move column in pandas dataframe

I have the following dataframe:

 a b x y 0 1 2 3 -1 1 2 4 6 -2 2 3 6 9 -3 3 4 8 12 -4 

How can I move columns b and x such that they are the last 2 columns in the dataframe? I would like to specify b and x by name, but not the other columns.

12 Answers

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]] 

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 

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 :

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 ) 

similar to ROBBAT1's answer above, but hopefully a bit more robust:

df.insert(len(df.columns)-1, 'b', df.pop('b')) df.insert(len(df.columns)-1, 'x', df.pop('x')) 

This function will reorder your columns without losing data. Any omitted columns remain in the center of the data set:

def reorder_columns(columns, first_cols=[], last_cols=[], drop_cols=[]): columns = list(set(columns) - set(first_cols)) columns = list(set(columns) - set(drop_cols)) columns = list(set(columns) - set(last_cols)) new_order = first_cols + columns + last_cols return new_order 

Example usage:

my_list = ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'] reorder_columns(my_list, first_cols=['fourth', 'third'], last_cols=['second'], drop_cols=['fifth']) # Output: ['fourth', 'third', 'first', 'sixth', 'second'] 

To assign to your dataframe, use:

my_list = df.columns.tolist() reordered_cols = reorder_columns(my_list, first_cols=['fourth', 'third'], last_cols=['second'], drop_cols=['fifth']) df = df[reordered_cols] 

Simple solution:

old_cols = df.columns.values new_cols= ['a', 'y', 'b', 'x'] df = df.reindex(columns=new_cols) 

An alternative, more generic method;

from pandas import DataFrame def move_columns(df: DataFrame, cols_to_move: list, new_index: int) -> DataFrame: """ This method re-arranges the columns in a dataframe to place the desired columns at the desired index. ex Usage: df = move_columns(df, ['Rev'], 2) :param df: :param cols_to_move: The names of the columns to move. They must be a list :param new_index: The 0-based location to place the columns. :return: Return a dataframe with the columns re-arranged """ other = [c for c in df if c not in cols_to_move] start = other[0:new_index] end = other[new_index:] return df[start + cols_to_move + end] 

You can use pd.Index.difference with np.hstack, then reindex or use label-based indexing. In general, it's a good idea to avoid list comprehensions or other explicit loops with NumPy / Pandas objects.

cols_to_move = ['b', 'x'] new_cols = np.hstack((df.columns.difference(cols_to_move), cols_to_move)) # OPTION 1: reindex df = df.reindex(columns=new_cols) # OPTION 2: direct label-based indexing df = df[new_cols] # OPTION 3: loc label-based indexing df = df.loc[:, new_cols] print(df) # a y b x # 0 1 -1 2 3 # 1 2 -2 4 6 # 2 3 -3 6 9 # 3 4 -4 8 12 

You can also do this as a one-liner:

df.drop(columns=['b', 'x']).assign(b=df['b'], x=df['x']) 

This will move any column to the last column :

1. Move any column to the last column of dataframe :

df= df[ [ col for col in df.columns if col != 'col_name_to_moved' ] + ['col_name_to_moved']] 

2. Move any column to the first column of dataframe:

df= df[ ['col_name_to_moved'] + [ col for col in df.columns if col != 'col_name_to_moved' ]] 

where col_name_to_moved is the column that you want to move.

I use Pokémon database as an example, the columns for my data base are

['Name', '#', 'Type 1', 'Type 2', 'Total', 'HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Generation', 'Legendary'] 

Here is the code:

 import pandas as pd df = pd.read_html(')[0] cols = df.columns.to_list() cos_end= ["Name", "Total", "HP", "Defense"] for i, j in enumerate(cos_end, start=(len(cols)-len(cos_end))): cols.insert(i, cols.pop(cols.index(j))) print(cols) df = df.reindex(columns=cols) print(df)