Pandas 描述性统计操作实例
DataFrame用在大量的计算描述性信息统计和其他相关操作。其中大多数是聚合,例如sum(),mean(),但其中一些聚合(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,但是可以通过名称或整数指定轴 DataFrame − 索引 (axis=0, default), 列 (axis=1)
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
# 创建一个DataFrame
df = pd.DataFrame(d)
print(df)
运行结果:
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
返回所请求轴的值之和。默认情况下,轴为索引(轴=0)
import pandas as pd
import numpy as np
#创建一个Series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.sum())
运行结果:
Age 382
Name TomJamesRickyVinSteveSmithJackLeeDavidGasperBe...
Rating 44.92
dtype: object
每个单独的列都添加了字符串
此语法将输出以下内容。
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.sum(1))
运行结果:
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
返回平均值
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.mean())
运行结果:
Age 31.833333
Rating 3.743333
dtype: float64
返回数值列的Bressel标准偏差。
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.std())
运行结果:
Age 9.232682
Rating 0.661628
dtype: float64
现在我们来了解Python Pandas中描述性统计信息下的功能。下表列出了重要功能:
编号 | 方法 | 描述 |
1 | count() | 非空数 |
2 | sum() | 总数 |
3 | mean() | 平均数 |
4 | median() | 中位数 |
5 | mode() | 模式 |
6 | std() | 标准差 |
7 | min() | 最低值 |
8 | max() | 最大值 |
9 | abs() | 绝对值 |
10 | prod() | 乘积 |
11 | cumsum() | 累加 |
12 | cumprod() | 累乘 |
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.describe())
运行结果:
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”。
object − 汇总字符串列number − 汇总数字列all − 总结所有列在一起(不应该把它作为一个列表值)下面我们在程序中使用以下语句并执行并输出:
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df.describe(include=['object']))
运行结果:
Name
count 12
unique 12
top Ricky
freq 1
下面我们在程序中使用以下语句并执行并输出:
import pandas as pd
import numpy as np
# 创建一个series字典
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])
}
#创建一个DataFrame
df = pd.DataFrame(d)
print(df. describe(include='all'))
运行结果:
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