Extracting specific selected columns to new DataFrame as a copy

Question:

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 pandas 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 pandas way to do it?

Asked By: SpeedCoder5

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Source

Answers:

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)
Answered By: johnchase

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
Answered By: Hit

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)
Answered By: Deslin Naidoo

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)
Answered By: Ellen

The easiest way is

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

.

Answered By: stidmatt

columns by index:

# selected column index: 1, 6, 7
new = old.iloc[: , [1, 6, 7]].copy()
Answered By: sailfish009

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']]
Answered By: Ali.E

You can drop columns in the index:

df = pd.DataFrame({'A': [1, 1], 'B': [2, 2], 'C': [3, 3], 'D': [4, 4]})

df[df.columns.drop(['B', 'C'])]

or

df.loc[:, df.columns.drop(['B', 'C'])]

Output:

   A  D
0  1  4
1  1  4
Answered By: Mykola Zotko

As an alternative:

new = pd.DataFrame().assign(A=old['A'], C=old['C'], D=old['D'])
Answered By: Dimitris Paraschakis

You can also use get() to create a new copy (that doesn’t run into SettingWithCopyWarning).

new = old.get(['A', 'C', 'D'])

Also, filter selects by column labels by default, so the following works.

new = old.filter(['A', 'C', 'D'])

axis= is needed if one needs to select by row. For example, old.filter([0], axis=0) selects the first row.

If new is an already existing dataframe, then assign() also works (if you want to keep the old columns with their original column names).

new = pd.DataFrame()
new = new.assign(**old[['A', 'C', 'D']])
Answered By: cottontail