Python 按日期和/或条件筛选

我正在使用

pandas

尝试统计在两个日期之间购买特定类型合同的会员人数。我正在使用的数据帧类似于：

Member Nbr       Contract-Type    Date-Joined 
20           1 Year Membership     2011-08-01   
3128        3 Month Membership     2011-07-22   
3535        4 Month Membership     2015-02-18  
3760        4 Month Membership     2010-02-28
3762        3 Month Membership     2010-01-31
3882        1 Month Membership     2010-04-24    
3892        3 Month Membership     2010-03-24     
4116        3 Month Membership     2014-12-02   
4700        1 Month Membership     2014-11-11   
4802        4 Month Membership     2014-07-26   
5004         1 Year Membership     2012-03-12
5020         1 Year Membership     2010-07-28    
5022        3 Month Membership     2010-06-25    
5130         1 Year Membership     2011-01-04
                      ...

如果我只对使用一种合同类型感兴趣，我就能够获得计数

print(len(df[(df['Date-Joined'] > '2010-01-01') 
          & (df['Date-Joined'] < '2012-02-01')
          & (df['Member Type'] == '1 Year Membership')]))

我得到以下错误

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

---

用

和
条件替换
或
条件返回
0
使用
而不是
或
。另外，
&
优先于
|
，因此您的逻辑还需要一组括号

import io
import pandas as pd

data = io.StringIO('''\
Member Nbr,Contract-Type,Date-Joined
20,1 Year Membership,2011-08-01   
3128,3 Month Membership,2011-07-22
3535,4 Month Membership,2015-02-18  
3760,4 Month Membership,2010-02-28
3762,3 Month Membership,2010-01-31
3882,1 Month Membership,2010-04-24 
3892,3 Month Membership,2010-03-24
4116,3 Month Membership,2014-12-02
4700,1 Month Membership,2014-11-11
4802,4 Month Membership,2014-07-26
5004,1 Year Membership,2012-03-12
5020,1 Year Membership,2010-07-28 
5022,3 Month Membership,2010-06-25 
5130,1 Year Membership,2011-01-04
''')

df = pd.read_csv(data)

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   (df['Contract-Type'] == '1 Year Membership')
  ])

#     Member Nbr      Contract-Type    Date-Joined
# 0           20  1 Year Membership     2011-08-01   
# 11        5020  1 Year Membership     2010-07-28 
# 13        5130  1 Year Membership     2011-01-04

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   (df['Contract-Type'] == '1 Year Membership') |
   (df['Contract-Type'] == '4 Month Membership')
  ])

#     Member Nbr       Contract-Type    Date-Joined
# 0           20   1 Year Membership     2011-08-01   
# 2         3535  4 Month Membership     2015-02-18  <====== BEWARE!
# 3         3760  4 Month Membership     2010-02-28
# 9         4802  4 Month Membership     2014-07-26  <====== BEWARE!
# 11        5020   1 Year Membership     2010-07-28 
# 13        5130   1 Year Membership     2011-01-04

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   ((df['Contract-Type'] == '1 Year Membership') |
   (df['Contract-Type'] == '4 Month Membership'))
  ])

#     Member Nbr       Contract-Type    Date-Joined
# 0           20   1 Year Membership     2011-08-01   
# 3         3760  4 Month Membership     2010-02-28
# 11        5020   1 Year Membership     2010-07-28 
# 13        5130   1 Year Membership     2011-01-04

导入io
作为pd进口熊猫
数据=io.StringIO（“”）\
成员编号、合同类型、加入日期
20.1年会员资格，2011-08-01
3128，3个月会员，2011-07-22
3535,4个月会员资格，2015-02-18
3760，4个月会员资格，2010-02-28
3762，3个月会员资格，2010-01-31
3882，一个月会员资格，2010-04-24
3892，3个月会员资格，2010-03-24
4116,3个月会员资格，2014-12-02
4700,1个月会员资格，2014-11-11
4802，4个月会员资格，2014-07-26
5004，一年会员资格，2012年3月12日
5020，一年会员资格，2010-07-28
5022，3个月会员资格，2010-06-25
5130，一年会员，2011-01-04
''')
df=pd.read\U csv（数据）
打印（df）[
（df['Date-Joined']>'2010-01-01'）&
（df['Date-Joined']<'2012-02-01'）&
（df['Contract-Type']=='1年会员资格'）
])
#成员Nbr合同类型加入日期
#0 20 1年会员资格2011-08-01
#11 5020一年会员资格2010-07-28
#135130一年会员资格2011-01-04
打印（df）[
（df['Date-Joined']>'2010-01-01'）&
（df['Date-Joined']<'2012-02-01'）&
（df['Contract-Type']=='1年会员资格'）|
（df['Contract-Type']=='4个月会员资格'）
])
#成员Nbr合同类型加入日期
#0 20 1年会员资格2011-08-01
#23535 4个月会员资格2015-02-18使用
|
而不是
或
。此外，
&
优先于
|
，因此您的逻辑可能还需要一组括号。
import io
import pandas as pd

data = io.StringIO('''\
Member Nbr,Contract-Type,Date-Joined
20,1 Year Membership,2011-08-01   
3128,3 Month Membership,2011-07-22
3535,4 Month Membership,2015-02-18  
3760,4 Month Membership,2010-02-28
3762,3 Month Membership,2010-01-31
3882,1 Month Membership,2010-04-24 
3892,3 Month Membership,2010-03-24
4116,3 Month Membership,2014-12-02
4700,1 Month Membership,2014-11-11
4802,4 Month Membership,2014-07-26
5004,1 Year Membership,2012-03-12
5020,1 Year Membership,2010-07-28 
5022,3 Month Membership,2010-06-25 
5130,1 Year Membership,2011-01-04
''')

df = pd.read_csv(data)

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   (df['Contract-Type'] == '1 Year Membership')
  ])

#     Member Nbr      Contract-Type    Date-Joined
# 0           20  1 Year Membership     2011-08-01   
# 11        5020  1 Year Membership     2010-07-28 
# 13        5130  1 Year Membership     2011-01-04

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   (df['Contract-Type'] == '1 Year Membership') |
   (df['Contract-Type'] == '4 Month Membership')
  ])

#     Member Nbr       Contract-Type    Date-Joined
# 0           20   1 Year Membership     2011-08-01   
# 2         3535  4 Month Membership     2015-02-18  <====== BEWARE!
# 3         3760  4 Month Membership     2010-02-28
# 9         4802  4 Month Membership     2014-07-26  <====== BEWARE!
# 11        5020   1 Year Membership     2010-07-28 
# 13        5130   1 Year Membership     2011-01-04

print(df[
   (df['Date-Joined'] > '2010-01-01') &
   (df['Date-Joined'] < '2012-02-01') &
   ((df['Contract-Type'] == '1 Year Membership') |
   (df['Contract-Type'] == '4 Month Membership'))
  ])

#     Member Nbr       Contract-Type    Date-Joined
# 0           20   1 Year Membership     2011-08-01   
# 3         3760  4 Month Membership     2010-02-28
# 11        5020   1 Year Membership     2010-07-28 
# 13        5130   1 Year Membership     2011-01-04