How To Change Order of Columns in Pandas Dataframe – With Examples

Pandas dataframe is a two-dimensional data structure that allows you to store data in rows and columns format.

You can change the order of columns in the pandas dataframe using the df.reindex() method.

In this tutorial, you’ll learn how to change the order of columns in a pandas dataframe.

If You’re in Hurry…

To change the order of columns in pandas using the reindex(), pass the columns list in the order you want.

df = df.reindex(columns=['Available_Since_Date','Product_name', 'Unit_Price','No_Of_Units','Available_Quantity'])

df

The column Available_Since_date is moved to the first position.

Dataframe Will Look Like

	Available_Since_Date	Product_name	Unit_Price	No_Of_Units	Available_Quantity
0	11/5/2021	Keyboard	500.000	5	5
1	4/23/2021	Mouse	200.000	5	6
2	08/21/2021	Monitor	5000.235	10	10
3	09/18/2021	CPU	10000.550	20	Not Available
4	09/18/2021	CPU	10000.550	20	Not Available
5	01/05/2021	Speakers	250.500	8	NaT
6	NaT	NaT	NaN	NaT	NaT

If You Want to Understand Details, Read on…

In this tutorial, you’ll learn the different methods available to reorder columns in the dataframe.

Sample Dataframe

Create a sample dataframe that you’ll use throughout this tutorial.

Column Order

1. Product_name
2. Unit_Price
3. No_Of_Units
4. Available_Quantity
5. Available_Since_Date

Code

import pandas as pd

data = {"Product_name":["Keyboard","Mouse", "Monitor", "CPU","CPU", "Speakers",pd.NaT],
        "Unit_Price":[500,200, 5000.235, 10000.550, 10000.550, 250.50,None],
        "No_Of_Units":[5,5, 10, 20, 20, 8,pd.NaT],
        "Available_Quantity":[5,6,10,"Not Available","Not Available", pd.NaT,pd.NaT],
        "Available_Since_Date":['11/5/2021', '4/23/2021', '08/21/2021','09/18/2021','09/18/2021','01/05/2021',pd.NaT]
       }

df = pd.DataFrame(data)

df

Dataframe Will Look Like

	Product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.000	5	5	11/5/2021
1	Mouse	200.000	5	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	Not Available	09/18/2021
4	CPU	10000.550	20	Not Available	09/18/2021
5	Speakers	250.500	8	NaT	01/05/2021
6	NaT	NaN	NaT	NaT	NaT

Now let’s see the different methods available to reorder the columns.

Using Reindex

You can change the order of the dataframe columns using the reindex() method.

• The reindex() method accepts columns as a list.
• Pass the columns as a list in the order of how you want to rearrange them.

Code

df = df.reindex(columns=['Available_Since_Date','Product_name', 'Unit_Price','No_Of_Units','Available_Quantity'])

df

Dataframe Will Look Like

The column Available_Since_Date is moved to the first position, as in the order specified for the columns attribute.

	Available_Since_Date	Product_name	Unit_Price	No_Of_Units	Available_Quantity
0	11/5/2021	Keyboard	500.000	5	5
1	4/23/2021	Mouse	200.000	5	6
2	08/21/2021	Monitor	5000.235	10	10
3	09/18/2021	CPU	10000.550	20	Not Available
4	09/18/2021	CPU	10000.550	20	Not Available
5	01/05/2021	Speakers	250.500	8	NaT
6	NaT	NaT	NaN	NaT	NaT

Using Dataframe Indexing

You can use the dataframe indexing to change the order of the columns.

• Use the column names in a specific order with df
• Create a new dataframe out of it to rearrange the columns.

Code

df = df[['Available_Since_Date','Product_name', 'Unit_Price','No_Of_Units','Available_Quantity']]

df

Dataframe Will Look Like

The dataframe columns are rearranged in the order passed for the dataframe indexing.

	Available_Since_Date	Product_name	Unit_Price	No_Of_Units	Available_Quantity
0	11/5/2021	Keyboard	500.000	5	5
1	4/23/2021	Mouse	200.000	5	6
2	08/21/2021	Monitor	5000.235	10	10
3	09/18/2021	CPU	10000.550	20	Not Available
4	09/18/2021	CPU	10000.550	20	Not Available
5	01/05/2021	Speakers	250.500	8	NaT
6	NaT	NaT	NaN	NaT	NaT

This is how you can rearrange columns using dataframe indexing.

Sorting Columns Alphabetically Using Reindex()

To sort the columns alphabetically using reindex(),

1. Fetch the existing columns for the dataframe using df.columns
2. Pass it to the sorted() method. It’ll sort the methods alphabetically
3. Pass the sorted value to the reindex() method
4. Specify axis=1 to denote that the column axes need to be reordered

Code

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

df

Dataframe Will Look Like

The dataframe columns are sorted alphabetically as below.

	Available_Quantity	Available_Since_Date	No_Of_Units	Product_name	Unit_Price
0	5	11/5/2021	5	Keyboard	500.000
1	6	4/23/2021	5	Mouse	200.000
2	10	08/21/2021	10	Monitor	5000.235
3	Not Available	09/18/2021	20	CPU	10000.550
4	Not Available	09/18/2021	20	CPU	10000.550
5	NaT	01/05/2021	8	Speakers	250.500
6	NaT	NaT	NaT	NaT	NaN

Sorting Columns Alphabetically Using Sort_Index()

The Sort_index() method is typically used to sort the index column. Either the row index or the column index.

To sort the column index,

• Pass axis=1 to denote that the column axes are to be renamed.

Snippet

df = df.sort_index(axis=1)

df

Dataframe Will Look Like

The columns of the dataframe are sorted alphabetically, as shown below.

