pandas 在熊猫数据框中插入值
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Insert value in panda dataframe
提问by NILESH SUTHAR
I have data in an Excel sheet. I want to check one column value for a range and if that value lies in that range(5000-15000) then I want to insert value in another column(Correct or Flag).
我在 Excel 工作表中有数据。我想检查一个范围的一列值，如果该值在该范围内（5000-15000），那么我想在另一列（正确或标志）中插入值。
I have three columns: City, rent, status.
我有三列：城市、租金、状态。
I have tried append and insert method but that didn't work. How should I do this?
我试过 append 和 insert 方法，但没有用。我该怎么做？
Here is my code:
这是我的代码：
for index, row in df.iterrows():
对于索引，df.iterrows() 中的行：
if row['city']=='mumbai':
if 5000<= row['rent']<=15000:
pd.DataFrame.append({'Status': 'Correct'})
It shows this error:
它显示此错误：
TypeError: append() missing 1 required positional argument: 'other'
类型错误：append() 缺少 1 个必需的位置参数：“其他”
What procedure should I follow to insert data row by row in a column?
在列中逐行插入数据应该遵循什么程序？
采纳答案by jezrael
I think you can use numpy.wherewith boolean mask created by betweenand comparing with city:
我认为您可以使用numpy.where由创建的布尔掩码between并与之进行比较city：
mask = (df['city']=='mumbai') & df['rent'].between(5000,15000)
df['status'] = np.where(mask, 'Correct', 'Uncorrect')
Sample:
样本：
df = pd.DataFrame({'city':['mumbai','mumbai','mumbai', 'a'],
'rent':[1000,6000,10000,10000]})
mask = (df['city']=='mumbai') & df['rent'].between(5000,15000)
df['status'] = np.where(mask, 'Correct', 'Flag')
print (df)
city rent status
0 mumbai 1000 Flag
1 mumbai 6000 Correct
2 mumbai 10000 Correct
3 a 10000 Flag
Another solution with loc:
另一个解决方案loc：
mask = (df['city']=='mumbai') & df['rent'].between(5000,15000)
df['status'] = 'Flag'
df.loc[mask, 'status'] = 'Correct'
print (df)
city rent status
0 mumbai 1000 Flag
1 mumbai 6000 Correct
2 mumbai 10000 Correct
3 a 10000 Flag
For write to excel use to_excel, if need remove index column add index=False:
对于写入 excel 使用to_excel，如果需要删除索引列添加index=False：
df.to_excel('file.xlsx', index=False)
EDIT:
编辑：
For multiple masks is possible use:
对于多个masks 可以使用：
df = pd.DataFrame({'city':['Mumbai','Mumbai','Delhi', 'Delhi', 'Bangalore', 'Bangalore'],
'rent':[1000,6000,10000,1000,4000,5000]})
print (df)
city rent
0 Mumbai 1000
1 Mumbai 6000
2 Delhi 10000
3 Delhi 1000
4 Bangalore 4000
5 Bangalore 5000
m1 = (df['city']=='Mumbai') & df['rent'].between(5000,15000)
m2 = (df['city']=='Delhi') & df['rent'].between(1000,5000)
m3 = (df['city']=='Bangalore') & df['rent'].between(3000,5000)
m = m1 | m2 | m3
print (m)
0 False
1 True
2 False
3 True
4 True
5 True
dtype: bool
from functools import reduce
mList = [m1,m2,m3]
m = reduce(lambda x,y: x | y, mList)
print (m)
0 False
1 True
2 False
3 True
4 True
5 True
dtype: bool
print (df[m])
city rent
1 Mumbai 6000
3 Delhi 1000
4 Bangalore 4000
5 Bangalore 5000
