Pandas 描述性统计,有很多方法用来计算DataFrame
的描述性统计信息和其他相关操作。 其中大多数是sum()
,mean()
等聚合函数,但其中一些,如sumsum()
,产生一个相同大小的对象。 一般来说,这些方法采用轴参数,就像ndarray.{sum,std,...}
,但轴可以通过名称或整数来指定。
- DataFrame – “index”(axis=0,default),”columns”(axis=1)
下面创建一个数据帧(DataFrame),并使用此对象进行演示本章中所有操作。
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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)
执行结果如下:
函数和说明
下面来了解Python Pandas中描述性统计信息的函数,下表列出了重要函数
注 – 由于DataFrame是异构数据结构,泛型操作并不适用于所有函数。
- 类似于:
sum()
,cumsum()
函数能与数字和字符(或)字符串数据元素一起工作,不会产生任何错误。字符聚合从来都比较少被使用,虽然这些函数不会引发任何异常。 - 由于这样的操作无法执行,因此,当DataFrame包含字符或字符串数据时,像
abs()
,cumprod()
这样的函数会抛出异常。
sum() 示例
返回所请求轴的值的总和,默认情况下axis=0
。
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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())
执行结果如下:
Age 382
Name TomJamesRickyVinSteveMinsuJackLeeDavidGasperBe...
Rating 44.92
dtype: object
axis=1示例
此语法将给出如下所示的输出,参考示例代码如下:
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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))
执行结果如下:
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
mean() 示例
返回平均值,参考示例代码如下:
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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())
执行结果如下:
Age 31.833333
Rating 3.743333
dtype: float64
std() 示例
返回数值列的Bressel标准偏差,参考示例代码如下:
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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())
执行结果如下:
Age 9.232682
Rating 0.661628
dtype: float64
describe() 示例
describe()
函数是用来计算有关 DataFrame 列的统计信息的摘要。
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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())
执行结果如下:
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
该函数给出了平均值,标准差和IQR值。 而且,函数排除字符列,并给出关于数字列的摘要。 include
是用于传递关于什么列需要考虑用于总结的必要信息的参数。获取值列表; 默认情况下是”数字值”。
object
– 汇总字符串列number
– 汇总数字列all
– 将所有列汇总在一起(不应将其作为列表值传递)
现在,在程序中使用以下语句并检查输出
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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']))
执行结果如下:
Name
count 12
unique 12
top Gasper
freq 1
现在,使用以下语句并查看输出
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','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'))
执行结果如下:
Age Name Rating
count 12.000000 12 12.000000
unique NaN 12 NaN
top NaN David 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
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