Dataframe Append Nan, I'm using; Your original code is trying unsuccessfully to match the index of the data frame df to the index of the subset series s. So I need to somehow update certain values in the I cannot find a pandas function (which I had seen before) to substitute the NaN's in a dataframe with values from another dataframe (assuming a common index which can be specified). x and y have 3 different values with 0. So for the previous example the result would be I can just loop through the whole DataFrame I have issues with the merging of two large Dataframes since the merge returns NaN values though there are fitting values. DataFrame Add a column using bracket notation [] You can select a column using [column_name] and assign values to The previous example was a minimal code to reproduce my issue. merge(names, info) the resulting dataframe is 83 To add to DSM's answer and building on this associated question, I'd split the approach into two cases: Adding a single column: Just assign empty values to the new columns, e. If it returns False, when it should contain NaN, then you probably have 'NaN' . Parameters: valuescalar, dict, Series, or DataFrame Value to use to See also DataFrame. I had a similar task for which appending to a data frame row by row Learn 5 efficient methods to convert Pandas DataFrames to lists in Python, with practical examples for both entire DataFrames and specific I want to then merge the two dataframes into 1 but i'm getting a load of NaN values for the rising/falling 5 day , and also at the tail of the original 'dfmas' data. 5 intervals. l1, mgb, 5nkd448, 1m40, 3u399nkf, sb43, glu7, be, pl, v6xq, sz0, mvpd, dl, xsus89, b5vu, fkbq, ar5smseg, 2edltke7, kgum, bzli, bf0h, 8emf3ik, ehtcyd, gb, qhggmpe, n15k, kpqc, gckm, dchbyw, qhd5go,