eda | Material escrito para a disciplina de Estruturas de Dados e | Dataset library

Python beginner here...

Trying to understand how to use OneHotEncoder from the sklearn.preprocessing library. I feel pretty confident in using it in combination with fit_transform so that the results can also be fit to the test dataframe. Where I get confused is what to do with the resulting encoded array. Do you then convert the ohe results back to a dataframe and append it to the existing train/test dataframe?

The ohe method seems a lot more cumbersome than the pd.get_dummies method, but from my understanding using ohe with fit_transform makes it easier to apply the same transformation to the test data.

Searched for hours and having a lot of trouble trying to find a good answer for this.

Example with the widely used Titanic dataset:

I'm building a mobile app, I want to set my images from firestore-collections to my react-native-image-gallery. I already get my all link of images from firestore, but I couldn't solve how to set them to my image-gallery. My react-native-image-gallery codes with images are:

I am new to pandas and I am trying to carry out some EDA on my twitter dataset. Dataset column

Link to Dataset : https://www.kaggle.com/kaushiksuresh147/the-social-dilemma-tweets

Dataframe Sample : Sample dataframe

I want to filter new users created (from the user_created column) between "2020-09-08 and 2020-09-22" and then group the results with the sentiment column. I also want to count the total number of tweets created from this new users within that period and compare it with the overall number of tweets from other users which are not in the selected range(2020-09-08 and 2020-09-22).

I have tried an approach and my code keeps giving me the error message : KeyError: 'user_created'code snippet

I also tried this code which also gives me error message:KeyError: 'user_created'2nd code

I am using EDA Playground with Aldec Riviera simulator, and I have this module here:

I'm trying to write an I2C Slave and test it in isolation.

I have a simulation that should be pulling SDA low when write_ack is high (Also highlighted by the red dots). However, you can see that SDA remains the same.

Part of me thinks it's to do with the way I'm testing with the force methods and the delays.

Any help appreciated.

I have found the keyword release which seems to help.

Code below & EDA Playground is here: https://edaplayground.com/x/6snM

I am writing a query to pull a list of student enrollments, and creating a virtual column with logic to assign students with various academic plans to communication groups (comm_group). The source view I've been provided to work with pulls one record for EACH academic plan for a student with ANY enrollment in our department. As a result, there are records of enrollments that have nothing to do with our department, because OTHER enrollments that in our department exist. I could just filter out those rows, but I would like to double-check the logic in my virtual column by finding any students who have ALL null values in the comm_group column. That would indicate that I missed some plan codes somewhere. Here's some sample data:

User 3 has an enrollment in EDC, so should have a value for that column for COMM_GROUP for that row. This means I have left out EDC from my case statements in my virtual column. I would like to identify all such errors by selecting finding all users who ONLY have NULL values.

I'm almost there, but I'm missing something. My code looks like this right now:

I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:

first_job <- function(x) tail(x[!is.na(x)], 1)

first_job <- apply(data, 1, first_job)

I'm doing a sentiment analysis on the IMDB dataset in tensorflow and I'm trying to augment the training dataset by using the textaugment library which they said is 'plug and play' into tensorflow. So it should be rather simple, but I'm new to tf so I'm not sure how to go about doing that. Here is what I have and what I am trying, based on reading the tutorials on the site.

I tried to do a map to augment the training data but I got an error. You can scroll down to the last code block to see the error.

I wanted to create a weibull probability plot using Bokeh. Based on the reference (linked below),

https://www.itl.nist.gov/div898/handbook/eda/section3/weibplot.htm

The y-axis of a weibull probability plot has an axes with scale: ln(-ln(1-p)). Let's say that I have defined a function (with it's inverse function),