Python学习笔记：描述性统计describe

一、介绍

data.describe() 即可很方便的输出数据的统计信息。

但还有更详细的使用方法：

DataFrame.descirbe(percentiles=[0.1,0.2,0.5,0.75],
                  include=None,
                  exclude=None)

参数解释：

percentiles -- 0-1之间的数字，以返回各自的百分位数
include -- 包含的数据类型
exclude -- 剔除的数据类型

二、实操

默认统计量

import pandas as pd
import numpy as np

series = pd.Series(np.random.randn(100))
series.describe()
'''
count    100.000000  计数
mean      -0.049944  均值
std        0.967943  标准差
min       -2.692278  最小值
25%       -0.717809  25%分位数
50%       -0.061116  中位数
75%        0.682023  75%分位数
max        1.825730  最大值
dtype: float64
'''

percentiles参数

series.describe(percentiles=[0.05,0.25,0.3,0.7,0.8])
'''
count    100.000000
mean      -0.049944
std        0.967943
min       -2.692278
5%        -1.617615
25%       -0.717809
30%       -0.574646
50%       -0.061116
70%        0.543954
80%        0.776378
max        1.825730
dtype: float64
'''

include参数

df = pd.DataFrame({"class":["语文","语文","语文","语文","语文","数学","数学","数学","数学","数学"],
                  "name":["小明","小苏","小周","小孙","小王","小明","小苏","小周","小孙","小王"],
                  "score":[137,125,125,115,115,80,111,130,130,140]})
df

# 默认输出数值型特征的统计量
df.describe()
df.descirbe(include=[np.number])
'''
            score
count   10.000000
mean   120.800000
std     17.203359
min     80.000000
25%    115.000000
50%    125.000000
75%    130.000000
max    140.000000
'''

# 计算离散型变量的统计特征
df.describe(include=['O'])
df.describe(include=[object])
'''
       class name
count     10   10      非空计数
unique     2    5      唯一值
top       数学   小孙   出现最频繁
freq       5    2      频次
'''

# all 输出全部特征
df.describe(include='all')
'''
       class name       score
count     10   10   10.000000
unique     2    5         NaN
top       数学   小孙         NaN
freq       5    2         NaN
mean     NaN  NaN  120.800000
std      NaN  NaN   17.203359
min      NaN  NaN   80.000000
25%      NaN  NaN  115.000000
50%      NaN  NaN  125.000000
75%      NaN  NaN  130.000000
max      NaN  NaN  140.000000
'''

exclude参数

# 剔除统计类型
df.describe(exclude='O')
'''
            score
count   10.000000
mean   120.800000
std     17.203359
min     80.000000
25%    115.000000
50%    125.000000
75%    130.000000
max    140.000000
'''