How to swap two DataFrame columns?
PythonPandasPython Problem Overview
In MATLAB, to swap the first and second columns of a table A, one would do this1
A = A(:, [2 1 3:end]);
Is there a similarly convenient way to do this if A were a pandas DataFrame instead?
1 MATLAB uses 1-based indexing.
Python Solutions
Solution 1 - Python
pandas has reindex method that does it. You just need to give a list with the column names in the order you wish:
columns_titles = ["B","A"]
df=df.reindex(columns=columns_titles)
Cheers
Solution 2 - Python
A slight variant on acushner's answer:
# get a list of the columns
col_list = list(df)
# use this handy way to swap the elements
col_list[0], col_list[1] = col_list[1], col_list[0]
# assign back, the order will now be swapped
df.columns = col_list
example:
In [39]:
df = pd.DataFrame({'a':randn(3), 'b':randn(3), 'c':randn(3)})
df
Out[39]:
a b c
0 -0.682446 -0.200654 -1.609470
1 -1.998113 0.806378 1.252384
2 -0.250359 3.774708 1.100771
In [40]:
col_list = list(df)
col_list[0], col_list[1] = col_list[1], col_list[0]
df.columns = col_list
df
Out[40]:
b a c
0 -0.682446 -0.200654 -1.609470
1 -1.998113 0.806378 1.252384
2 -0.250359 3.774708 1.100771
UPDATE
If you just want to change the column order without changing the column contents then you can reindex using fancy indexing:
In [34]:
cols = list(df)
cols[1], cols[0] = cols[0], cols[1]
cols
Out[34]:
['b', 'a', 'c']
In [35]:
df.ix[:,cols]
Out[35]:
b a c
0 -0.200654 -0.682446 -1.609470
1 0.806378 -1.998113 1.252384
2 3.774708 -0.250359 1.100771
Solution 3 - Python
c = A.columns
A = A[c[np.r_[1, 0, 2:len(c)]]]
or, even easier:
A[[c[0], c[1]]] = A[[c[1], c[0]]]
*edit: fixed per Ivan's suggestions.
Solution 4 - Python
In my case, I have over 100 columns in my data frame. So instead list all columns, I wrote a short function to just switch two columns
def df_column_switch(df, column1, column2):
i = list(df.columns)
a, b = i.index(column1), i.index(column2)
i[b], i[a] = i[a], i[b]
df = df[i]
return df
Solution 5 - Python
I finally settled for this:
A = A.iloc[:, [1, 0] + range(2, A.shape[1])]
It's far less convenient than the MATLAB version, but I like the fact that it does not require creating temporary variables.
Solution 6 - Python
If you have multiple columns and performance and memory are not an issue, you can simply use this function:
def swap_columns(df, c1, c2):
df['temp'] = df[c1]
df[c1] = df[c2]
df[c2] = df['temp']
df.drop(columns=['temp'], inplace=True)
Solution 7 - Python
I would use:
end = df.shape[1] # or len(df.columns)
df.iloc[:, np.r_[1, 0, 2:end]
Solution 8 - Python
This works for me in Python 3.x:
df = df.iloc[:, [1, 0] + list(range(2, df.shape[1]))]
Remember df = P.iloc[:, [1, 0] + range(2, P.shape[1])] wont work and will give the error:
TypeError: can only concatenate list (not "range") to list
Solution 9 - Python
colList = list(df.columns)
colList[0], colList[1] = colList[1], colList[0]
df = df[colList]
Solution 10 - Python
For Dataframes in python, Considering that you have given the 2 columns, then:
#df is your data frame
col1='c1'
col2='c2'
df = df[[col1 if col == col2 else col2 if col == col1 else col for col in df.columns]]
Solution 11 - Python
column swap
import pandas as pd
df = pd.read_csv('/Users/parent/Desktop/Col_swap.csv')
print(df)
columns_titles = ["A","B","C","E"]
df_reorder=df.reindex(columns=columns_titles)
df_reorder.to_csv('/Users/parent/Desktop/col_reorder1.csv', index=False)
print(df_reorder)
Output:
B A C E
0 c1 a1 b1 d1
1 c2 a2 b2 d2
A B C E
0 a1 c1 b1 d1
1 a2 c2 b2 d2
Solution 12 - Python
You can use this easily
columns_titles = ["D","C","B","A"]
and then
df=df[column_titles]