Pandas-数据操作-数值型（一）：统计函数【describe、count、sum、mean、median、max、std、skew（偏度）、kurt（峰度）】【参数：axis、skipna】

清疚 2023-09-29 09:38 51阅读 0赞

综合分析: 能够直接得出很多统计结果,`count`, `mean`, `std`, `min`, `max` 等

# 计算平均值、标准差、最大值、最小值
    data.describe()

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## 一、统计函数 ##

<table> 
 <thead> 
  <tr> 
   <th>统计函数</th> 
   <th>描述</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td><code>count</code></td> 
   <td>Number of non-NA observations</td> 
  </tr> 
  <tr> 
   <td><code>sum</code></td> 
   <td><strong>Sum of values</strong></td> 
  </tr> 
  <tr> 
   <td><code>mean</code></td> 
   <td><strong>Mean of values</strong></td> 
  </tr> 
  <tr> 
   <td><code>median</code></td> 
   <td>Arithmetic median of values</td> 
  </tr> 
  <tr> 
   <td><code>min</code></td> 
   <td><strong>Minimum</strong></td> 
  </tr> 
  <tr> 
   <td><code>max</code></td> 
   <td><strong>Maximum</strong></td> 
  </tr> 
  <tr> 
   <td><code>mode</code></td> 
   <td>Mode</td> 
  </tr> 
  <tr> 
   <td><code>abs</code></td> 
   <td>Absolute Value</td> 
  </tr> 
  <tr> 
   <td><code>prod</code></td> 
   <td>Product of values</td> 
  </tr> 
  <tr> 
   <td><code>std</code></td> 
   <td><strong>Bessel-corrected sample standard deviation</strong></td> 
  </tr> 
  <tr> 
   <td><code>var</code></td> 
   <td><strong>Unbiased variance</strong></td> 
  </tr> 
  <tr> 
   <td><code>idxmax</code></td> 
   <td>compute the index labels with the maximum</td> 
  </tr> 
  <tr> 
   <td><code>idxmin</code></td> 
   <td>compute the index labels with the minimum</td> 
  </tr> 
 </tbody> 
</table>

**对于单个函数去进行统计的时候，坐标轴还是按照默认列“columns” (axis=0, default)，如果要对行“index” 需要指定(axis=1)**

### 1、Series ###

import numpy as np
    import pandas as pd
    
    # 主要数学计算方法，可用于Series和DataFrame
    
    s = pd.Series(np.random.rand(10) * 10)
    print("s = \n", s)
    print('-' * 200)
    
    # count统计非Na值的数量
    print("count统计非Na值的数量: s.count() = ", s.count())
    print('-' * 100)
    # min统计最小值
    print("min统计最小值: s.min() = ", s.min())
    print('-' * 100)
    # max统计最大值
    print("max统计最大值: s.max() = ", s.max())
    print('-' * 100)
    # quantile统计分位数，参数q确定位置
    print("quantile统计分位数，参数q确定位置: s.quantile(q=0.75) = ", s.quantile(q=0.75))
    print('-' * 100)
    # sum求和
    print("sum求和: s.sum() = ", s.sum())
    print('-' * 100)
    # mean求平均值
    print("mean求平均值: s.mean() = ", s.mean())
    print('-' * 100)
    # median求算数中位数，50%分位数
    print("median求算数中位数，50%分位数: s.median() = ", s.median())
    print('-' * 100)
    # std求标准差
    print("std求标准差: s.std() = ", s.std())
    print('-' * 100)
    # var求方差
    print("var求方差: s.var() = ", s.var())
    print('-' * 100)
    # skew样本的偏度
    print("skew样本的偏度: s.skew() = ", s.skew())
    print('-' * 100)
    # kurt样本的峰度
    print("kurt样本的峰度: s.kurt() = ", s.kurt())
    print('-' * 200)

