3	Sandstrom, Miss. Marguerite Rut	female
11	1	Bonnell, Miss. Elizabeth	female
12	3	Saundercock, Mr. William Henry	male
13	3	Andersson, Mr. Anders Johan	male
14	3	Vestrom, Miss. Hulda Amanda Adolfina	female
15	2	Hewlett, Mrs. (Mary D Kingcome)	female
16	3	Rice, Master. Eugene	male
17	2	Williams, Mr. Charles Eugene	male
18	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female
19	3	Masselmani, Mrs. Fatima	female
20	2	Fynney, Mr. Joseph J	male
21	2	Beesley, Mr. Lawrence	male
22	3	McGowan, Miss. Anna "Annie"	female
23	1	Sloper, Mr. William Thompson	male
24	3	Palsson, Miss. Torborg Danira	female

3.将表中元素进行替换

titanic.iloc[:3,3]="YuHongxia"#将前三行的第四列换成YuHongxia

titanic.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	YuHongxia	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	YuHongxia	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	YuHongxia	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

小结：

选择数据子集时，使用方括号[]不是（）。
在括号内，可以使用单个列/行标签，列/行标签列表，标签切片，条件表达式或冒号。
选择特定的行和列时，如果使用行和列名可以使用loc
选择特定的行和列时，如果使用表中的位置可以使用iloc
可以基于loc/ iloc为选择分配新值。

笔记——pandas学习1

导入pandas

1. 字典键将用作列标题，而每个列表中的值将用作的行DataFrame。

2. DataFrame的每一列都是一个Series，并且每个Series都又是一个DataFrame

3. 对数据进行操作

读取和写入表格数据

1.read_csv()将存储为csv文件的数据读入

2.使用head()查看前n行，tail（）查看后m行

3. dtypes属性：解释每种列数据类型

4.数据存储

5. 方法info()提供有关的技术信息

小结：

选择子集

1.选择单列

2.选择多列

过滤特定行

1.isin()条件函数

注意

2.notna()：为每一行的某一个not Null的值返回一个True

选择特定的行和列

1. loc[] ：行和列名

2. iloc[]:基于位置

3.将表中元素进行替换

小结：

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