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How to search for a value anywhere in a pandas Dataframe?
The question is: if I want to search for a value somewhere in my df (I don’t know which column it’s in) and return all rows with a match. What’s the most Pandaic way to do it?
How to join pandas DataFrames by column names?
If that is the case then the following will do that (the above will in effect do a many to many merge) for your information, in pandas left join breaks when the right frame has non unique values on the joining column. see this bug. Thanks for contributing an answer to Stack Overflow!
Where are the index and columns in a pandas query?
The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query.
How to evaluate a pandas Dataframe query expression?
DataFrame resulting from the provided query expression or None if inplace=True. Evaluate a string describing operations on DataFrame columns. Evaluate a string describing operations on DataFrame columns.
How to get pandas to read column as string?
As easier way is to force Pandas to read the column as a Python object (dtype) The O is used for Python objects including lists. More info here. Please note that this method fails if you parse empty list strings: ” []” Alternatively you can also apply a function to your column (this one is for integers):
When to insert a list into a cell in pandas?
Creating a fake class to do this might look like a hassle but it can have some practical applications. For an example you can use this with apply when the return value is list. Pandas would normally refuse to insert list into a cell but if you use this method, you can force the insert. I prefer .at and .loc.
How to convert pandas Dataframe stored list as string?
To answer your second question, you can convert it back with ast.literal_eval: This will read that column as a it’s corresponding dtype in python instead of a string. I just came across this problem and there is a very simple solution ( pandas.eval () ).