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.

Labels:

Comments

Post a Comment

Popular posts from this blog

Tinder bio generation with OpenAI GPT-3 API

Introduction: Recently I got access to OpenAI API beta. After a few simple experiments, I set on creating a simple test project. In this project, I will try to create good tinder bio for a specific person.  The abc of openai API playground: In the OpenAI API playground, you get a prompt, and then you can write instructions or specific text to trigger a response from the gpt-3 models. There are also a number of preset templates which loads a specific kind of prompt and let's you generate pre-prepared results. What are the models available? There are 4 models which are stable. These are: (1) curie (2) babbage (3) ada (4) da-vinci da-vinci is the strongest of them all and can perform all downstream tasks which other models can do. There are 2 other new models which openai introduced this year (2021) named da-vinci-instruct-beta and curie-instruct-beta. These instruction models are specifically built for taking in instructions. As OpenAI blog explains and also you will see in our
Post a Comment
Read more

Can we write codes automatically with GPT-3?

 Introduction: OpenAI created and released the first versions of GPT-3 back in 2021 beginning. We wrote a few text generation articles that time and tested how to create tinder bio using GPT-3 . If you are interested to know more on what is GPT-3 or what is openai, how the server look, then read the tinder bio article. In this article, we will explore Code generation with OpenAI models.  It has been noted already in multiple blogs and exploration work, that GPT-3 can even solve leetcode problems. We will try to explore how good the OpenAI model can "code" and whether prompt tuning will improve or change those performances. Basic coding: We will try to see a few data structure coding performance by GPT-3. (a) Merge sort with python:  First with 200 words limit, it couldn't complete the Write sample code for merge sort in python.   def merge(arr, l, m, r):     n1 = m - l + 1     n2 = r- m       # create temp arrays     L = [0] * (n1)     R = [0] * (n
Post a Comment
Read more

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
Post a Comment
Read more