Question

Question

Question

Question

I have a dictionary of about 600 dataframes for connectome metrics for those 600 participants. I’m making a dummy dictionary though for conciseness. The dataframes all have 1 row. Ignore the fact that the actual meaning of “mean” as the column names are not what is of the row samples I give below, they’re just the column names of all the dataframes I want to merge. Each of the keys are unique.

I have a dictionary of about 600 dataframes for connectome metrics for those 600 participants. I’m making a dummy dictionary though for conciseness. The dataframes all have 1 row. Ignore the fact that the actual meaning of “mean” as the column names are not what is of the row samples I give below, they’re just the column names of all the dataframes I want to merge. Each of the keys are unique.

The function I am using to convert my data frame into a nested dictionary strips the column names from the hierarchy, making the dictionary diffictult to naviagte.

I have three columns in a pandas dataframe A, B, and C. I also have 3 dictionaries, dict_A, dict_B, dict_C that have all values of A, B, and C as keys in each respective dataframe. I am trying to create 2 new columns in the dataframe that use the data in these dictionaries as part of a calculation. This is what I tried

Lets say I have a data frame lets call it A

I am trying to create a pandas multi-indexed DataFrame from a double-nested dictionary but am quite stumped as to how to do this.