	Available_Quantity	Available_Since_Date	No_Of_Units	Product_name	Unit_Price
0	5	11/5/2021	5	Keyboard	500.000
1	6	4/23/2021	5	Mouse	200.000
2	10	08/21/2021	10	Monitor	5000.235
3	Not Available	09/18/2021	20	CPU	10000.550
4	Not Available	09/18/2021	20	CPU	10000.550
5	NaT	01/05/2021	8	Speakers	250.500
6	NaT	NaT	NaT	NaT	NaN

This is how you can sort columns using the sort_index() alphabetical order.

Sorting Columns in Descending order

You can sort the columns in a descending alphabetical order using the sort_index() method.

To sort columns in descending order,

• pass axis=1 to denote that column axes must be reordered
• Pass ascending=False to denote that the columns must be sorted in descending order.

Code

df2 = df.sort_index(axis=1, ascending=False)

df2

Dataframe Will Look Like

	Unit_Price	Product_name	No_Of_Units	Available_Since_Date	Available_Quantity
0	500.000	Keyboard	5	11/5/2021	5
1	200.000	Mouse	5	4/23/2021	6
2	5000.235	Monitor	10	08/21/2021	10
3	10000.550	CPU	20	09/18/2021	Not Available
4	10000.550	CPU	20	09/18/2021	Not Available
5	250.500	Speakers	8	01/05/2021	NaT
6	NaN	NaT	NaT	NaT	NaT

Move Column to First Position

In this section, you’ll learn how to move a column to the first position.

There are no methods directly available to move a column to the first position.

1. Pop out the column to be moved using the pop() method
2. Insert the column into the 0th position using the insert() method.

This will move the column to the front position.

Code

column_to_reorder = df.pop('Unit_Price')

df.insert(0, 'Unit_Price', column_to_reorder)

df

Dataframe Will Look Like

By executing the above code, the column Unit_Price Column is shifted to the first position.

	Unit_Price	Product_name	No_Of_Units	Available_Since_Date	Available_Quantity
0	500.000	Keyboard	5	11/5/2021	5
1	200.000	Mouse	5	4/23/2021	6
2	5000.235	Monitor	10	08/21/2021	10
3	10000.550	CPU	20	09/18/2021	Not Available
4	10000.550	CPU	20	09/18/2021	Not Available
5	250.500	Speakers	8	01/05/2021	NaT
6	NaN	NaT	NaT	NaT	NaT

Move Column to Last Position

As similar to moving a column to the first position, there are no methods directly available to move a column to the last position.

1. Pop out the column to be moved using the pop() method
2. Find the length of the dataframe column using len(df. columns). Since position is 0 based, you can use the length to insert into the last position.
3. Insert the column into the len(df. columns) position using the insert() method.

This will move the column to the end position.

Code

column_to_reorder = df.pop('Unit_Price')

df.insert(len(df. columns), 'Unit_Price', column_to_reorder)

df

Dataframe Will Look Like

By executing the above code, the column Unit_Price Column is shifted to the last position.

	Product_name	No_Of_Units	Available_Since_Date	Available_Quantity	Unit_Price
0	Keyboard	5	11/5/2021	5	500.000
1	Mouse	5	4/23/2021	6	200.000
2	Monitor	10	08/21/2021	10	5000.235
3	CPU	20	09/18/2021	Not Available	10000.550
4	CPU	20	09/18/2021	Not Available	10000.550
5	Speakers	8	01/05/2021	NaT	250.500
6	NaT	NaT	NaT	NaT	NaN

Move Column To Specific Position

You can move the column to a specific position by popping out the column and inserting it into the specific position.

1. Define the position to which the column needs to be moved
2. Pop out the column to be moved using the pop() method
3. Insert the popped up column to the defined position using the insert() method.

Code

position =2

column_to_reorder = df.pop('Unit_Price')

df.insert(position, 'Unit_Price', column_to_reorder)

df

Dataframe Will Look Like

The column Unit_Price Column is shifted to the 3rd position because the index is 0 based while using the insert() method.

	Product_name	No_Of_Units	Unit_Price	Available_Since_Date	Available_Quantity
0	Keyboard	5	500.000	11/5/2021	5
1	Mouse	5	200.000	4/23/2021	6
2	Monitor	10	5000.235	08/21/2021	10
3	CPU	20	10000.550	09/18/2021	Not Available
4	CPU	20	10000.550	09/18/2021	Not Available
5	Speakers	8	250.500	01/05/2021	NaT
6	NaT	NaT	NaN	NaT	NaT

This is how you can move columns to either first, last, or specified position.

Change Order Like Other DataFrame

Until now, you’ve learned how to reorder the columns in the same dataframe using the same dataframe columns.

To reindex columns of a dataframe similar to the order of the dataframe,

• Use the method reindex_like() method.
• It’ll reorder the columns based on the order of the columns in the dataframe passed.

Code

df = df.reindex_like(df2)

df

Dataframe Will Look Like

The columns in the dataframe df will be reordered similar to the order of columns in dataframe df2.

	Unit_Price	Product_name	No_Of_Units	Available_Since_Date	Available_Quantity
0	500.000	Keyboard	5	11/5/2021	5
1	200.000	Mouse	5	4/23/2021	6
2	5000.235	Monitor	10	08/21/2021	10
3	10000.550	CPU	20	09/18/2021	Not Available
4	10000.550	CPU	20	09/18/2021	Not Available
5	250.500	Speakers	8	01/05/2021	NaT
6	NaN	NaT	NaT	NaT	NaT

This is how you can rename the columns of a dataframe based on the order of the columns from the other dataframe.

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