Pandas系列学习教程(七）

Pandas系列教程（七）

  对于想要入门数据科学的朋友们来说，Python是一个很好的选择，除了因为简单的语法外，Python 生态中提供了很多在数值计算方面非常优秀的库，其中Pandas不可不提，Pandas是很强大是数据集处理工具，往往和numpy, matplotlib 等库搭配使用，我也是刚刚开始学习Pandas,　顺便翻译了一下官方的Pandas教程,　这里使用的是jupyter notebook, 因为博客不支持html直接编辑，所以只能转化为markdown 格式，如果想直接查看html版本可点击每一节下的链接。本文仅供学习和交流使用，欢迎大家交流和指正！

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摘要

• groupby()函数的使用
• percentile 分位点的使用
• mean(),std()等统计函数

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import pandas as pd
import sys

print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)

Python version 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56) 
[GCC 7.2.0]
Pandas version 0.23.0

# 以日期为index创建
States = ['NY', 'NY', 'NY', 'NY', 'FL', 'FL', 'GA', 'GA', 'FL', 'FL'] 
data = [1.0, 2, 3, 4, 5, 6, 7, 8, 9, 10]
idx = pd.date_range('1/1/2012', periods=10, freq='MS')
df1 = pd.DataFrame(data, index=idx, columns=['Revenue'])
df1['State'] = States

# 创建第二个数据帧
data2 = [10.0, 10.0, 9, 9, 8, 8, 7, 7, 6, 6]
idx2 = pd.date_range('1/1/2013', periods=10, freq='MS')
df2 = pd.DataFrame(data2, index=idx2, columns=['Revenue'])
df2['State'] = States

# 用pd.concat()函数拼接数据帧
df = pd.concat([df1,df2])

df

	Revenue	State
2012-01-01	1.0	NY
2012-02-01	2.0	NY
2012-03-01	3.0	NY
2012-04-01	4.0	NY
2012-05-01	5.0	FL
2012-06-01	6.0	FL
2012-07-01	7.0	GA
2012-08-01	8.0	GA
2012-09-01	9.0	FL
2012-10-01	10.0	FL
2013-01-01	10.0	NY
2013-02-01	10.0	NY
2013-03-01	9.0	NY
2013-04-01	9.0	NY
2013-05-01	8.0	FL
2013-06-01	8.0	FL
2013-07-01	7.0	GA
2013-08-01	7.0	GA
2013-09-01	6.0	FL
2013-10-01	6.0	FL

# 方法一

# 获得一份原数据帧的拷贝，避免丢失数据
newdf = df.copy()

newdf['x-Mean'] = abs(newdf['Revenue'] - newdf['Revenue'].mean())
newdf['1.96*std'] = 1.96*newdf['Revenue'].std()
newdf['Outlier'] = abs(newdf['Revenue'] - newdf['Revenue'].mean()) > newdf['1.96*std']

newdf

	Revenue	State	x-Mean	1.96*std	Outlier
2012-01-01	1.0	NY	5.75	5.200273	True
2012-02-01	2.0	NY	4.75	5.200273	False
2012-03-01	3.0	NY	3.75	5.200273	False
2012-04-01	4.0	NY	2.75	5.200273	False
2012-05-01	5.0	FL	1.75	5.200273	False
2012-06-01	6.0	FL	0.75	5.200273	False
2012-07-01	7.0	GA	0.25	5.200273	False
2012-08-01	8.0	GA	1.25	5.200273	False
2012-09-01	9.0	FL	2.25	5.200273	False
2012-10-01	10.0	FL	3.25	5.200273	False
2013-01-01	10.0	NY	3.25	5.200273	False
2013-02-01	10.0	NY	3.25	5.200273	False
2013-03-01	9.0	NY	2.25	5.200273	False
2013-04-01	9.0	NY	2.25	5.200273	False
2013-05-01	8.0	FL	1.25	5.200273	False
2013-06-01	8.0	FL	1.25	5.200273	False
2013-07-01	7.0	GA	0.25	5.200273	False
2013-08-01	7.0	GA	0.25	5.200273	False
2013-09-01	6.0	FL	0.75	5.200273	False
2013-10-01	6.0	FL	0.75	5.200273	False

# 方法二

newdf1 = df.copy()

State = newdf1.groupby('State')

