Python——奥运会数据分析（2）

1.3.8 loc( ) 与iloc( ) ------选取一行的两种方式

（1）通过loc( )方法，选取index=2的那一行元素

datafame.loc[row_indexer,column_indexer]

|----------------|-----------------------|
| row_indexer | 行标签；可以是单个标签、标签列表或标签切片 |
| column_indexer | 列标签；可以是单个标签、标签列表或标签切片 |
| 如果row_indexer和column_indexer都是标签列表或标签切片，则表示选择交叉区域的数据。 ||

python 复制代码

# 1.通过loc方法，选取index=2的那一行元素
athlete.loc[2]  

# 2.得到的结果为
ID                              3
Name          Gunnar Nielsen Aaby
Sex                             M
Age                          24.0
Height                        NaN
Weight                        NaN
Team                      Denmark
NOC                           DEN
Games                 1920 Summer
Year                         1920
Season                     Summer
City                    Antwerpen
Sport                    Football
Event     Football Men's Football
Medal                         NaN
Name: 2, dtype: object

（2）通过iloc( )方法选取某一行，根据行号索引

dataframe.iloc[row_index,column_index]：用法与loc类似，row_index和column_index可以是单个整数、整数列表、整数切片或布尔数

python 复制代码

# 1.选取某一行，根据行号索引
athlete.iloc[2]

# 2.得到的结果为
ID                              3
Name          Gunnar Nielsen Aaby
Sex                             M
Age                          24.0
Height                        NaN
Weight                        NaN
Team                      Denmark
NOC                           DEN
Games                 1920 Summer
Year                         1920
Season                     Summer
City                    Antwerpen
Sport                    Football
Event     Football Men's Football
Medal                         NaN
Name: 2, dtype: object

Series 查看Sex列的数据的类型，发现这个是个一维数组
DataFrame 查看Sex列的数据类型，发现这是个二维数组

python 复制代码

# 1.Series 查看Sex列的数据的类型，发现这个是个一维数组
type(athlete.iloc[2])

# 2.得到的结果为
pandas.core.series.Series

# 3.DataFrame 查看Sex列的数据类型，发现这是个二维数组
type(athlete.iloc[[2]])

# 4.得到的结果为
pandas.core.frame.DataFrame

1.3.9 选取多行或多列

（1）显示前五行，同head()方法一样

python 复制代码

# 1.显示前五行，同head()方法一样
athlete.iloc[0:5] 

# 2.得到的结果为
	ID	Name	Sex	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
0	1	A Dijiang	M	24.0	180.0	80.0	China	CHN	1992 Summer	1992	Summer	Barcelona	Basketball	Basketball Men's Basketball	NaN
1	2	A Lamusi	M	23.0	170.0	60.0	China	CHN	2012 Summer	2012	Summer	London	Judo	Judo Men's Extra-Lightweight	NaN
2	3	Gunnar Nielsen Aaby	M	24.0	NaN	NaN	Denmark	DEN	1920 Summer	1920	Summer	Antwerpen	Football	Football Men's Football	NaN
3	4	Edgar Lindenau Aabye	M	34.0	NaN	NaN	Denmark/Sweden	DEN	1900 Summer	1900	Summer	Paris	Tug-Of-War	Tug-Of-War Men's Tug-Of-War	Gold
4	5	Christine Jacoba Aaftink	F	21.0	185.0	82.0	Netherlands	NED	1988 Winter	1988	Winter	Calgary	Speed Skating	Speed Skating Women's 500 metres	NaN

（2）显示前五列

python 复制代码

# 1.显示前五列
athlete.iloc[:,0:5] 

# 2.得到的结果为
ID	Name	Sex	Age	Height
0	1	A Dijiang	M	24.0	180.0
1	2	A Lamusi	M	23.0	170.0
2	3	Gunnar Nielsen Aaby	M	24.0	NaN
3	4	Edgar Lindenau Aabye	M	34.0	NaN
4	5	Christine Jacoba Aaftink	F	21.0	185.0
...	...	...	...	...	...
271111	135569	Andrzej ya	M	29.0	179.0
271112	135570	Piotr ya	M	27.0	176.0
271113	135570	Piotr ya	M	27.0	176.0
271114	135571	Tomasz Ireneusz ya	M	30.0	185.0
271115	135571	Tomasz Ireneusz ya	M	34.0	185.0
271116 rows × 5 columns

（3）显示前五列与前五行

python 复制代码

# 1.显示前五列与前五行
athlete.iloc[0:5,0:5]

# 2.得到的结果为
ID	Name	Sex	Age	Height
0	1	A Dijiang	M	24.0	180.0
1	2	A Lamusi	M	23.0	170.0
2	3	Gunnar Nielsen Aaby	M	24.0	NaN
3	4	Edgar Lindenau Aabye	M	34.0	NaN
4	5	Christine Jacoba Aaftink	F	21.0	185.0

