描述性统计
06 Jan 2018 | free time描述性统计
1. sum(),mean(),std(),…
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() # by default,axis is index(axis=0)
print df.sum(1),'\n'
print df.mean(),'\n'
print df.std()
Age 382
Name TomJamesRickyVinSteveSmithJackLeeDavidGasperBe...
Rating 44.92
dtype: object
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
Age 31.833333
Rating 3.743333
dtype: float64
Age 9.232682
Rating 0.661628
dtype: float64
2. 函数及描述汇总
| No. | 函数 | 描述 |
|---|---|---|
| 1 | count() | 非空观测值的数量 |
| 2 | sum() | 值的总和 |
| 3 | mean() | 值的均值 |
| 4 | median() | 值的中位数 |
| 5 | mode() | 值的取模 |
| 6 | std() | 值的标准差 |
| 7 | min() | 最小值 |
| 8 | max() | 最大值 |
| 9 | abs() | 绝对值 |
| 10 | prod() | 值的乘积 |
| 11 | cumsum() | 累积和 |
| 12 | cumprod() | 累计乘 |
总结性数据describe()
describe()函数计算与DataFrame列有关的统计信息的摘要
import pandas as pd
import numpy as np
# create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','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,'\n'
print df.describe()
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
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
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