单变量频数统计&多变量分组统计中的相关方法~
1. count&unique&nunique
import pandas as
test_data = pd.DataFrame({
'x1': ["a", "b", "c", "b"],
"x2": [1, 2, 3, 4],
"x3": [4, 3, 2, 1]
})
x1 | x2 | x3 | |
0 | a | 1 | 4 |
1 | b | 2 | 3 |
2 | c | 3 | 2 |
3 | b | 4 | 1 |
1.1 统计个数count
## 统计个数
test_data.x1.count()
4
1.2 统计不重复值个数nunique
## 统计不重复的个数
test_data.x1.nunique()
3
1.3 筛选不重复值
## 得到不重复的值
## 返回结果是array
test_data.x1.unique()
array(['a', 'b', 'c'], dtype=object)
1.4 统计某一个值的频数
不同于列表,可以直接统计某个值出现的次数,DataFrame需要做一些转换。
list(test_data.x1).count('b')
2
sum(test_data.x1.apply(lambda x: 1 if x=='b' else 0))
2
test_data.x1.apply(lambda x: 1 if x=='b' else 0).sum()
2
2. 分组统计—groupby
groupby有一点奇葩,分组之后,label都变成索引(行名了),可以设置as_index=False改变默认参数。
文档地址:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html
import pandas as
x = pd.DataFrame({
"x1": ["a", "a", "b", "b", 'c'],
"x2": [1, 1, 1, 2, 2],
"x3": [1, 2, 3, 4, 5]
})
x
x1 | x2 | x3 | |
0 | a | 1 | 1 |
1 | a | 1 | 2 |
2 | b | 1 | 3 |
3 | b | 2 | 4 |
4 | c | 2 | 5 |
2.1 分组统计count(*)
# 分组统计各个列的个数
x.groupby(by='x1').count()
x2 | x3 | |
x1 | ||
a | 2 | 2 |
b | 2 | 2 |
c | 1 | 1 |
x.groupby(by=['x1', 'x2'], as_index=False).count()
x1 | x2 | x3 | |
0 | a | 1 | 2 |
1 | b | 1 | 1 |
2 | b | 2 | 1 |
3 | c | 2 | 1 |
# 这里没有分各个列。
x.groupby(by='x1').size()
x1
a 2
b 2
c 1
dtype: int64
2.2 分组统计count(distinct col1)
# 类似于sql:select x1,count(distinct x1),count(distinct x2),count(distinct x3) from table group by x1
x.groupby(by='x1').nunique()
x1 | x2 | x3 | |
x1 | |||
a | 1 | 1 | 2 |
b | 1 | 2 | 2 |
c | 1 | 1 | 1 |
2.4 其余统计函数
x.groupby(by=["x1",'x2']).mean()
x3 | ||
x1 | x2 | |
a | 1 | 1.5 |
b | 1 | 3.0 |
2 | 4.0 | |
c | 2 | 5.0 |
x.groupby(by=["x1",'x2']).sum()
x3 | ||
x1 | x2 | |
a | 1 | 3 |
b | 1 | 3 |
2 | 4 | |
c | 2 | 5 |
x.groupby(by=["x1",'x2'], as_index=False).aggregate(sum)
x1 | x2 | x3 | |
0 | a | 1 | 3 |
1 | b | 1 | 3 |
2 | b | 2 | 4 |
3 | c | 2 | 5 |
2.5 整体的描述统计
x.groupby(by=["x1",'x2'], as_index=True).describe()
x3 | |||||||||
count | mean | std | min | 25% | 50% | 75% | max | ||
x1 | x2 | ||||||||
a | 1 | 2.0 | 1.5 | 0.707107 | 1.0 | 1.25 | 1.5 | 1.75 | 2.0 |
b | 1 | 1.0 | 3.0 | NaN | 3.0 | 3.00 | 3.0 | 3.00 | 3.0 |
2 | 1.0 | 4.0 | NaN | 4.0 | 4.00 | 4.0 | 4.00 | 4.0 | |
c | 2 | 1.0 | 5.0 | NaN | 5.0 | 5.00 | 5.0 | 5.00 | 5.0 |
x.groupby(by=["x1",'x2'], as_index=False).describe()
x2 | x3 | |||||||||||||||
count | mean | std | min | 25% | 50% | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | |
0 | 2.0 | 1.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.5 | 0.707107 | 1.0 | 1.25 | 1.5 | 1.75 | 2.0 |
1 | 1.0 | 1.0 | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | NaN | 3.0 | 3.00 | 3.0 | 3.00 | 3.0 |
2 | 1.0 | 2.0 | NaN | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 4.0 | NaN | 4.0 | 4.00 | 4.0 | 4.00 | 4.0 |
3 | 1.0 | 2.0 | NaN | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 5.0 | NaN | 5.0 | 5.00 | 5.0 | 5.00 | 5.0 |
2018-10-13 于南京市栖霞区紫东创业园