pandas 基于循环变量和附加字符串创建新列名
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Creating a new column name based on a loop variable and an additional string
提问by A.Papa
I want to create percentage change column for each column that is a float in my dataframe and stored it in a newn column each time with the name of the initial column and the add on "_change"
我想为我的数据框中的每个浮动列创建百分比变化列，并每次将其存储在一个 newn 列中，其中包含初始列的名称和“_change”的添加
I tried this but it does not seem to work any idea?
我试过这个，但它似乎没有任何想法？
for col in df.columns:
if df[col].dtypes == "float":
df[ col&'_change'] = (df.col - df.groupby(['New_ID']).col.shift(1))/ df.col
for example if my column is df["Expenses"] I would like to save the percentage change in df["Expenses_change"] Edited for adding example data frame and output
例如，如果我的列是 df["Expenses"] 我想保存 df["Expenses_change"] 中的百分比变化编辑以添加示例数据框和输出
df initially
df 最初
Index ID Reporting_Date Sales_Am Exp_Am
0 1 01/01/2016 1000 900
1 1 02/01/2016 1050 950
2 1 03/01/2016 1060 960
3 2 01/01/2016 2000 1850
4 2 02/01/2016 2500 2350
4 2 03/01/2016 3000 2850
after the loop
循环后
Index ID Reporting_Date Sales_Am Sales_Am_chge Exp_Am Exp_Am_chge
0 1 01/01/2016 1000 Null 900 Null
1 1 02/01/2016 1050 5% 950 6%
2 1 03/01/2016 1060 1% 960 1%
3 2 01/01/2016 2000 Null 1850 Null
4 2 02/01/2016 2500 25% 2350 27%
4 2 03/01/2016 3000 20% 2850 21%
keep in mind that i have more than 2 columns on my dataframe.
请记住，我的数据框中有超过 2 列。
回答by Zach
Why are you using '&' instead of '+' in
为什么你使用 '&' 而不是 '+'
df[ col&'_change']
?
?
回答by jpp
String concatenation is performed in python via the +operator.
字符串连接是通过+操作符在 python 中执行的。
So changing to col+'_change'will fix this issue for you.
因此，更改为col+'_change'将为您解决此问题。
You might find it helpful to read the relevant python documentation.
您可能会发现阅读相关的Python 文档很有帮助。
回答by DanSan
As it has been mentioned in other answers, just by changing & for + should do it. I was getting issues with using dots instead of square brackets so I changed them too.
正如其他答案中提到的那样，只需更改 & for + 即可。我在使用点而不是方括号时遇到问题，所以我也改变了它们。
This code has been tested in Python 3 and it works :)
此代码已在 Python 3 中进行了测试，并且可以正常工作:)
for col in df.columns:
if df[col].dtypes == "float":
df[col+'_change'] = (df[col] - df.groupby(['repeat_present'])[col].shift(1))/ df[col]
Enjoy!
享受！
