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📜  Python Pandas-描述性统计

📅  最后修改于: 2020-11-06 05:39:45             🧑  作者: Mango

大量方法共同在DataFrame上计算描述性统计信息和其他相关操作。其中大多数是聚合，例如sum()，mean()，但是其中一些聚合(例如sumsum())会产生相同大小的对象。一般而言，这些方法采用轴参数，就像ndarray。{sum，std，…}一样，但是轴可以通过名称或整数指定

  • DataFrame- “索引”(轴= 0，默认值)，“列”(轴= 1)

让我们创建一个DataFrame并在本章中使用此对象进行所有操作。

例

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df

其输出如下-

Age  Name   Rating
0   25   Tom     4.23
1   26   James   3.24
2   25   Ricky   3.98
3   23   Vin     2.56
4   30   Steve   3.20
5   29   Smith   4.60
6   23   Jack    3.80
7   34   Lee     3.78
8   40   David   2.98
9   30   Gasper  4.80
10  51   Betina  4.10
11  46   Andres  3.65

和()

返回所请求轴的值之和。默认情况下，轴为索引(轴= 0)。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df.sum()

其输出如下-

Age                                                    382
Name     TomJamesRickyVinSteveSmithJackLeeDavidGasperBe...
Rating                                               44.92
dtype: object

每个单独的列都单独添加(附加字符串)。

轴= 1

此语法将给出如下所示的输出。

现场演示
import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
 
#Create a DataFrame
df = pd.DataFrame(d)
print df.sum(1)

其输出如下-

0    29.23
1    29.24
2    28.98
3    25.56
4    33.20
5    33.60
6    26.80
7    37.78
8    42.98
9    34.80
10   55.10
11   49.65
dtype: float64

意思()

返回平均值

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df.mean()

其输出如下-

Age       31.833333
Rating     3.743333
dtype: float64

std()

返回数值列的Bressel标准偏差。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df.std()

其输出如下-

Age       9.232682
Rating    0.661628
dtype: float64

功能与说明

现在让我们了解Python Pandas中描述性统计信息下的功能。下表列出了重要功能-

Sr.No. Function Description
1 count() Number of non-null observations
2 sum() Sum of values
3 mean() Mean of Values
4 median() Median of Values
5 mode() Mode of values
6 std() Standard Deviation of the Values
7 min() Minimum Value
8 max() Maximum Value
9 abs() Absolute Value
10 prod() Product of Values
11 cumsum() Cumulative Sum
12 cumprod() Cumulative Product

注-由于DataFrame是异构数据结构。泛型运算并不适用于所有功能。

  • 诸如sum()，cumsum()之类的函数可用于数字和字符(或)字符串数据元素，而不会出现任何错误。虽然ñ实践，字符集合从不普遍使用，这些功能不抛出任何异常。

  • 当DataFrame包含字符或字符串数据时，诸如abs()，cumprod()之类的函数将引发异常，因为此类操作无法执行。

汇总数据

describe()函数计算与DataFrame列有关的统计信息摘要。

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df.describe()

其输出如下-

Age         Rating
count    12.000000      12.000000
mean     31.833333       3.743333
std       9.232682       0.661628
min      23.000000       2.560000
25%      25.000000       3.230000
50%      29.500000       3.790000
75%      35.500000       4.132500
max      51.000000       4.800000

此函数提供平均值，std和IQR值。并且，函数不包括字符列和有关数字列的给定摘要。 “ include”是用于传递有关汇总时需要考虑哪些列的必要信息的参数。取值列表；默认情况下为“数字”。

  • 对象-汇总字符串列
  • 数字-汇总数字列
  • 所有-总结所有列在一起(不应该把它作为一个列表值)

现在，在程序中使用以下语句并检查输出-

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df.describe(include=['object'])

其输出如下-

Name
count       12
unique      12
top      Ricky
freq         1

现在，使用以下语句并检查输出-

现场演示
import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}

#Create a DataFrame
df = pd.DataFrame(d)
print df. describe(include='all')

其输出如下-

Age          Name       Rating
count   12.000000        12    12.000000
unique        NaN        12          NaN
top           NaN     Ricky          NaN
freq          NaN         1          NaN
mean    31.833333       NaN     3.743333
std      9.232682       NaN     0.661628
min     23.000000       NaN     2.560000
25%     25.000000       NaN     3.230000
50%     29.500000       NaN     3.790000
75%     35.500000       NaN     4.132500
max     51.000000       NaN     4.800000