我有这个CSV文件refs/：
emp.csv使用以下代码，我得到所有index empno ename job mgr hiredate sal comm deptno
0, 7839, KING, PRESIDENT, 0, 1981-11-17, 5000, 0, 10
1, 7698, BLAKE, MANAGER, 7839, 1981-05-01, 2850, 0, 30
2, 7782, CLARK, MANAGER, 7839, 1981-05-09, 2450, 0, 10
3, 7566, JONES, MANAGER, 7839, 1981-04-01, 2975, 0, 20
4, 7654, MARTIN, SALESMAN, 7698, 1981-09-10, 1250, 1400, 30
5, 7499, ALLEN, SALESMAN, 7698, 1981-02-11, 1600 300, 30
6, 7844, TURNER, SALESMAN, 7698, 1981-08-21, 1500, 0, 30
7, 7900, JAMES, CLERK, 7698, 1981-12-11, 950, 0, 30
8, 7521, WARD, SALESMAN, 7698, 1981-02-23, 1250, 500, 30
9, 7902, FORD, ANALYST, 7566, 1981-12-11, 3000, 0, 20
10, 7369, SMITH, CLERK, 7902, 1980-12-09, 800, 0, 20
11, 7788, SCOTT, ANALYST, 7566 1982-12-22, 3000, 0, 20
12, 7876, ADAMS, CLERK, 7788, 1983-01-15, 1100, 0, 20
13, 7934, MILLER, CLERK, 7782, 1982-01-11, 1300, 0, 10
＆＃39; s：
NaN这是代码的输出：
import csv
import sys
import pandas as pd
import dateutil
# Load data from csv file
emp = pd.DataFrame.from_csv("D:\R data\emp.csv")
# Convert date from string to date times`enter code here`
emp['hiredate'] = emp['hiredate'].apply(dateutil.parser.parse, dayfirst=True)
jonessal = emp[['sal']][emp['ename']=='JONES']
empename = emp[['ename','sal']][emp['sal'] > jonessal]
print(empename)
我想要的输出是：
index
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
10 NaN NaN
11 NaN NaN
12 NaN NaN
13 NaN NaN
我认为变量index
0 KING 5000
9 FORD 3000
11 SCOTT 3000
的值是2975，但结果是jonesal。
如果我使用NaN对薪水进行硬编码，它会正常工作，但当我使用变量时，它会返回所有NaN：empename = emp[['ename','sal']][emp['sal'] > 2975 ]
答案 0 :(得分：3)
emp[['ename','sal']][emp['sal'] > jonessal]
是一个DataFrame。
emp['sal'] > jonessal这里，比较jonessal = emp.loc[emp['ename']=='JONES', 'sal'].values[0]
不是标量，并且由于brodcasting它返回一个奇怪的DataFrame。由于索引/形状不匹配，因此最终结果由NaN组成。
在这里，您假设只有一名员工叫琼斯。遵循相同的假设，你可以得到标量：
.values（[0]返回一个数组，emp[['ename','sal']][emp['sal'] > jonessal]
Out[81]:
ename sal
0 KING 5000
9 FORD 3000
11 SCOTT 3000
来自单个员工的假设。）
现在，它将返回相同的结果：
Use a List of boolean type to hold state of check box.By default fill your collection with false value.
