I have a very large data frame in python and I want to drop all rows that have a particular string inside a particular column. For example, I want to drop all rows which have the string “XYZ” as a substring in the column C of the data frame. Can this be implemented in an … Read more
I have a pandas DataFrame with indices I want to sort naturally. Natsort doesn’t seem to work. Sorting the indices prior to building the DataFrame doesn’t seem to help because the manipulations I do to the DataFrame seem to mess up the sorting in the process. Any thoughts on how I can resort the indices naturally?
How should I transform from datetime to string? My attempt:
I have trouble querying a table of > 5 million records from MS SQL Server database. I want to select all of the records, but my code seems to fail when selecting to much data into memory.
In Python Pandas, what’s the best way to check whether a DataFrame has one (or more) NaN values?
Can someone point me to a link or provide an explanation of the benefits of indexing in pandas? I routinely deal with tables and join them based on columns, and this joining/merging process seems to re-index things anyway, so it’s a bit cumbersome to apply index criteria considering I don’t think I need to.
This question is specific to using flatten_json from GitHub Repo: flatten The package is on pypi flatten-json 0.1.7 and can be installed with pip install flatten-json This question is specific to the following component of the package: def flatten_json(nested_json: dict, exclude: list=[”], sep: str=’_’) -> dict: “”” Flatten a list of nested dicts. “”” out … Read more
I want to go from this data frame which is basically one hot encoded.
I have a dataframe like this one:
Why do we use ‘loc’ for pandas dataframes? it seems the following code with or without using loc both compile anr run at a simulular speed