WebReplace the value by creating a list by looking up the value and assign to dataframe 1 column. df_1['Group'] = [dict_lookup[item] for item in key_list] Updated dataframe 1. Date Group Family Bonus 0 2011-06-09 Jamel Laavin 456 1 2011-07-09 Frank Grendy 679 2 2011-09-10 Luxy Fantol 431 3 2011-11-02 Frank Gondow 569 WebMar 17, 2024 · I have 2 dataframes, df1,and df2 as below. df1. and df2. I would like to lookup "result" from df1 and fill into df2 by "Mode" as below format. Note "Mode" has become my column names and the results have been filled into corresponding columns.
python - vlookup between 2 Pandas dataframes - Stack …
WebFeb 19, 2024 · I'd like to add two columns to an existing dataframe from another dataframe based on a lookup in the name column. And I'd like to take the height and weight from this dataframe (actually a json file) and add it based on matching Player names: existing_dataframe ['Height'] = pd.Series (height_weight_df ['Height']) WebOct 1, 2024 · Adding a single row to a dataframe requires copying the entire dataframe - so building up a dataframe one row at a time is an O(n^2) operation, and very slow. Also, Series.str.contains requires checking every single string value for whether it's contained. Since you're comparing every row to every other row, that too is an O(n^2) operation. how do i get background pictures
python - Mapping column values of one DataFrame to another DataFrame ...
Webnew <- df # create a copy of df # using lapply, loop over columns and match values to the look up table. store in "new". new [] <- lapply (df, function (x) look$class [match (x, look$pet)]) An alternative approach which will be faster is: new <- df new [] <- look$class [match (unlist (df), look$pet)] WebFeb 18, 2024 · You can think of it as dataframe = [1,2,3], array = [True, False, True], and match them up, then only take the value if it is True in the array. So, in this case it would be only "1" and "3". df_new = df.loc [df.apply (lambda row:True if row ["Date"] == "2024-03-27" and row ["Ticker"] == "AAPL" else False ,axis=1)] Share Improve this answer Follow WebNov 2, 2024 · for a similar task on my moderately powerful laptup, I used np.vectorize on a medium sized df (50k rows, 10 columns) and a large lookup table (4 mio rows of name-id pairs), and it worked almost instantaneously. however, on a much larger df it broke: Unable to allocate 17.8 TiB for an array with shape (3400599, 25) and data type how do i get bally sports in mn