# 对State进行transform 则得到的结果中state已经排好序了，lambda表达式得到的x
# 是Revenue的值
newdf1['x-Mean'] = State.transform(lambda x: abs(x - x.mean()))
newdf1['1.96*std'] = State.transform(lambda x:1.96*x.std())
newdf1['Outlier'] = State.transform(lambda x: abs(x - x.mean()) > 1.96*x.std())

newdf1

	Revenue	State	x-Mean	1.96*std	Outlier
2012-01-01	1.0	NY	5.00	7.554813	False
2012-02-01	2.0	NY	4.00	7.554813	False
2012-03-01	3.0	NY	3.00	7.554813	False
2012-04-01	4.0	NY	2.00	7.554813	False
2012-05-01	5.0	FL	2.25	3.434996	False
2012-06-01	6.0	FL	1.25	3.434996	False
2012-07-01	7.0	GA	0.25	0.980000	False
2012-08-01	8.0	GA	0.75	0.980000	False
2012-09-01	9.0	FL	1.75	3.434996	False
2012-10-01	10.0	FL	2.75	3.434996	False
2013-01-01	10.0	NY	4.00	7.554813	False
2013-02-01	10.0	NY	4.00	7.554813	False
2013-03-01	9.0	NY	3.00	7.554813	False
2013-04-01	9.0	NY	3.00	7.554813	False
2013-05-01	8.0	FL	0.75	3.434996	False
2013-06-01	8.0	FL	0.75	3.434996	False
2013-07-01	7.0	GA	0.25	0.980000	False
2013-08-01	7.0	GA	0.25	0.980000	False
2013-09-01	6.0	FL	1.25	3.434996	False
2013-10-01	6.0	FL	1.25	3.434996	False

"""
方法二对多个关键字分组
这种分组方式统计的revenue就是在计算均值的时候计入每年状态相同的月份
"""


# 拷贝数据帧
newdf = df.copy()

StateMonth = newdf.groupby(['State', lambda x: x.month])

newdf['Outlier'] = StateMonth.transform( lambda x: abs(x-x.mean()) > 1.96*x.std() )
newdf['x-Mean'] = StateMonth.transform( lambda x: abs(x-x.mean()) )
newdf['1.96*std'] = StateMonth.transform( lambda x: 1.96*x.std() )
newdf

	Revenue	State	Outlier	x-Mean	1.96*std
2012-01-01	1.0	NY	False	4.5	12.473364
2012-02-01	2.0	NY	False	4.0	11.087434
2012-03-01	3.0	NY	False	3.0	8.315576
2012-04-01	4.0	NY	False	2.5	6.929646
2012-05-01	5.0	FL	False	1.5	4.157788
2012-06-01	6.0	FL	False	1.0	2.771859
2012-07-01	7.0	GA	False	0.0	0.000000
2012-08-01	8.0	GA	False	0.5	1.385929
2012-09-01	9.0	FL	False	1.5	4.157788
2012-10-01	10.0	FL	False	2.0	5.543717
2013-01-01	10.0	NY	False	4.5	12.473364
2013-02-01	10.0	NY	False	4.0	11.087434
2013-03-01	9.0	NY	False	3.0	8.315576
2013-04-01	9.0	NY	False	2.5	6.929646
2013-05-01	8.0	FL	False	1.5	4.157788
2013-06-01	8.0	FL	False	1.0	2.771859
2013-07-01	7.0	GA	False	0.0	0.000000
2013-08-01	7.0	GA	False	0.5	1.385929
2013-09-01	6.0	FL	False	1.5	4.157788
2013-10-01	6.0	FL	False	2.0	5.543717

# 方法三

# 拷贝原始数据帧
newdf = df.copy()

State = newdf.groupby('State')

def s(group):
    group['x-Mean'] = abs(group['Revenue'] - group['Revenue'].mean())
    group['1.96*std'] = 1.96*group['Revenue'].std()  
    group['Outlier'] = abs(group['Revenue'] - group['Revenue'].mean()) > 1.96*group['Revenue'].std()
    return group

Newdf2 = State.apply(s)
Newdf2

	Revenue	State	x-Mean	1.96*std	Outlier
2012-01-01	1.0	NY	5.00	7.554813	False
2012-02-01	2.0	NY	4.00	7.554813	False
2012-03-01	3.0	NY	3.00	7.554813	False
2012-04-01	4.0	NY	2.00	7.554813	False
2012-05-01	5.0	FL	2.25	3.434996	False
2012-06-01	6.0	FL	1.25	3.434996	False
2012-07-01	7.0	GA	0.25	0.980000	False
2012-08-01	8.0	GA	0.75	0.980000	False
2012-09-01	9.0	FL	1.75	3.434996	False
2012-10-01	10.0	FL	2.75	3.434996	False
2013-01-01	10.0	NY	4.00	7.554813	False
2013-02-01	10.0	NY	4.00	7.554813	False
2013-03-01	9.0	NY	3.00	7.554813	False
2013-04-01	9.0	NY	3.00	7.554813	False
2013-05-01	8.0	FL	0.75	3.434996	False
2013-06-01	8.0	FL	0.75	3.434996	False
2013-07-01	7.0	GA	0.25	0.980000	False
2013-08-01	7.0	GA	0.25	0.980000	False
2013-09-01	6.0	FL	1.25	3.434996	False
2013-10-01	6.0	FL	1.25	3.434996	False

# 方法三

# 拷贝原始数据帧
newdf = df.copy()

StateMonth = newdf.groupby(['State', lambda x: x.month])

def s(group):
    group['x-Mean'] = abs(group['Revenue'] - group['Revenue'].mean())
    group['1.96*std'] = 1.96*group['Revenue'].std()  
    group['Outlier'] = abs(group['Revenue'] - group['Revenue'].mean()) > 1.96*group['Revenue'].std()
    return group

Newdf2 = StateMonth.apply(s)
Newdf2

	Revenue	State	x-Mean	1.96*std	Outlier
2012-01-01	1.0	NY	4.5	12.473364	False
2012-02-01	2.0	NY	4.0	11.087434	False
2012-03-01	3.0	NY	3.0	8.315576	False
2012-04-01	4.0	NY	2.5	6.929646	False
2012-05-01	5.0	FL	1.5	4.157788	False
2012-06-01	6.0	FL	1.0	2.771859	False
2012-07-01	7.0	GA	0.0	0.000000	False
2012-08-01	8.0	GA	0.5	1.385929	False
2012-09-01	9.0	FL	1.5	4.157788	False
2012-10-01	10.0	FL	2.0	5.543717	False
2013-01-01	10.0	NY	4.5	12.473364	False
2013-02-01	10.0	NY	4.0	11.087434	False
2013-03-01	9.0	NY	3.0	8.315576	False
2013-04-01	9.0	NY	2.5	6.929646	False
2013-05-01	8.0	FL	1.5	4.157788	False
2013-06-01	8.0	FL	1.0	2.771859	False
2013-07-01	7.0	GA	0.0	0.000000	False
2013-08-01	7.0	GA	0.5	1.385929	False
2013-09-01	6.0	FL	1.5	4.157788	False
2013-10-01	6.0	FL	2.0	5.543717	False

newdf = df.copy()

State = newdf.groupby('State')

newdf['Lower'] = State['Revenue'].transform( lambda x: x.quantile(q=.25) - (1.5*(x.quantile(q=.75)-x.quantile(q=.25))) )
newdf['Upper'] = State['Revenue'].transform( lambda x: x.quantile(q=.75) + (1.5*(x.quantile(q=.75)-x.quantile(q=.25))) )
newdf['Outlier'] = (newdf['Revenue'] < newdf['Lower']) | (newdf['Revenue'] > newdf['Upper']) 
newdf

	Revenue	State	Lower	Upper	Outlier
2012-01-01	1.0	NY	-7.000	19.000	False
2012-02-01	2.0	NY	-7.000	19.000	False
2012-03-01	3.0	NY	-7.000	19.000	False
2012-04-01	4.0	NY	-7.000	19.000	False
2012-05-01	5.0	FL	2.625	11.625	False
2012-06-01	6.0	FL	2.625	11.625	False
2012-07-01	7.0	GA	6.625	7.625	False
2012-08-01	8.0	GA	6.625	7.625	True
2012-09-01	9.0	FL	2.625	11.625	False
2012-10-01	10.0	FL	2.625	11.625	False
2013-01-01	10.0	NY	-7.000	19.000	False
2013-02-01	10.0	NY	-7.000	19.000	False
2013-03-01	9.0	NY	-7.000	19.000	False
2013-04-01	9.0	NY	-7.000	19.000	False
2013-05-01	8.0	FL	2.625	11.625	False
2013-06-01	8.0	FL	2.625	11.625	False
2013-07-01	7.0	GA	6.625	7.625	False
2013-08-01	7.0	GA	6.625	7.625	False
2013-09-01	6.0	FL	2.625	11.625	False
2013-10-01	6.0	FL	2.625	11.625	False

x = [1,2,3,4]
y = [2,3,4,5]
dataset = list(zip(x,y))
du = pd.DataFrame(data=dataset,columns=['a','b'])
du

	a	b
0	1	2
1	2	3
2	3	4
3	4	5

du['a']

0    1
1    2
2    3
3    4
Name: a, dtype: int64

du['a'].quantile()

2.5