问题
I have a pandas DataFrame with 4 columns and I want to create a new DataFrame that only has three of the columns. This question is similar to: Extracting specific columns from a data frame but for pandas not R. The following code does not work, raises an error, and is certainly not the pandasnic way to do it.
import pandas as pd
old = pd.DataFrame({'A' : [4,5], 'B' : [10,20], 'C' : [100,50], 'D' : [-30,-50]})
new = pd.DataFrame(zip(old.A, old.C, old.D)) # raises TypeError: data argument can't be an iterator
What is the pandasnic way to do it?
回答1:
There is a way of doing this and it actually looks similar to R
new = old[['A', 'C', 'D']].copy()
Here you are just selecting the columns you want from the original data frame and creating a variable for those. If you want to modify the new dataframe at all you'll probably want to use .copy() to avoid a SettingWithCopyWarning.
An alternative method is to use filter which will create a copy by default:
new = old.filter(['A','B','D'], axis=1)
Finally, depending on the number of columns in your original dataframe, it might be more succinct to express this using a drop (this will also create a copy by default):
new = old.drop('B', axis=1)
回答2:
Another simpler way seems to be :
new = pd.DataFrame([old.A,old.B,old.C]).transpose()
where old.column_name will give you a series. Make a list of all the column-series you want to retain and passs it to the DataFrame constructor. We need to do a transpose to adjust the shape.
In [14]:pd.DataFrame([old.A,old.B,old.C]).transpose()
Out[14]:
A B C
0 4 10 100
1 5 20 50
回答3:
The easiest way is
new = old[['A','C','D']]
.
回答4:
Generic functional form
def select_columns(data_frame, column_names):
new_frame = data_frame.loc[:, column_names]
return new_frame
Specific for your problem above
selected_columns = ['A', 'C', 'D']
new = select_columns(old, selected_columns)
回答5:
As far as I can tell, you don't necessarily need to specify the axis when using the filter function.
new = old.filter(['A','B','D'])
returns the same dataframe as
new = old.filter(['A','B','D'], axis=1)
回答6:
columns by index:
# selected column index: 1, 6, 7
new = old.iloc[: , [1, 6, 7]].copy()
来源：https://stackoverflow.com/questions/34682828/extracting-specific-selected-columns-to-new-dataframe-as-a-copy