Pandas-数据操作-常用函数（一）：unique（获取Series中的去重值）、value_counts（值计数、词频）、isin（成员资格）、duplicated（去重）、replace（替换）

迈不过友情╰ 2023-09-29 09:38 22阅读 0赞

## 一、unique()：唯一值 ##

作用：unique()函数用于获取Series对象的唯一值。唯一性按出现顺序返回。基于哈希表的唯一，因此不排序

*  语法：Series.unique(self)
 *  返回：ndarray 或 ExtensionArray作为 NumPy 数组返回的唯一值。
 *  不能用于DataFrame

注意：以 NumPy 数组的形式返回唯一值。 如果是扩展数组支持的系列，则返回该类型的新 ExtensionArray，其中仅包含唯一值。 这包括

*  分类的
 *  时期
 *  带时区的日期时间
 *  间隔
 *  疏
 *  整数NA

import pandas as pd
    
    # 唯一值：.unique()
    
    s = pd.Series(list('asdvasdcfgg'))
    print("s = \n{0} \ntype(s) = {1}".format(s, type(s)))
    print('-' * 200)
    
    # 得到一个唯一值数组
    sq = s.unique()
    print("得到一个唯一值数组: sq = s.unique() = \n{0} \ntype(sq) = {1}".format(sq, type(sq)))
    print('-' * 200)
    
    # 通过pd.Series重新变成新的Series
    s2 = pd.Series(sq)
    print("s2 = \n{0} \ntype(s2) = {1}".format(s2, type(s2)))
    print('-' * 200)
    
    # 重新排序
    sq.sort()
    print("sq重新排序后: sq = \n{0} \ntype(sq) = {1}".format(sq, type(sq)))
    s3 = pd.Series(sq)
    print('-' * 50)
    print("s3 = \n{0} \ntype(s3) = {1}".format(s3, type(s3)))
    print('-' * 200)

打印结果：

s = 
    0     a
    1     s
    2     d
    3     v
    4     a
    5     s
    6     d
    7     c
    8     f
    9     g
    10    g
    dtype: object 
    type(s) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    得到一个唯一值数组: sq = s.unique() = 
    ['a' 's' 'd' 'v' 'c' 'f' 'g'] 
    type(sq) = <class 'numpy.ndarray'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    s2 = 
    0    a
    1    s
    2    d
    3    v
    4    c
    5    f
    6    g
    dtype: object 
    type(s2) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    sq重新排序后: sq = 
    ['a' 'c' 'd' 'f' 'g' 's' 'v'] 
    type(sq) = <class 'numpy.ndarray'>
    --------------------------------------------------
    s3 = 
    0    a
    1    c
    2    d
    3    f
    4    g
    5    s
    6    v
    dtype: object 
    type(s3) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 二、value\_counts()：值计数（eg. 词频） ##

### 1、Series ###

import pandas as pd
    
    # 值计数：.value_counts()
    
    s = pd.Series(list('asdvasdcfgg'))
    print("s = \n{0} \ntype(s) = {1}".format(s, type(s)))
    print('-' * 200)
    
    # 得到一个新的Series，计算出不同值出现的频率
    # sort参数：排序，默认为True
    sc = s.value_counts(sort=False)  # 也可以这样写：pd.value_counts(sc, sort = False)
    print("sc = \n{0} \ntype(sc) = {1}".format(sc, type(sc)))

打印结果：

### 2、DataFrame ###

import numpy as np
    import pandas as pd
    
    # 值计数：.value_counts()
    
    df = pd.DataFrame({
        'key1': ['a', 'a', 3, 4, 5],
                       'key2': ['e', 'a', 'b', 'b', 'c'],
                       'key3': ['d', 'f', 'a', 3, 5]})
    print("df = \n{0} \ntype(df) = {1}".format(df, type(df)))
    print('-' * 200)
    
    # 得到一个新的DataFrame，计算出不同值出现的频率
    # sort参数：排序，默认为True
    sc = df.value_counts(sort = False)  # 也可以这样写：pd.value_counts(sc, sort = False)
    print("sc = \n{0} \ntype(sc) = {1}".format(sc, type(sc)))

打印结果：

df = 
      key1 key2 key3
    0    a    e    d
    1    a    a    f
    2    3    b    a
    3    4    b    3
    4    5    c    5 
    type(df) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    sc = 
    key1  key2  key3
    3     b     a       1
    4     b     3       1
    5     c     5       1
    a     a     f       1
          e     d       1
    dtype: int64 
    type(sc) = <class 'pandas.core.series.Series'>
    
