I have 2 Data Frames, one named USERS and another named EXCLUDE. Both of them have a field named “email”. Basically, I want to remove every row in USERS that has an email contained in EXCLUDE. How can I do it? Answer You can use boolean indexing and condition with isin, inverting boolean Series is by ~: Another solution with
Tag: merge
pandas – Merging on string columns not working (bug?)
I’m trying to do a simple merge between two dataframes. These come from two different SQL tables, where the joining keys are strings: I try to merge them using this: The result of the inner join is empty, which first prompted me that there might not be any entries in the intersection: But when I try to match a single
How to merge two dataframe in pandas to replace nan
I want to do this in pandas: I have 2 dataframes, A and B, I want to replace only NaN of A with B values. Answer The official way promoted exactly to do this is A.combine_first(B). Further information are in the official documentation. However, it gets outperformed massively with large databases from A.fillna(B) (performed tests with 25000 elements):
Python Pandas merge only certain columns
Is it possible to only merge some columns? I have a DataFrame df1 with columns x, y, z, and df2 with columns x, a ,b, c, d, e, f, etc. I want to merge the two DataFrames on x, but I only want to merge columns df2.a, df2.b – not the entire DataFrame. The result would be a DataFrame with
How to merge dictionaries of dictionaries?
I need to merge multiple dictionaries, here’s what I have for instance: With A B C and D being leaves of the tree, like {“info1″:”value”, “info2″:”value2”} There is an unknown level(depth) of dictionaries, it could be {2:{“c”:{“z”:{“y”:{C}}}}} In my case it represents a directory/files structure with nodes being docs and leaves being files. I want to merge them to obtain: