python - Extracting specific selected columns to new DataFrame as a copy

258
votes

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?

7
pythonpandaschained-assignment

7 Answers

506
votes

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)
32
votes

The easiest way is

new = old[['A','C','D']]

.

13
votes

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 pass 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
11
votes

columns by index:

# selected column index: 1, 6, 7
new = old.iloc[: , [1, 6, 7]].copy()
6
votes

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)
4
votes

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)
2
votes

If you want to have a new data frame then:

import pandas as pd
old = pd.DataFrame({'A' : [4,5], 'B' : [10,20], 'C' : [100,50], 'D' : [-30,-50]})
new=  old[['A', 'C', 'D']]