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Comprehensive Tutorial: Viewing All Columns in Pandas

In this tutorial, we will explore various ways to view all columns in a Pandas DataFrame.

Pandas is a powerful data manipulation library for Python that provides flexible and efficient tools for working with structured data. It provides easy-to-use data structures like DataFrame and Series, along with a wide range of functions to perform data manipulation, analysis, and exploration tasks.

In many cases, when working with large datasets, it becomes important to examine all the columns present in the DataFrame. Let’s dive into the different methods to accomplish this.

Table of Contents

  1. Introduction to Pandas
  2. Dataset
  3. Method 1: Using print(df.columns)
  4. Method 2: Using df.head()
  5. Method 3: Using
  6. Method 4: Using df.describe()
  7. Method 5: Using a Loop
  8. Conclusion

Introduction to Pandas

Pandas is an open-source library that provides data manipulation and analysis tools for Python. It is built on top of NumPy and offers easy-to-use data structures and data analysis capabilities.


To demonstrate the different methods of viewing all columns, we will create a sample DataFrame with fictional data. Consider the following example:

import pandas as pd
data = {
'name': ['John', 'Jane', 'Mike'],
'age': [28, 35, 42],
'city': ['New York', 'London', 'Paris'],
'salary': [50000, 70000, 60000]
df = pd.DataFrame(data)

Here, we have created a DataFrame with four columns: name, age, city, and salary. Let’s proceed with the methods to view all these columns.

Method 1: Using print(df.columns)

The simplest way to view all columns in a Pandas DataFrame is by accessing the columns attribute of the DataFrame and printing it. Here is how you can do it:


This will produce the following output:

Index(['name', 'age', 'city', 'salary'], dtype='object')

The output displays all column names present in the DataFrame.

Method 2: Using df.head()

Another way to view all columns is by using the head() function. This function displays the first few rows of the DataFrame, including all the columns. Here is an example:


This will output the following:

name age city salary
0 John 28 New York 50000
1 Jane 35 London 70000
2 Mike 42 Paris 60000

The output shows all columns with their values for the first five rows in the DataFrame. If there are more than five rows, only the first five will be shown.

Method 3: Using

The info() function provides informative summary statistics about a DataFrame. It includes the data types, non-null values, and memory usage of each column. Let’s see how to use it:


The output will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 3 non-null object
1 age 3 non-null int64
2 city 3 non-null object
3 salary 3 non-null int64
dtypes: int64(2), object(2)
memory usage: 224.0+ bytes

The output provides comprehensive information about the DataFrame, including column names, non-null counts, data types, and memory usage.

Method 4: Using df.describe()

The describe() function generates descriptive statistics of the DataFrame’s numeric columns. Though it primarily focuses on statistical information, it also outputs the column names. Let’s see an example:


The output will be:

age salary
count 3.000000 3.000000
mean 35.000000 60000.000000
std 7.211103 1000.000000
min 28.000000 50000.000000
25% 31.500000 55000.000000
50% 35.000000 60000.000000
75% 38.500000 65000.000000
max 42.000000 70000.000000

This output provides statistical information like count, mean, standard deviation, minimum value, and maximum value for each numeric column, along with their names.

Method 5: Using a Loop

In scenarios where you want to iterate through all columns of a DataFrame and perform some operation on each column, you can use a loop. Here is an example:

for column in df.columns:

This will output the following:


You can replace the print(column) statement with your desired logic to perform operations on each column.


In this tutorial, we explored various ways to view all columns in a Pandas DataFrame. We covered five methods: using print(df.columns), df.head(),, df.describe(), and a loop. Each method provides a different perspective or level of detail about the columns. You can choose the method that best suits your needs based on the information you require. Happy coding!