Skip to main content

Pandas errors and their solutions


Problem 1: Grouper for is not 1-dimensional

I was writing a nice little script in pandas where I wanted to group some data using some identifier columns. And then suddenly the error popped in the console was: Grouper for Id is not 1-dimensional. And I started investigating for it.

Solution:

This error comes if there are multiple copies of the same grouper column. In that case, you will find this error.

grouper for 'bar' is not 1-dimensionalgrouper for bar is not 1-dimensional: source: stackoverflow

Problem 2: unhashable type: numpy.ndarray

This is a more generalized type of error. There are multiple instances where this error occurs; including when you try to treat a numpy ndarray object as a value or a string type object and so on. I encountered this problem when I tried to group a bunch of values in which a column contained arrays as entries. If your pandas dataframe contains array or np.ndarray as values of single cells, then this dataframe can not be further used for other different actions even like drop_duplicates. But this is not easily visible. Often array will be entered as the value in your dataframe cells by mistake and then on later lines of codes, you will find this error.
The solution is here to find out where did you enter the array values in the dataframe and then turning the array into a single integer or string type object. i.e. if your cell value is currently array([0.628]) then you should use array.values[0] which will give you 0.628. So this is how you can solve this problem. Other types of problems, i.e. normally trying to take array[0] from a n-dimensional array or some more explicit errors also can lead to this issue. Here is a link to stack exchange for the same issue.

Comments

Popular posts from this blog

Mastering SQL for Data Science: Top SQL Interview Questions by Experience Level

Introduction: SQL (Structured Query Language) is a cornerstone of data manipulation and querying in data science. SQL technical rounds are designed to assess a candidate’s ability to work with databases, retrieve, and manipulate data efficiently. This guide provides a comprehensive list of SQL interview questions segmented by experience level—beginner, intermediate, and experienced. For each level, you'll find key questions designed to evaluate the candidate’s proficiency in SQL and their ability to solve data-related problems. The difficulty increases as the experience level rises, and the final section will guide you on how to prepare effectively for these rounds. Beginner (0-2 Years of Experience) At this stage, candidates are expected to know the basics of SQL, common commands, and elementary data manipulation. What is SQL? Explain its importance in data science. Hint: Think about querying, relational databases, and data manipulation. What is the difference between WHERE

What is Bort?

 Introduction: Bort, is the new and more optimized version of BERT; which came out this october from amazon science. I came to know about it today while parsing amazon science's news on facebook about bort. So Bort is the newest addition to the long list of great LM models with extra-ordinary achievements.  Why is Bort important? Bort, is a model of 5.5% effective and 16% total size of the original BERT model; and is 20x faster than BERT, while being able to surpass the BERT model in 20 out of 23 tasks; to quote the abstract of the paper,  ' it obtains performance improvements of between 0 . 3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. ' So what made this achievement possible? The main idea behind creation of Bort is to go beyond the shallow depth of weight pruning, connection deletion or merely factoring the NN into different matrix factorizations and thus distilling it. While methods like knowle

Spacy errors and their solutions

 Introduction: There are a bunch of errors in spacy, which never makes sense until you get to the depth of it. In this post, we will analyze the attribute error E046 and why it occurs. (1) AttributeError: [E046] Can't retrieve unregistered extension attribute 'tag_name'. Did you forget to call the set_extension method? Let's first understand what the error means on superficial level. There is a tag_name extension in your code. i.e. from a doc object, probably you are calling doc._.tag_name. But spacy suggests to you that probably you forgot to call the set_extension method. So what to do from here? The problem in hand is that your extension is not created where it should have been created. Now in general this means that your pipeline is incorrect at some level.  So how should you solve it? Look into the pipeline of your spacy language object. Chances are that the pipeline component which creates the extension is not included in the pipeline. To check the pipe eleme