1.3.10 选取某行某列

（1）loc( ) 方法选取某行某列：先选取索引Sex，再在这列中选取索引为2的元素

python 复制代码

# 1.选取某行某列  先选取索引Sex，再在这列中选取索引为2的元素
athlete.loc[2,'Sex'] 

# 2.得到的结果为
'M'

（2）iloc( ) 方法选取第2行第2列元素

python 复制代码

# 1.选取第2行第2列元素
athlete.iloc[2,2]  

# 2.得到的结果为
'M'

（3）loc( ) 方法选取列名为Sex和Age的列，再选取他们的第2到第4个元素

python 复制代码

# 1.选取列名为Sex和Age的列，再选取他们的第2到第4个元素
athlete.loc[2:4,['Sex','Age']]  

# 2.得到的结果为
Sex	Age
2	M	24.0
3	M	34.0
4	F	21.0

（4）iloc( ) 方法选取他们的行和列的第2到第4个元素，这里采用的是切片的方法

python 复制代码

# 1.选取他们的行和列的第2到第4个元素，这里采用的是切片的方法
athlete.iloc[2:4,2:4]  

# 2.得到的结果为
	Sex	Age
2	M	24.0
3	M	34.0

1.4 数据的增删改

1.4.1 drop( ) 方法------删除某列

drop(labels=None, axis=0,index=None, columns=None,level=None,inplace=False,errors='raise')

|---------|---------------------------------------------------------------|
| labels | 一个字符或者数值，加上axis ，表示带label标识的行或者列；如（labels = 'A',axis = 1）表示A列 |
| axis | axis=0表示行，axis=1表示列 |
| columns | 列名 |
| index | 表示dataframe的index，如index=1，index=a |
| inplace | True表示删除某行后原dataframe变化，False不改变原始dataframe |

python 复制代码

# drop方法：axis=1（列）,删除columns，axis=0 指删除index；删除Sex列
athlete.drop('Sex',axis=1) 

# 2.得到的结果为
ID	Name	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
0	1	A Dijiang	24.0	180.0	80.0	China	CHN	1992 Summer	1992	Summer	Barcelona	Basketball	Basketball Men's Basketball	NaN
1	2	A Lamusi	23.0	170.0	60.0	China	CHN	2012 Summer	2012	Summer	London	Judo	Judo Men's Extra-Lightweight	NaN
2	3	Gunnar Nielsen Aaby	24.0	NaN	NaN	Denmark	DEN	1920 Summer	1920	Summer	Antwerpen	Football	Football Men's Football	NaN
3	4	Edgar Lindenau Aabye	34.0	NaN	NaN	Denmark/Sweden	DEN	1900 Summer	1900	Summer	Paris	Tug-Of-War	Tug-Of-War Men's Tug-Of-War	Gold
4	5	Christine Jacoba Aaftink	21.0	185.0	82.0	Netherlands	NED	1988 Winter	1988	Winter	Calgary	Speed Skating	Speed Skating Women's 500 metres	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
271111	135569	Andrzej ya	29.0	179.0	89.0	Poland-1	POL	1976 Winter	1976	Winter	Innsbruck	Luge	Luge Mixed (Men)'s Doubles	NaN
271112	135570	Piotr ya	27.0	176.0	59.0	Poland	POL	2014 Winter	2014	Winter	Sochi	Ski Jumping	Ski Jumping Men's Large Hill, Individual	NaN
271113	135570	Piotr ya	27.0	176.0	59.0	Poland	POL	2014 Winter	2014	Winter	Sochi	Ski Jumping	Ski Jumping Men's Large Hill, Team	NaN
271114	135571	Tomasz Ireneusz ya	30.0	185.0	96.0	Poland	POL	1998 Winter	1998	Winter	Nagano	Bobsleigh	Bobsleigh Men's Four	NaN
271115	135571	Tomasz Ireneusz ya	34.0	185.0	96.0	Poland	POL	2002 Winter	2002	Winter	Salt Lake City	Bobsleigh	Bobsleigh Men's Four	NaN
271116 rows × 14 columns