打印结果：

s = 
     0    1.268546
    1    6.385458
    2    9.744136
    3    0.192000
    4    5.736461
    5    8.635534
    6    2.635359
    7    3.042843
    8    2.880422
    9    5.899908
    dtype: float64
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    count统计非Na值的数量: s.count() =  10
    ----------------------------------------------------------------------------------------------------
    min统计最小值: s.min() =  0.19200024578894803
    ----------------------------------------------------------------------------------------------------
    max统计最大值: s.max() =  9.744136205123855
    ----------------------------------------------------------------------------------------------------
    quantile统计分位数，参数q确定位置: s.quantile(q=0.75) =  6.264070647567895
    ----------------------------------------------------------------------------------------------------
    sum求和: s.sum() =  46.42066667909582
    ----------------------------------------------------------------------------------------------------
    mean求平均值: s.mean() =  4.642066667909582
    ----------------------------------------------------------------------------------------------------
    median求算数中位数，50%分位数: s.median() =  4.3896517346977975
    ----------------------------------------------------------------------------------------------------
    std求标准差: s.std() =  3.138819536014843
    ----------------------------------------------------------------------------------------------------
    var求方差: s.var() =  9.852188079668435
    ----------------------------------------------------------------------------------------------------
    skew样本的偏度: s.skew() =  0.2737130849268303
    ----------------------------------------------------------------------------------------------------
    kurt样本的峰度: s.kurt() =  -0.9683145338195316
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

### 2、DataFrame ###

DataFrame：统计函数是对每一列的数据进行统计运算。

import numpy as np
    import pandas as pd
    
    # 主要数学计算方法，可用于Series和DataFrame（1）
    
    df = pd.DataFrame({
        'key1': np.arange(10),
                       'key2': np.random.rand(10) * 10})
    print("df = \n", df)
    print('-' * 200)
    
    # count统计非Na值的数量
    print("count统计非Na值的数量: df.count() = \n", df.count())
    print('-' * 50)
    # min统计最小值
    print("min统计最小值: df.min() = \n", df.min())
    print('-' * 50)
    # max统计最大值
    print("max统计最大值: df.max() = \n", df.max())
    print('-' * 50)
    # quantile统计分位数，参数q确定位置
    print("quantile统计分位数，参数q确定位置: df.quantile(q=0.75) = \n", df.quantile(q=0.75))
    print('-' * 50)
    # sum求和
    print("sum求和: df.sum() = \n", df.sum())
    print('-' * 50)
    # mean求平均值
    print("mean求平均值: df.mean() = \n", df.mean())
    print('-' * 50)
    # median求算数中位数，50%分位数
    print("median求算数中位数，50%分位数: df.median() = \n", df.median())
    print('-' * 50)
    # std求标准差
    print("std求标准差: df.std() = \n", df.std())
    print('-' * 50)
    # var求方差
    print("var求方差: df.var() = \n", df.var())
    print('-' * 50)
    # skew样本的偏度
    print("skew样本的偏度: df.skew() = \n", df.skew())
    print('-' * 50)
    # kurt样本的峰度
    print("kurt样本的峰度: df.kurt() = \n", df.kurt())
    print('-' * 200)

