Effortlessly Reorder Pandas Columns: A Beginner's Guide

Effortlessly Reorder Pandas Columns: A Beginner's Guide

MoeNagy Dev

Importance of Reordering Columns in Pandas

Reordering columns in Pandas DataFrames is an essential skill for data analysts and researchers. It can enhance data analysis and visualization, improve the readability and organization of datasets, and align data for specific use cases.

By rearranging the order of columns, you can:

  • Enhance data analysis and visualization by grouping related columns together, making it easier to understand and interpret the data.
  • Improve the readability and organization of your datasets, making it simpler to navigate and understand the structure of the data.
  • Align data for specific use cases, such as preparing data for machine learning models or creating customized reports.

Understanding Column Order in Pandas DataFrames

In Pandas, the order of columns in a DataFrame is determined by the order in which the columns are created or added to the DataFrame. When you create a new DataFrame, the columns are typically ordered in the same order as they were provided during the creation process.

You can access the column order of a DataFrame using the columns attribute:

import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# Display the column order
# Output: Index(['A', 'B', 'C'], dtype='object')

Reordering Columns Using Built-in Methods

Pandas provides several built-in methods for reordering columns in a DataFrame.


The reindex() method allows you to reorder the columns by specifying a new order in the columns parameter. This method preserves the original data types of the columns.

# Reorder the columns
new_order = ['C', 'A', 'B']
df_reordered = df.reindex(columns=new_order)
#    C  A  B
# 0  7  1  4
# 1  8  2  5
# 2  9  3  6

If you specify a column that doesn't exist in the original DataFrame, reindex() will add a new column with NaN values.


You can also reorder the columns by selecting them in the desired order using the column names in square brackets.

# Reorder the columns
new_order = ['C', 'A', 'B']
df_reordered = df[new_order]
#    C  A  B
# 0  7  1  4
# 1  8  2  5
# 2  9  3  6

This method is more concise than using reindex(), but it doesn't handle missing columns in the new order.

Advanced Reordering Techniques

Reordering based on column names

You can reorder columns based on their names, either in alphabetical order or a specific order defined by a list.

# Reorder columns alphabetically
df_alphabetical = df[sorted(df.columns)]
#    A  B  C
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9
# Reorder columns in a specific order
specific_order = ['B', 'C', 'A']
df_specific_order = df[specific_order]
#    B  C  A
# 0  4  7  1
# 1  5  8  2
# 2  6  9  3

Reordering based on column data types

You can group columns by their data types and rearrange the order of the data types.

# Reorder columns by data type
df_by_dtype = df.reindex(sorted(df.columns, key=lambda x: (df[x].dtype, x)), axis=1)
#    A  B  C
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9

In this example, the columns are first sorted by their data types, and then by their names (as a secondary sort criterion).

Reordering based on column statistics

You can reorder columns based on specific statistics, such as the minimum, maximum, or mean values of the columns.

# Reorder columns by minimum value
df_by_min = df.reindex(df.min().sort_values().index, axis=1)
#    A  B  C
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9

In this example, the columns are reordered based on the minimum value of each column, as determined by df.min().sort_values().index.

Conditional Reordering of Columns

You can reorder columns based on specific conditions, such as selecting columns that match a pattern or excluding columns that meet certain criteria.

# Reorder columns that contain the letter 'A'
df_with_A = df[df.columns[df.columns.str.contains('A')]]
#    A
# 0  1
# 1  2
# 2  3
# Reorder columns excluding those that contain the letter 'A'
df_without_A = df[df.columns[~df.columns.str.contains('A')]]
#    B  C
# 0  4  7
# 1  5  8
# 2  6  9

You can combine conditional reordering with the built-in reordering methods, such as reindex() or column selection with square brackets.

Preserving Original Column Order

If you need to reset the column order to the initial state, you can use the reindex() method with the original column order.

# Reset the column order to the original state
df_original_order = df.reindex(df.columns, axis=1)
#    A  B  C
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9

This ensures that the column order is restored to the initial state, even if the order was modified during the data processing workflow.

Reordering Columns in Multi-level Columns

Pandas also supports multi-level column structures, where each column has a hierarchical header. You can apply the reordering techniques discussed earlier to individual levels of the multi-level column structure.

