move column in pandas dataframe

For example, to move column "name" to be the first column in df you can use insert:

column_to_move = df.pop("name")

# insert column with insert(location, column_name, column_value)

df.insert(0, "name", column_to_move)

similarly, if you want this column to be e.g. third column from the beginning:

df.insert(2, "name", column_to_move )

You can rearrange columns directly by specifying their order:

df = df[['a', 'y', 'b', 'x']]

In the case of larger dataframes where the column titles are dynamic, you can use a list comprehension to select every column not in your target set and then append the target set to the end.

>>> df[[c for c in df if c not in ['b', 'x']] 
       + ['b', 'x']]
   a  y  b   x
0  1 -1  2   3
1  2 -2  4   6
2  3 -3  6   9
3  4 -4  8  12

To make it more bullet proof, you can ensure that your target columns are indeed in the dataframe:

cols_at_end = ['b', 'x']
df = df[[c for c in df if c not in cols_at_end] 
        + [c for c in cols_at_end if c in df]]

You can use to way below. It's very simple, but similar to the good answer given by Charlie Haley.

df1 = df.pop('b') # remove column b and store it in df1
df2 = df.pop('x') # remove column x and store it in df2
df['b']=df1 # add b series as a 'new' column.
df['x']=df2 # add b series as a 'new' column.

Now you have your dataframe with the columns 'b' and 'x' in the end. You can see this video from OSPY : https://youtu.be/RlbO27N3Xg4

cols = list(df.columns.values) #Make a list of all of the columns in the df
cols.pop(cols.index('b')) #Remove b from list
cols.pop(cols.index('x')) #Remove x from list
df = df[cols+['b','x']] #Create new dataframe with columns in the order you want