【python-数据分析】pandas数据提取

import pandas as pd

1. 直接索引

df = pd.DataFrame({'AdmissionDate': ['2021-01-25','2021-01-22','2021-01-20',

                        '2021-01-18','2021-01-17','2021-01-17','2021-01-21'],

                     'StudentID': [7,1,3,2,6,3,4],

                     'Name': ['Jack','Shyam','Mohan','Janne','Lucky','Abhinav','Danny'],

                     'Stream':['CSE','ECE','Civil','Mechanical','CSE','IT','EEE']

                   })

df.set_index("Name",inplace=True)

# 选取某一列或者某几列

print(df["AdmissionDate"])

print(df[["AdmissionDate","StudentID"]])  # 选取多列时，多个列要放到一个list中

Name

Jack       2021-01-25

Shyam      2021-01-22

Mohan      2021-01-20

Janne      2021-01-18

Lucky      2021-01-17

Abhinav    2021-01-17

Danny      2021-01-21

Name: AdmissionDate, dtype: object

        AdmissionDate  StudentID

Name

Jack       2021-01-25          7

Shyam      2021-01-22          1

Mohan      2021-01-20          3

Janne      2021-01-18          2

Lucky      2021-01-17          6

Abhinav    2021-01-17          3

Danny      2021-01-21          4

# 按行编号选取连续的行

df[1:3]  # df[start:end], [start,end)闭开区间

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	AdmissionDate	StudentID	Stream
Name
Shyam	2021-01-22	1	ECE
Mohan	2021-01-20	3	Civil

# 按行时间索引选取连续的行

df["AdmissionDate"] = pd.to_datetime(df["AdmissionDate"])

df.set_index("AdmissionDate",inplace=True)

# df["2021-01-01":"2021-01-20"]  # 将被弃用

df.sort_index().loc["2021-01-01":"2021-01-20",:]  # 推荐写法

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	StudentID	Stream
AdmissionDate
2021-01-17	6	CSE
2021-01-17	3	IT
2021-01-18	2	Mechanical
2021-01-20	3	Civil

2. 布尔索引

df = pd.DataFrame({'AdmissionDate': ['2021-01-25','2021-01-22','2021-01-20',

                        '2021-01-18','2021-01-17','2021-01-17','2021-01-21'],

                     'StudentID': [7,1,3,2,6,3,4],

                     'Name': ['Jack','Shyam','Mohan','Janne','Lucky','Abhinav','Danny'],

                     'Stream':['CSE','ECE','Civil','Mechanical','CSE','IT','EEE']

                   })

df["AdmissionDate"] = pd.to_datetime(df["AdmissionDate"])

df.set_index("Name",inplace=True)

df

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	AdmissionDate	StudentID	Stream
Name
Jack	2021-01-25	7	CSE
Shyam	2021-01-22	1	ECE
Mohan	2021-01-20	3	Civil
Janne	2021-01-18	2	Mechanical
Lucky	2021-01-17	6	CSE
Abhinav	2021-01-17	3	IT
Danny	2021-01-21	4	EEE

# 选取满足某一条件的行

df[df["StudentID"]==2]

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	AdmissionDate	StudentID	Stream
Name
Janne	2021-01-18	2	Mechanical

# 选取满足多个条件的行

# 注意：索引列表中，可以使用& |操作符，但不能使用and or not等关键字

from datetime import datetime

df[(df["StudentID"]>=3) & (df["AdmissionDate"]>="2021-01-20")]  # 注意：索引列表中，各布尔条件必须用圆括号扩起来

2021-01-20 00:00:00

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	AdmissionDate	StudentID	Stream
Name
Jack	2021-01-25	7	CSE
Mohan	2021-01-20	3	Civil
Danny	2021-01-21	4	EEE

df[(df["StudentID"]>=3) | (df["AdmissionDate"]>="2021-01-20")]

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	AdmissionDate	StudentID	Stream
Name
Jack	2021-01-25	7	CSE
Shyam	2021-01-22	1	ECE
Mohan	2021-01-20	3	Civil
Lucky	2021-01-17	6	CSE
Abhinav	2021-01-17	3	IT
Danny	2021-01-21	4	EEE

3. 索引器索引

Dataframe的loc和iloc属性

loc属性：
- 以列名和行名作为参数，当只有一个参数时，默认是行名，即抽取整行数据，包括所有列
iloc属性：
- 以行和列位置索引，作为参数。当只有一个参数时，默认是行索引，即抽取整行数据，包括所有列

df = pd.DataFrame({'AdmissionDate': ['2021-01-25','2021-01-22','2021-01-20',

                        '2021-01-18','2021-01-17','2021-01-17','2021-01-21'],

                     'StudentID': [7,1,3,2,6,3,4],

                     'Name': ['Jack','Shyam','Mohan','Janne','Lucky','Abhinav','Danny'],

                     'Stream':['CSE','ECE','Civil','Mechanical','CSE','IT','EEE']

                   })

df["AdmissionDate"] = pd.to_datetime(df["AdmissionDate"])

df.set_index("Name",inplace=True)

df

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	AdmissionDate	StudentID	Stream
Name
Jack	2021-01-25	7	CSE
Shyam	2021-01-22	1	ECE
Mohan	2021-01-20	3	Civil
Janne	2021-01-18	2	Mechanical
Lucky	2021-01-17	6	CSE
Abhinav	2021-01-17	3	IT
Danny	2021-01-21	4	EEE

3.1 loc索引器

# 选取一行, loc["行索引名称"]

df.loc["Jack"]

AdmissionDate    2021-01-25 00:00:00

StudentID                          7

Stream                           CSE

Name: Jack, dtype: object

# df.loc[['行1,行2'],['列1,列2']]：选取行列组合

df.loc[["Jack","Janne"],["StudentID","Stream"]]

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	StudentID	Stream
Name
Jack	7	CSE
Janne	2	Mechanical

# df.loc[(df['列']>条件)]：按条件选取列满足一定条件的行。

df.loc[df["StudentID"]>=2,["Stream","AdmissionDate"]]

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	Stream	AdmissionDate
Name
Jack	CSE	2021-01-25
Mohan	Civil	2021-01-20
Janne	Mechanical	2021-01-18
Lucky	CSE	2021-01-17
Abhinav	IT	2021-01-17
Danny	EEE	2021-01-21

# df.loc[行1:行2,列1:列2]：按列名选取连续的列。冒号前后留空代表开口。

df.loc["Jack":"Janne","AdmissionDate":"StudentID"]

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	AdmissionDate	StudentID
Name
Jack	2021-01-25	7
Shyam	2021-01-22	1
Mohan	2021-01-20	3
Janne	2021-01-18	2

3.2 iloc索引器

iloc索引器与loc索引器的使用几乎相同，唯一不同的是，iloc索引器中只能使用原始索引，不能使用自定义索引。

注意：原始索引初值从0开始，切片前闭后开。自定义索引切片为闭区间

df.iloc[1:3,1:2]

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	StudentID
Name
Shyam	1
Mohan	3

带步长的索引

df.iloc[::2]