I have an html file which I open in notepad and read in python as list the html is done like this: I’m making a search on word “data” and from there I manipulate the string in order to get the data. Since I need to iterate this research and I don’t want to start everytime from the first “data”/”time”-row,
Tag: indexing
Nested dictionary incorrectly populating all top level key-value pairs with same values
I am having a strange issue where indexing a nested dictionary correctly results in the same value being assigned for all top level keys (k’s). Below is the whole chunk of code. Below is the output of this block: You can see by the print statements that after the first iteration of the middle loop, all top level keys have
Check to see if two lists have the same value at the same index, if so return the index. If not return -1
So basically I am trying to compare two lists to see if they hold the same value at the same index at any point. If they do I return the index, if they do not, I return -1. When I had first done this as a test I was having no issues however adding in the text has made it
Creating a list of positions of a substring within a string (DNA) (Python 3)
I am doing a bioinformatics course and I am trying to write a function to find all occurrences of a substring within a string. The output above should exactly match the following: [2, 4, 10] [2, 5, 6, 15, 17, 18] How can I fix this? Preferably without using regular expressions. Answer It looks like you badly indented the code,
SQLAlchemy How to create a composite index between a polymorphic class and it’s subclass
I am trying to get a composite index working between a polymorphic subclass and it’s parent. Alembic autogenerate does not seem to detect Indexes outside of __table_args__. I can’t use __table_args__ because, being in the subclass, it does not count my class as having a __table__. How do I create a composite Index between these? The goal is to have
how do I match and find a vector in a Numpy array?
I have a Numpy array with size [2000,6].now I have an array with size [1,6] and I want to know which row of the main Numpy array is the same as this [1,6] array. if it exists in the main array, return the index of the row. for example row 1. but I do not want to use for loops
find out if the indexes of a grouped data frame match a column of another dataframe?
I have a grouped data frame named df_grouped where AF & Local are the indexes. I would like to assert whether the indexes in df_grouped are equal to a column from another dataframe df[A]. This is an example of my code I tried this but it does not work: Answer To use assert for pandas series you can use assert_series_equal
How to enter new rows in a dataset by keeping same index
I am trying to enter into the following dataset: the rows: As you could see, the dataset created with the first chunk of code turns a with the first row marked with index 1. To maintain it, I used the following code to add two further rows where the new rows are added as follows at the last one of
Pandas data frame index
if I have a Series But, I need a standard index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], with index[4, 7, 8] values equal to zeros. So I expect the updated series will be How should I update the series? Thank you in advance! Answer Try this: Output:
Problems with DataFrame indexing with pandas
Using pandas, I have to modify a DataFrame so that it only has the indexes that are also present in a vector, which was acquired by performing operations in one of the df’s columns. Here’s the specific line of code used for that (please do not mind me picking the name ‘dataset’ instead of ‘dataframe’ or ‘df’): it worked, and