How to solve Pandas/Numpy error in Python?
I was trying to retrieve all the 4 digit characters under column A from an excel file.
Add new rows to dataset in nested for loop
The following script works when data4
is used to create dataframe, hours_date_match_barc
. It breaks when I want to use data3
and I believe it is because it is not able to add more rows to out
.
can someone help me with the data report?
data report instructions
https://docs.google.com/document/d/1-ldgUP3uZtUiHhdrM9JxqWcXmtbOjhmCK2JPSpx7Rxc/edit?usp=sharing
I have a smoothing loop that just isn’t setting values with .loc and I don’t understand it at all
This is the code
How to save single Random Forest model with cross validation
I am using 10 fold cross validation, trying to predict binary labels (Y) based on the embedding inputs (X).
I want to save one of the models (perhaps the one with the highest ROC AUC). I’m not sure how to do it because the ROC AUCs are not stored and I don’t know how to grab accordingly.
Number signs issue Python
The issue with number signs. When running the same code, it sometimes returns the correct signs for the numbers, but in the next run, it might return the correct absolute values, but completely random signs for the numbers. What could be the problem?
Scalable approach instead of apply in python
I use apply to loop the rows and get the column names of feat1, feat2 or feat3 if they are equal to 1 and scored is equal to 0. The column names are then inserted into a new feature called reason.
Filter a dataframe based on another dataframe
I have 2 dataframes:
How to set max row height in pandas
One column in my Pandas dataframe is too long. How can I set max row height (not number of rows in a dataframe) in pandas so that I can truncate the cells with too much content and have a balanced view of all columns?
Given a pandas DataFrame with several columns containing NaNs. The goal is to efficiently find the first non-null value for each row
Given a pandas DataFrame with several columns containing potentially missing values(NaN).
The goal is to efficiently find the first non-null value for each row.
A similar Question using polars Dataframe and solution is here :/q/77401947/6037956