    Process finished with exit code 0

## 三、isin()：成员资格 ##

import numpy as np
    import pandas as pd
    
    # 成员资格：.isin()
    # 用[]表示
    # 得到一个布尔值的Series或者Dataframe
    
    s = pd.Series(np.arange(10, 15))
    print("s = \n{0} \ntype(s) = {1}".format(s, type(s)))
    print('-' * 50)
    print("s.isin([5, 14]) = \n", s.isin([5, 14]))
    print('-' * 200)
    
    df = pd.DataFrame({
        'key1': list('asdcbvasd'),
                       'key2': np.arange(4, 13)})
    print("df = \n{0} \ntype(df) = {1}".format(df, type(df)))
    print('-' * 50)
    print("df.isin(['a', 'bc', '10', 8]) = \n", df.isin(['a', 'bc', '10', 8]))
    print('-' * 200)

打印结果：

s = 
    0    10
    1    11
    2    12
    3    13
    4    14
    dtype: int32 
    type(s) = <class 'pandas.core.series.Series'>
    --------------------------------------------------
    s.isin([5, 14]) = 
     0    False
    1    False
    2    False
    3    False
    4     True
    dtype: bool
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df = 
      key1  key2
    0    a     4
    1    s     5
    2    d     6
    3    c     7
    4    b     8
    5    v     9
    6    a    10
    7    s    11
    8    d    12 
    type(df) = <class 'pandas.core.frame.DataFrame'>
    --------------------------------------------------
    df.isin(['a', 'bc', '10', 8]) = 
         key1   key2
    0   True  False
    1  False  False
    2  False  False
    3  False  False
    4  False   True
    5  False  False
    6   True  False
    7  False  False
    8  False  False
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 四、duplicated：去重 ##

import pandas as pd
    
    # 去重 .duplicated
    
    # Series中使用duplicated
    s = pd.Series([1, 1, 1, 1, 2, 2, 2, 3, 4, 5, 5, 5, 5])
    # 判断是否重复
    data1 = s.duplicated()
    print("data1 = s.duplicated() = \n", data1)
    print('-' * 50)
    # 通过布尔判断，得到不重复的值
    print("s[s.duplicated() == False] = \n", s[s.duplicated() == False])
    print('-' * 200)
    
    # drop.duplicates移除重复
    # inplace参数：是否替换原值，默认False
    s_re = s.drop_duplicates()
    print("s_re = s.drop_duplicates() = \n{0}".format(s_re))
    print('-' * 200)
    
    # Dataframe中使用duplicated
    df = pd.DataFrame({
        'key1': ['a', 'a', 3, 4, 5],
                       'key2': ['a', 'a', 'b', 'b', 'c']})
    
    print("df.duplicated() = \n", df.duplicated())
    print('-' * 50)
    print("df['key2'].duplicated() = \n", df['key2'].duplicated())

打印结果：

data1 = s.duplicated() = 
     0     False
    1      True
    2      True
    3      True
    4     False
    5      True
    6      True
    7     False
    8     False
    9     False
    10     True
    11     True
    12     True
    dtype: bool
    --------------------------------------------------
    s[s.duplicated() == False] = 
     0    1
    4    2
    7    3
    8    4
    9    5
    dtype: int64
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    s_re = s.drop_duplicates() = 
    0    1
    4    2
    7    3
    8    4
    9    5
    dtype: int64
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    df.duplicated() = 
     0    False
    1     True
    2    False
    3    False
    4    False
    dtype: bool
    --------------------------------------------------
    df['key2'].duplicated() = 
     0    False
    1     True
    2    False
    3     True
    4    False
    Name: key2, dtype: bool
    
    Process finished with exit code 0

## 五、replace：替换 ##

import numpy as np
    import pandas as pd
    
    # 替换 .replace
    # 可一次性替换一个值或多个值
    # 可传入列表或字典
    
    s = pd.Series(list('ascaazsd'))
    print("s = \n", s)
    print('-' * 200)
    data1 = s.replace('a', np.nan)
    print("data1 = s.replace('a', np.nan) = \n", data1)
    print('-' * 200)
    data2 = s.replace(['a', 's'], np.nan)
    print("data2 = s.replace(['a', 's'], np.nan) = \n", data2)
    print('-' * 200)
    
    data3 = s.replace({
        'a': 'hello world!', 's': 123})
    print("data3 = s.replace({'a': 'hello world!', 's': 123}) = \n", data3)

打印结果：

s = 
     0    a
    1    s
    2    c
    3    a
    4    a
    5    z
    6    s
    7    d
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data1 = s.replace('a', np.nan) = 
     0    NaN
    1      s
    2      c
    3    NaN
    4    NaN
    5      z
    6      s
    7      d
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data2 = s.replace(['a', 's'], np.nan) = 
     0    NaN
    1    NaN
    2      c
    3    NaN
    4    NaN
    5      z
    6    NaN
    7      d
    dtype: object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    data3 = s.replace({
        'a': 'hello world!', 's': 123}) = 
     0    hello world!
    1             123
    2               c
    3    hello world!
    4    hello world!
    5               z
    6             123
    7               d
    dtype: object
    
    Process finished with exit code 0

--------------------

参考资料：  
[python | pandas：unique函数][python _ pandas_unique]

[python _ pandas_unique]: https://blog.csdn.net/weixin_48419914/article/details/121635937