This article is about understanding the error “AttributeError: 'numpy.ndarray' object has no attribute 'columns'“. We will learn about attribute errors, their causes in NumPy, and how to solve attribute errors in for NumPy.
Introduction
NumPy stands for “Numerical Python”. It is an open-source library of Python. It is the most basic library such that it frequently used by both beginner programmers and highly experienced researchers that state-of-the-art research. It is used immensely alongside the data science tools like Matplotlib, Pandas, scikit-learn, SciPy and many other data science packages.
The NumPy library comprises of the multidimensional array and matrix data structures. It proposes a ndarray, which is an n-dimensional array object of the same type of elements. NumPy is capable of being used for a lot of array-based mathematical operations.
A programmer can face several errors when using NumPy such as Index Error, Shape Error, Attribute Error, and Type Error. In this article, we are concerned with the Attribute Error. The topic is AttributeError: 'numpy.ndarray' object has no attribute 'columns'.
What are Numpy Arrays?
NumPy arrays as mentioned previously are multi-dimensional arrays that are provided by the NumPy library in Python for storing large arrays of data and are extensively used in numerical computations, scientific computing, and data analysis applications. The key characteristics of NumPy arrays are that they always consist of data of the same type and the arrays can range from 1-dimensional to n-dimensional. Also, they have a fixed size.
NumPy arrays are mainly used to represent large arrays of data, such as images, audio signals, or numerical data sets, and provide a very wide range of functions for performing mathematical operations on the data. And similar to NumPy, another data structure that is frequently used in data analysis is DataFrames which belongs to the Pandas library in Python. However, NumPy arrays do not provide structured or labeled data while DataFrames provide structured data with proper labels and also have the attribute ‘columns‘ which is missing in NumPy arrays.
Causes of the Error
The AttributeError is a type of error that occurs when an object does not have a particular attribute or method that is being called. It can also happen when an attribute cannot be accessed for some reason, such as if it is not defined, misspelled, or not accessible in the current context.
The attribute error has a lot of significance in Python because it helps programmers identify the issues with their code. By identifying the source of the error, programmers can troubleshoot this issue and ensure that their code runs smoothly and effectively.
The error “AttributeError: 'numpy.ndarray' object has no attribute 'columns'” that we are facing right now states that an Attribute Error was raised because the object 'numpy.ndarray' does not have any attribute that is named as 'column'. In our error, we already understand the term Attribute Error so moving forward the term attribute refers to properties or variables that hold information about the features of the concerned class. The numpy.ndarray is an object of the NumPy library in Python. It is a multi-dimensional array that stores arrays with the same types of data. Lastly, the term column just refers to a column.
The reason the error is raised is that this attribute is not present in the NumPy library instead this attribute is a part of the Pandas library. The attribute columns is a property of a DataFrame object which provides the data in the form of rows and columns.
This can usually happen when you assume that you are working with DataFrames instead of NumPy arrays or you could also import the wrong library by mistake thinking that you are working with DataFrames instead of NumPy arrays. An example is shown below.
import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr.columns)
The above code will give the following error:
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_1760\3630117670.py in
3 # create a NumPy array
4 arr = np.array([[1, 2], [3, 4]])
----> 5 print(arr.columns)
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
Solving the Error
There are different methods to resolve this error. The most common is that you can use indexing to obtain the values of the columns. You can also convert the NumPy arrays into Pandas DataFrames and then perform the column operations as you wish. Also, there are many other useful functions for manipulating columns that you could use. These include np.sum() np.transpose(), np.reshape(), and np.concatenate().
Now the implementation of the above methods is below. First, look at how to convert a NumPy array into a DataFrame.
import numpy as np
import pandas as pd
arr = np.array([[1, 2], [3, 4]])
# Convert the NumPy array to a Pandas DataFrame
df = pd.DataFrame(arr)
print(df)
The method to obtain a column by indexing or slicing is as follows:
import numpy as np
arr = np.array([[1, 2], [3, 4]])
# Access the First Column
col1 = arr[0:2]
print(col1)Best Practices to Avoid Error
There are several ways to avoid making this error. These methods are listed below:
- Make sure the type of data structure you are working with. It can be either a NumPy array or DataFrame. You can check this using the
type()function. - If you want to perform many column operations then make sure to convert your NumPy array object into a DataFrame object.
- If you want to obtain individual columns with a NumPy array then do so using indexing instead of calling the
columnattribute. - You can also perform exception handling so that you can tackle an error better. An example is shown below:
try: import numpy as np arr = np.array([[1, 2], [3, 4]] print(arr.column) except AttributeError: print("An attribute was used which is not a part of NumPy") - You can also refer to the NumPy documentation to obtain a better understanding of handling columns in a NumPy array.
The Conclusion
In conclusion the error “AttributeError: 'numpy.ndarray' object has no attribute 'columns'” is caused because there is an attribute named column for NumPy array instead, it is an attribute of Pandas DataFrame. Therefore, to resolve this error we can either convert our current array into a DataFrame or we can also using indexing to obtain each column and then perform operations on them. And it is best to avoid this error by making sure what type of data you are handling.
The References
- To refer to NumPy documentation, please use the below link:
https://numpy.org/doc/