打印结果：

df = 
        key1      key2
    0     0  9.132186
    1     1  2.324223
    2     2  0.151036
    3     3  3.802248
    4     4  6.358117
    5     5  1.051407
    6     6  5.322711
    7     7  9.895836
    8     8  9.511710
    9     9  9.260366
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    count统计非Na值的数量: df.count() = 
    key1    10
    key2    10
    dtype: int64
    --------------------------------------------------
    min统计最小值: df.min() = 
    key1    0.000000
    key2    0.151036
    dtype: float64
    --------------------------------------------------
    max统计最大值: df.max() = 
    key1    9.000000
    key2    9.895836
    dtype: float64
    --------------------------------------------------
    quantile统计分位数，参数q确定位置: df.quantile(q=0.75) = 
    key1    6.750000
    key2    9.228321
    Name: 0.75, dtype: float64
    --------------------------------------------------
    sum求和: df.sum() = 
    key1    45.000000
    key2    56.809838
    dtype: float64
    --------------------------------------------------
    mean求平均值: df.mean() = 
    key1    4.500000
    key2    5.680984
    dtype: float64
    --------------------------------------------------
    median求算数中位数，50%分位数: df.median() = 
    key1    4.500000
    key2    5.840414
    dtype: float64
    --------------------------------------------------
    std求标准差: df.std() = 
    key1    3.027650
    key2    3.720213
    dtype: float64
    --------------------------------------------------
    var求方差: df.var() = 
    key1     9.166667
    key2    13.839987
    dtype: float64
    --------------------------------------------------
    skew样本的偏度: df.skew() = 
    key1    0.000000
    key2   -0.254827
    dtype: float64
    --------------------------------------------------
    kurt样本的峰度: df.kurt() = 
    key1   -1.200000
    key2   -1.637533
    dtype: float64
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

## 二、参数：axis、skipna ##

import numpy as np
    import pandas as pd
    
    # 基本参数：axis、skipna
    
    import numpy as np
    import pandas as pd
    
    # np.nan ：空值
    df = pd.DataFrame({
        'key1': [4, 5, 3, 1, 2],
                       'key2': [1, 2, np.nan, 4, 5],
                       'key3': [1, 2, 3, 'j', 'k']},
                      index=['a', 'b', 'c', 'd', 'e'])
    
    print("df = \n", df)
    print('-' * 50)
    print("df['key1'].dtype = {0} \ndf['key2'].dtype = {1} \ndf['key3'].dtype = {2}".format(df['key1'].dtype, df['key2'].dtype, df['key3'].dtype))
    print('-' * 200)
    
    # .mean()计算均值【只统计数字列】【可以通过索引单独统计一列】
    m1 = df.mean()
    print("以列来统计(默认以列，默认忽略NaN): \nm1 = \n{0} \ntype(m1) = {1}".format(m1, type(m1)))
    print('-' * 50)
    
    # skipna参数：是否忽略NaN【默认True】，如False，有NaN的列统计结果仍为NaN
    m2 = df.mean(skipna=False)
    print("以列来统计(不忽略NaN): m2 = \n{0} \ntype(m2) = {1}".format(m2, type(m2)))
    print('-' * 200)
    
    # axis参数：默认为0，以列来计算，axis=1，以行来计算，这里就按照行来汇总了
    m3 = df.mean(axis=1)
    print("以行来统计: \nm3 = \n{0} \ntype(m3) = {1}".format(m3, type(m3)))
    print('-' * 200)
    
    # 单独统计一列
    m_key2 = df['key2'].mean()
    print("单独统计一列: m_key2 = df['key2'].mean() = ", m_key2)
    print('-' * 200)

打印结果：

df = 
        key1  key2 key3
    a     4   1.0    1
    b     5   2.0    2
    c     3   NaN    3
    d     1   4.0    j
    e     2   5.0    k
    --------------------------------------------------
    df['key1'].dtype = int64 
    df['key2'].dtype = float64 
    df['key3'].dtype = object
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    以列来统计(默认以列，默认忽略NaN): 
    m1 = 
    key1    3.0
    key2    3.0
    dtype: float64 
    type(m1) = <class 'pandas.core.series.Series'>
    --------------------------------------------------
    以列来统计(不忽略NaN): m2 = 
    key1    3.0
    key2    NaN
    dtype: float64 
    type(m2) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    以行来统计: 
    m3 = 
    a    2.5
    b    3.5
    c    3.0
    d    2.5
    e    3.5
    dtype: float64 
    type(m3) = <class 'pandas.core.series.Series'>
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    单独统计一列: m_key2 = df['key2'].mean() =  3.0
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

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