When you select a check box change the state of map using set method
As you know When you scroll there will be a call to your adapter there you read the value from map using get and set it to checkbox
List<Boolean> checkstate = new ArrayList<Boolean>();
Inside the adapter constructor
for(i=0;i<itemSize;i++)
{
checkstate.add(false);
}
Inside your Viewholder add below Line what it does is whatever the updated value of checkbox will set to your checkbox.Initially all the Items will be false
yourCheckbox.setChecked(checkstate.get(position));
Now inside onCheckedChanged Listener
if(boxchecked)
{
checkstate.set(position, true);
}else
{
checkstate.set(position, false);
}
答案 1 :(得分：2)
我认为您需要read_csv boolean indexing和ix，它们只会过滤ename和sal列：
import pandas as pd
import io
temp=u"""index empno ename job mgr hiredate sal comm deptno
0, 7839, KING, PRESIDENT, 0, 1981-11-17, 5000, 0, 10
1, 7698, BLAKE, MANAGER, 7839, 1981-05-01, 2850, 0, 30
2, 7782, CLARK, MANAGER, 7839, 1981-05-09, 2450, 0, 10
3, 7566, JONES, MANAGER, 7839, 1981-04-01, 2975, 0, 20
4, 7654, MARTIN, SALESMAN, 7698, 1981-09-10, 1250, 1400, 30
5, 7499, ALLEN, SALESMAN, 7698, 1981-02-11, 1600, 300, 30
6, 7844, TURNER, SALESMAN, 7698, 1981-08-21, 1500, 0, 30
7, 7900, JAMES, CLERK, 7698, 1981-12-11, 950, 0, 30
8, 7521, WARD, SALESMAN, 7698, 1981-02-23, 1250, 500, 30
9, 7902, FORD, ANALYST, 7566, 1981-12-11, 3000, 0, 20
10, 7369, SMITH, CLERK, 7902, 1980-12-09, 800, 0, 20
11, 7788, SCOTT, ANALYST, 7566, 1982-12-22, 3000, 0, 20
12, 7876, ADAMS, CLERK, 7788, 1983-01-15, 1100, 0, 20
13, 7934, MILLER, CLERK, 7782, 1982-01-11, 1300, 0, 10"""
#after testing replace io.StringIO(temp) to filename
emp = pd.read_csv(io.StringIO(temp),
skipinitialspace=True,
skiprows=1,
parse_dates=[5],
names=['index','empno','ename', 'job','mgr','hiredate','sal','comm','deptno'])
print (emp)
index empno ename job mgr hiredate sal comm deptno
0 0 7839 KING PRESIDENT 0 1981-11-17 5000 0 10
1 1 7698 BLAKE MANAGER 7839 1981-05-01 2850 0 30
2 2 7782 CLARK MANAGER 7839 1981-05-09 2450 0 10
3 3 7566 JONES MANAGER 7839 1981-04-01 2975 0 20
4 4 7654 MARTIN SALESMAN 7698 1981-09-10 1250 1400 30
5 5 7499 ALLEN SALESMAN 7698 1981-02-11 1600 300 30
6 6 7844 TURNER SALESMAN 7698 1981-08-21 1500 0 30
7 7 7900 JAMES CLERK 7698 1981-12-11 950 0 30
8 8 7521 WARD SALESMAN 7698 1981-02-23 1250 500 30
9 9 7902 FORD ANALYST 7566 1981-12-11 3000 0 20
10 10 7369 SMITH CLERK 7902 1980-12-09 800 0 20
11 11 7788 SCOTT ANALYST 7566 1982-12-22 3000 0 20
12 12 7876 ADAMS CLERK 7788 1983-01-15 1100 0 20
13 13 7934 MILLER CLERK 7782 1982-01-11 1300 0 10
jonessal = emp.ix[emp['ename'] == 'JONES', 'sal'].iat[0]
print (jonessal)
2975
empename = emp.ix[emp['sal'] > jonessal, ['ename','sal']]
print (empename)
ename sal
0 KING 5000
9 FORD 3000
11 SCOTT 3000
答案 2 :(得分：1)
您对DataFrame.from_csv的调用不正确。默认情况下，它会使用逗号＆＃34;，＆＃34;作为字段分隔符：
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_csv.html
Parameters:
sep : string, default ‘,’
Field delimiter
但是你的csv不是以逗号分隔，而是以制表符分隔。
尝试将sep='\t'参数添加到from_csv调用：pd.DataFrame.from_csv("D:\R data\emp.csv",sep='\t')