# Create a DataFrame with multi-level columns
df_multilevel = pd.DataFrame({
    ('level1', 'A'): [1, 2, 3],
    ('level1', 'B'): [4, 5, 6],
    ('level2', 'C'): [7, 8, 9]
# Reorder the columns by the first level
df_reordered_multilevel = df_multilevel[sorted(df_multilevel.columns.get_level_values(0))]
#    (level1, A)  (level1, B)  (level2, C)
# 0           1           4            7
# 1           2           5            8
# 2           3           6            9

In this example, the columns are reordered based on the first level of the multi-level column structure.

Conditional Statements

Conditional statements in Python allow you to execute different blocks of code based on certain conditions. The most common conditional statements are if, elif, and else.

age = 25
if age >= 18:
    print("You are an adult.")
    print("You are a minor.")

In this example, if the age variable is greater than or equal to 18, the code block under the if statement will be executed. Otherwise, the code block under the else statement will be executed.

You can also use the elif statement to check for multiple conditions:

score = 85
if score >= 90:
    print("You got an A.")
elif score >= 80:
    print("You got a B.")
elif score >= 70:
    print("You got a C.")
    print("You failed.")


Loops in Python allow you to repeatedly execute a block of code. The two most common loop types are for and while.

For Loops

for loops are used to iterate over a sequence (such as a list, tuple, or string).

fruits = ["apple", "banana", "cherry"]
for fruit in fruits:

This will output:


You can also use the range() function to create a sequence of numbers to iterate over:

for i in range(5):

This will output:


While Loops

while loops are used to execute a block of code as long as a certain condition is true.

count = 0
while count < 5:
    count += 1

This will output:



Functions in Python are blocks of reusable code that perform a specific task. They can take arguments and return values.

def greet(name):
    print(f"Hello, {name}!")

This will output:

Hello, Alice!
Hello, Bob!

You can also define functions with default arguments and variable-length arguments:

def calculate_area(length, width, height=None):
    if height:
        return length * width * height
        return length * width
print(calculate_area(5, 3))       # Output: 15
print(calculate_area(4, 2, 6))    # Output: 48

Modules and Packages

Python's standard library provides a wide range of built-in modules, and you can also create your own modules and packages.

import math
print(math.pi)    # Output: 3.141592653589793

You can also import specific functions or attributes from a module:

from math import sqrt
print(sqrt(16))   # Output: 4.0

Packages are collections of modules, and they help organize your code into a hierarchical structure.


Exception Handling

Python's exception handling mechanisms allow you to handle errors and unexpected situations in your code.

    result = 10 / 0
except ZeroDivisionError:
    print("Error: Division by zero.")

You can also use except blocks to handle multiple exception types and include an optional else and finally block.

    num = int(input("Enter a number: "))
    print(10 / num)
except ValueError:
    print("Error: Invalid input. Please enter a number.")
except ZeroDivisionError:
    print("Error: Division by zero.")
    print("The operation was successful.")
    print("This block will always execute.")

File I/O

Python provides built-in functions for reading from and writing to files.

# Writing to a file
with open("example.txt", "w") as file:
    file.write("Hello, world!")
# Reading from a file
with open("example.txt", "r") as file:
    content =
    print(content)  # Output: Hello, world!

The with statement ensures that the file is properly closed after the operations are completed.

Object-Oriented Programming (OOP)

Python supports object-oriented programming, which allows you to create custom classes and objects.

class Car:
    def __init__(self, make, model, year):
        self.make = make
        self.model = model
        self.year = year
    def start(self):
        print(f"The {self.year} {self.make} {self.model} is starting.")
my_car = Car("Toyota", "Camry", 2020)
my_car.start()  # Output: The 2020 Toyota Camry is starting.

In this example, we define a Car class with an __init__ method to initialize the object's attributes, and a start method to perform an action.


In this tutorial, we've covered a wide range of Python concepts, including conditional statements, loops, functions, modules and packages, exception handling, file I/O, and object-oriented programming. These topics are essential for building robust and efficient Python applications. Remember to practice and experiment with the code examples provided to solidify your understanding of these concepts. Happy coding!

MoeNagy Dev