Pandas-数据结构-Series（三）：常用操作【数据查看（head）、排序（sort_values）、重新索引（reindex）、对齐（计算时根据标签自动对齐）、添加元素、修改元素、删除元素】

灰太狼 2023-09-29 09:02 1阅读 0赞

## 一、数据查看 ##

*  .head()查看头部数据
 *  .tail()查看尾部数据
 *  默认查看5条

import numpy as np
    import pandas as pd
    
    s = pd.Series(np.random.rand(50))
    
    print("s.head() = \n", s.head())
    print("-" * 100)
    print("s.head(10) = \n", s.head(10))
    print("-" * 100)
    print("s.tail() = \n", s.tail())

打印结果：

s.head() = 
    0    0.891778
    1    0.575982
    2    0.138742
    3    0.101361
    4    0.247216
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s.head(10) = 
    0    0.891778
    1    0.575982
    2    0.138742
    3    0.101361
    4    0.247216
    5    0.376180
    6    0.117379
    7    0.001082
    8    0.769211
    9    0.204997
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s.tail() = 
    45    0.020636
    46    0.062189
    47    0.110146
    48    0.958667
    49    0.788788
    dtype: float64
    
    Process finished with exit code 0

## 二、排序 ##

*  使用series.sort\_values(ascending=True)进行排序

series排序时，只有一列，不需要参数

data['p_change'].sort_values(ascending=True).head()
    
    2015-09-01   -10.03
    2015-09-14   -10.02
    2016-01-11   -10.02
    2015-07-15   -10.02
    2015-08-26   -10.01
    Name: p_change, dtype: float64

*  使用series.sort\_index()进行排序

与df一致

# 对索引进行排序
    data['p_change'].sort_index().head()
    
    2015-03-02    2.62
    2015-03-03    1.44
    2015-03-04    1.57
    2015-03-05    2.02
    2015-03-06    8.51
    Name: p_change, dtype: float64

## 三、重新索引 ##

.reindex将会根据索引重新排序，如果当前索引不存在，则引入缺失值

*  .reindex()中也是写列表
 *  这里’d’索引不存在，所以值为NaN
 *  fill\_value参数：填充缺失值的值

import numpy as np
    import pandas as pd
    
    # 重新索引reindex
    # .reindex将会根据索引重新排序，如果当前索引不存在，则引入缺失值
    
    s = pd.Series(np.random.rand(3), index=['a', 'b', 'c'])
    print("s = \n", s)
    print("-" * 100)
    
    # .reindex()中也是写列表
    # 这里'd'索引不存在，所以值为NaN
    s1 = s.reindex(['c', 'b', 'a', 'd'])
    print("s1 = \n", s1)
    print("-" * 100)
    
    # fill_value参数：填充缺失值的值
    s2 = s.reindex(['c', 'b', 'a', 'd'], fill_value=0)
    print("s2 = \n", s2)

打印结果：

s = 
    a    0.496666
    b    0.828771
    c    0.363888
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s1 = 
    c    0.363888
    b    0.828771
    a    0.496666
    d         NaN
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s2 = 
    c    0.363888
    b    0.828771
    a    0.496666
    d    0.000000
    dtype: float64
    
    Process finished with exit code 0

## 四、Series对齐（操作会根据标签自动对齐） ##

Series 和 ndarray 之间的主要区别是，Series 上的操作会根据标签自动对齐

*  index顺序不会影响数值计算，以标签来计算
 *  空值和任何值计算结果扔为空值

import numpy as np
    import pandas as pd
    
    # Series对齐
    
    s1 = pd.Series(np.random.rand(3), index = ['Jack','Marry','Tom'])
    s2 = pd.Series(np.random.rand(3), index = ['Wang','Jack','Marry'])
    print("s1 = \n", s1)
    print("s2 = \n", s2)
    print("-" * 100)
    print("s1+s2 = \n", s1+s2)