# 3.查看原始数据
ID	Name	Sex	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
0	1	A Dijiang	M	24.0	180.0	80.0	China	CHN	1992 Summer	1992	Summer	Barcelona	Basketball	Basketball Men's Basketball	NaN
1	2	A Lamusi	M	23.0	170.0	60.0	China	CHN	2012 Summer	2012	Summer	London	Judo	Judo Men's Extra-Lightweight	NaN
2	3	Gunnar Nielsen Aaby	M	24.0	NaN	NaN	Denmark	DEN	1920 Summer	1920	Summer	Antwerpen	Football	Football Men's Football	NaN
3	4	Edgar Lindenau Aabye	M	34.0	NaN	NaN	Denmark/Sweden	DEN	1900 Summer	1900	Summer	Paris	Tug-Of-War	Tug-Of-War Men's Tug-Of-War	Gold
4	5	Christine Jacoba Aaftink	F	21.0	185.0	82.0	Netherlands	NED	1988 Winter	1988	Winter	Calgary	Speed Skating	Speed Skating Women's 500 metres	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
271111	135569	Andrzej ya	M	29.0	179.0	89.0	Poland-1	POL	1976 Winter	1976	Winter	Innsbruck	Luge	Luge Mixed (Men)'s Doubles	NaN
271112	135570	Piotr ya	M	27.0	176.0	59.0	Poland	POL	2014 Winter	2014	Winter	Sochi	Ski Jumping	Ski Jumping Men's Large Hill, Individual	NaN
271113	135570	Piotr ya	M	27.0	176.0	59.0	Poland	POL	2014 Winter	2014	Winter	Sochi	Ski Jumping	Ski Jumping Men's Large Hill, Team	NaN
271114	135571	Tomasz Ireneusz ya	M	30.0	185.0	96.0	Poland	POL	1998 Winter	1998	Winter	Nagano	Bobsleigh	Bobsleigh Men's Four	NaN
271115	135571	Tomasz Ireneusz ya	M	34.0	185.0	96.0	Poland	POL	2002 Winter	2002	Winter	Salt Lake City	Bobsleigh	Bobsleigh Men's Four	NaN
271116 rows × 15 columns

可以发现，原始数据中有15列，并没有改变原始数据，这是因为参数inplace = False（默认）
若需要改变原始数据，则inplace = True

1.4.2 drop( ) 方法------删除某行

python 复制代码

# 1.删除第0行，不改变原数据
athlete.drop(0,axis=0) 

# 2.查看第一行数据
athlete.head(1)   

# 3.得到的结果为
ID	Name	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
0	1	A Dijiang	24.0	180.0	80.0	China	CHN	1992 Summer	1992	Summer	Barcelona	Basketball	Basketball Men's Basketball	NaN


# 1.删除第0行，改变原数据
athlete.drop(0,axis=0,inplace=True)  

# 2.查看第一行数据
athlete.head(1)  

# 3.得到的结果为
ID	Name	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
1	2	A Lamusi	23.0	170.0	60.0	China	CHN	2012 Summer	2012	Summer	London	Judo	Judo Men's Extra-Lightweight	NaN

1.4.3 insert( )方法------增加一列

insert(loc,column,value,allow_duplicates = False)

|------------------|-------------------------|
| loc | 插入列的索引，第一列是0 |
| column | 赋予新的列的名称 |
| value | 新的列的值，数组的形式 |
| allow_duplicates | 是否允许新的列匹配现有的列名，默认为Flase |

python 复制代码

# 1.第二种方法插入新的一列
athlete['rate'] = athlete['Weight']/athlete['Height']

# 2.查看前两列
athlete.head(2)

1.4.4 concat( )方法------增加一行

concal(objs, axis=0, join='outer,join axes=None, ignore index=False, keys=None, levels=None, names=Noneverify integrity=False,copy=True)

|-------------|-------------------------------------------------------------|
| axis | 拼接轴方向，默认为0，沿行拼接;若为1，沿列拼接 |
| join | 默认外联"outer'，拼接另一轴所有的label，缺失值用NaN填充;内联"inner，只拼接另一轴相同的label |
| join axes | 指定需要拼接的轴的labels，可在join既不内联又不外联的时候使用 |
| gnore index | 对index进行重新排序 |
| keys | 多重索引 |

1.4.6 更改某个值

1.4.7 更改某一列或行

1.5 描述性统计

1.5.1 describe( )方法------用于生成描述性统计数据，软计数据集的集中趋势，分教和行列的分布情况，不包括NaN值

1.5.2 info( )方法 ------显示概要信息

1.5.3 value_counts函数------统计某列中各个值的出现次数

value counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

|-----------|-----------------------------------------------------|
| nommalize | 布尔值,默认=False，如为true，则以百分比的形式显示 |
| sort | 布尔值，默认=True，对结果进行排序 |
| ascending | 布尔值，默认降序False排列 |
| bins | intege,格式(bins=1),意义不是执行计算，而是把它们分成半开放的数据出合，只适用于数字数据 |
| dropna | 布尔值,默认=True,默认删除na值 |

1.5.4 mean、max、min单独计算

注意：本文中数据以及内容若有侵权，请第一时间联系删除。

本文是作者个人学习后的总结，未经作者授权，禁止转载，谢谢配合。