How to change the order of Pandas DataFrame columns

Different methods to change the order of columns in pandas DataFrame

In this tutorial we will discuss how to change the order of columns in panads dataframe  using the following methods:

• Using reindex() function
• Using sort_index() function
• Using indexing
• Move columns to particular position

 

Create pandas DataFrame with example data

DataFrame is a data structure used to store the data in two dimensional format. It is similar to table that stores the data in rows and columns. Rows represents the records/ tuples and columns refers to the attributes.

We can create the DataFrame by using pandas.DataFrame() method.

Syntax:

pandas.DataFrame(input_data,columns,index)

Parameters:

It will take mainly three parameters

1. input_data is represents a list of data
2. columns represent the columns names for the data
3. index represent the row numbers/values

We can also create a DataFrame using dictionary by skipping columns and indices.

 

Example: Python Program to create a dataframe for market data from a dictionary of food items by specifying the column names.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2  

You can learn more at Pandas dataframe explained with simple examples

 

Method 1 : Using reindex() function

reindex() function is used to change the order of columns in the given dataframe.

It will take list of specified columns as input and return the dataframe according to the specified order.

Syntax:

dataframe.reindex(columns=[list_of_columns])

where,

1. dataframe is the input dataframe
2. columns takes the list of columns specified in an order

 

Example 1: Define the order of individual columns

In this example we are going to order the dataframe.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe aafter ordering columns
print(dataframe.reindex(columns=['cost','quantity','id','name']))

Output:

          cost  quantity      id            name
item-1  567.00         1  foo-23  ground-nut oil
item-2  562.56         2  foo-13         almonds
item-3   67.00         3  foo-02           flour
item-4   76.09         2  foo-31         cereals

 

Example 2: Order the dataframe columns alphabetically

If we want to order the columns in an alphabetical order, then we have to use sorted() method with reindex() function.

Syntax:

dataframe.reindex(sorted(dataframe.columns), axis=1)

Example: In this example, we are going to order the columns in alphabetical order.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns in alphabetical order
print(dataframe.reindex(sorted(dataframe.columns), axis=1))

Output:

           cost      id            name  quantity
item-1  567.00  foo-23  ground-nut oil         1
item-2  562.56  foo-13         almonds         2
item-3   67.00  foo-02           flour         3
item-4   76.09  foo-31         cereals         2

 

Method 2 : Using sort_index() function

sort_index() function is used to order the columns based on ascending or descending order.So we can change the order of columns by using this fuction.

Syntax:

dataframe.sort_index(axis=1, ascending)

where,

1. dataframe is the input dataframe
2. axis=1 specifies column
3. ascending is the parameter used to order the columns in ascending order when it is set to True and order the dataframe columns  in descending order when it is set to False

 

Example 1: Chage the order of dataframe columns in ascending order

In this example we will change the order of columns in ascending order

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns in ascending order
print(dataframe.sort_index(axis=1, ascending=True))

Output:

          cost      id            name  quantity
item-1  567.00  foo-23  ground-nut oil         1
item-2  562.56  foo-13         almonds         2
item-3   67.00  foo-02           flour         3
item-4   76.09  foo-31         cereals         2

 

Example 2: Change the order of columns in descending order

In this example we will change the order of columns in descending order

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns in descending order
print(dataframe.sort_index(axis=1, ascending=False))

Output:

        quantity            name      id    cost
item-1         1  ground-nut oil  foo-23  567.00
item-2         2         almonds  foo-13  562.56
item-3         3           flour  foo-02   67.00
item-4         2         cereals  foo-31   76.09

 

Method 3 : Using indexing

In this method , we are going  change the order of columns by passing  the columns in a list in an order through index - [].

Syntax:

dataframe[[list_of_columns]]

where,

1. dataframe is the input dataframe
2. list_of_columns is the columns in an order

 

Example 1: In this example, we specified the columns through an index - [] in the order - ['cost','id','name','quantity']

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns 
print(dataframe[['cost','id','name','quantity']])

Output:

          cost      id            name  quantity
item-1  567.00  foo-23  ground-nut oil         1
item-2  562.56  foo-13         almonds         2
item-3   67.00  foo-02           flour         3
item-4   76.09  foo-31         cereals         2

 

Example 2: In this example, we specified the columns through an index - [] in the order - ['name','quantity','cost','id']

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns 
print(dataframe[['name','quantity','cost','id']])

Output:

                  name  quantity    cost      id
item-1  ground-nut oil         1  567.00  foo-23
item-2         almonds         2  562.56  foo-13
item-3           flour         3   67.00  foo-02
item-4         cereals         2   76.09  foo-31

 

Method 4 : Move columns to particular position

Here we are going to order the columns by changing the position.

So first we have to remove the column at the position and then we replace the column at particular column.

We can remove by using pop() function

Syntax:

column = dataframe.pop('column_name')

where, column_name is the column to be replaced

 

We can insert the column by using insert() function

Syntax:

dataframe.insert(position, 'column_name', column)

where, position is an index value to place the column at given position

 

Example 1: Program to remove cost column and place at first position

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns 
# by moving cost column to first
column = dataframe.pop('cost')
dataframe.insert(0, 'cost', column)

# display dataframe
print(dataframe)

Output:

          cost      id            name  quantity
item-1  567.00  foo-23  ground-nut oil         1
item-2  562.56  foo-13         almonds         2
item-3   67.00  foo-02           flour         3
item-4   76.09  foo-31         cereals         2

 

Example 2: Program to remove id column and place at last position

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe after ordering columns 
# by moving id column to last
column = dataframe.pop('id')
dataframe.insert(3, 'id', column)

# display dataframe
print(dataframe)

Output:

                  name    cost  quantity      id
item-1  ground-nut oil  567.00         1  foo-23
item-2         almonds  562.56         2  foo-13
item-3           flour   67.00         3  foo-02
item-4         cereals   76.09         2  foo-31

 

Summary

In this article, we discussed how to change the order of columns in pandas dataframe using reindex(), sort_index() , indexing methods, we also seen that how to order the columns alphabetically using sorted() with reindex() function.

pop() and insert() functions played an important role to change the order of columns.

 

References

• Pandas reindex()
• Indexing in panads