打印结果：

s1 = 
    Jack     0.965087
    Marry    0.088279
    Tom      0.369567
    dtype: float64
    s2 = 
    Wang     0.398997
    Jack     0.082579
    Marry    0.856640
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s1+s2 = 
    Jack     1.047665
    Marry    0.944919
    Tom           NaN
    Wang          NaN
    dtype: float64
    
    Process finished with exit code 0

## 五、添加元素/数组 ##

直接通过下标索引/标签index添加值

*  通过.append方法，直接添加一个数组
 *  .append方法生成一个新的数组，不改变之前的数组

import numpy as np
    import pandas as pd
    
    # 添加
    
    s1 = pd.Series(np.random.rand(5))
    s2 = pd.Series(np.random.rand(5), index=list('ngjur'))
    print("s1 = \n", s1)
    print("s2 = \n", s2)
    print("-" * 100)
    
    # 直接通过下标索引/标签index添加值
    s1[5] = 100
    s2['a'] = 100
    print("s1 = \n", s1)
    print("s2 = \n", s2)
    print("-" * 100)
    
    s3 = s1.append(s2)
    print("s1 = \n", s1)
    print("s3 = \n", s3)

打印结果：

s1 = 
    0    0.418343
    1    0.611628
    2    0.793579
    3    0.643884
    4    0.062399
    dtype: float64
    s2 = 
     n    0.178642
    g    0.360007
    j    0.287545
    u    0.016724
    r    0.126153
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s1 = 
    0      0.418343
    1      0.611628
    2      0.793579
    3      0.643884
    4      0.062399
    5    100.000000
    dtype: float64
    s2 = 
    n      0.178642
    g      0.360007
    j      0.287545
    u      0.016724
    r      0.126153
    a    100.000000
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s1 = 
    0      0.418343
    1      0.611628
    2      0.793579
    3      0.643884
    4      0.062399
    5    100.000000
    dtype: float64
    s3 = 
    0      0.418343
    1      0.611628
    2      0.793579
    3      0.643884
    4      0.062399
    5    100.000000
    n      0.178642
    g      0.360007
    j      0.287545
    u      0.016724
    r      0.126153
    a    100.000000
    dtype: float64
    
    Process finished with exit code 0

## 六、修改元素 ##

通过索引直接修改，类似序列

import numpy as np
    import pandas as pd
    
    # 修改
    
    s = pd.Series(np.random.rand(3), index=['a', 'b', 'c'])
    print("s = \n", s)
    s['a'] = 100
    s[['b', 'c']] = 200
    print("-" * 100)
    print("s = \n", s)

打印结果：

s = 
    a    0.383475
    b    0.123369
    c    0.911300
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s = 
    a    100.0
    b    200.0
    c    200.0
    dtype: float64
    
    Process finished with exit code 0

## 七、删除值 ##

drop 删除元素之后返回新对象

import numpy as np
    import pandas as pd
    
    # 删除：.drop
    
    s = pd.Series(np.random.rand(5), index=list('ngjur'))
    print("s = \n", s)
    print("-" * 100)
    s1 = s.drop('n')
    s2 = s.drop(['g', 'j'])
    print("s1 = \n", s1)
    print("-" * 50)
    print("s2 = \n", s2)
    print("-" * 50)
    print("s = \n", s)

打印结果

s = 
    n    0.744795
    g    0.345820
    j    0.001573
    u    0.275530
    r    0.046669
    dtype: float64
    ----------------------------------------------------------------------------------------------------
    s1 = 
    g    0.345820
    j    0.001573
    u    0.275530
    r    0.046669
    dtype: float64
    --------------------------------------------------
    s2 = 
    n    0.744795
    u    0.275530
    r    0.046669
    dtype: float64
    --------------------------------------------------
    s = 
    n    0.744795
    g    0.345820
    j    0.001573
    u    0.275530
    r    0.046669
    dtype: float64
    
    Process finished with exit code 0