Advertisements I am trying to store coefficients from a mle model just like a normally do for other models. Surprisingly, it throws an error. Any other methods to store it? set.seed(111) y.size <- rnorm(100,4,1) p <- rnorm(100,5,1) df <- data.frame(p, y.size) glae <- function(A,K,Ka,q, sigma, c) { lnqp <- if(q == 0) log(p) else ((p^q… Read More How to get coefficients from a model in R?
Advertisements As far as I understand .NET concepts, the source code is first translated into an Assembly (IL code+meta data), which is compiled to machine code on the target machine with the CLR’s JIT compiler. However I cannot really match these concepts to what I see when working on a C# project. When I build… Read More Understanding .NET compilation
Advertisements Someone on this platform has already helped me with generating the following code: col,row = (100,1000) a = np.random.uniform(0,10,size=col*row).round(6).reshape(row,col) mask = (a*1e6+1).astype(int)%10<2 a[mask] += 2e-6 This code ensures that the last decimal is not a 0 or a 9. However, I would like to have no 0’s nor 9’s in all the decimals that… Read More Replace decimals in floating point numbers
Advertisements What I want here is for the Nans to ultimately be integer values. Since my dataset is 1000s of columns, I can’t just change a couple of columns to make them integer and when I tried df = df.astype(‘int’) in Dask, after changing the values to 0 floats, for whatever reason, it didn’t work.… Read More Filling NAs to string 0 and then to int reverts to float
Advertisements I have a pearson correlation heat map coded, but its showing data from my dataframe which i dont need. is there a way to specify which columns i’d like to include? thanks in advance sb.heatmap(df[‘POPDEN’, ‘RoadsArea’, ‘MedianIncome’, ‘MedianPrice’, ‘PropertyCount’, ‘AvPTAI2015’, ‘PTAL’].corr(), annot=True, fmt=’.2f’) ————————————————————————— TypeError Traceback (most recent call last) <ipython-input-54-832fc3c86e3e> in <module> —->… Read More how to specify data on pearson correlation heatmap?
Advertisements I have a dataframe column like this: df[‘col_name’].unique() >>>array([-1, ‘Not Passed, On the boundary’, 1, ‘Passed, On the boundary’, ‘Passed, Unclear result’, ‘Passes, Unclear result, On the boudnary’, ‘Rejected, Unclear result’], dtype=object) In this column, if an element contains the word ‘Passed’ as a field or as a substring, then replace the entire field… Read More Dataframe conditional replacement with intigers
Advertisements I have panel data and I would like to get the percentage of observations in a column (Size) that are below 1 million. My data is the following: structure(list(Product = c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "C", "C", "C", "C", "C", "C"), Date = c("02.05.2018", "04.05.2018", "05.05.2018", "06.05.2018",… Read More R: Calculate percentage of observations in a column that are below a certain value for panel data
Advertisements My code works but it doesn’t bring the header with the names, it only brings the numbers 0 1 … 10 , what can I do ? Utils_db.py def consulta_sql(sql): try: connection = psycopg2.connect(user="postgres", password="postgres", host="localhost", port="5432", database="tb_cliente") cursor = connection.cursor() except (Exception, psycopg2.Error) as error: try: cursor.execute(sql) connection.commit() except (Exception, psycopg2.Error) as error:… Read More Export Postgresql Table to excel with header in Python
Advertisements I have a df (dataframe A) with over 200k rows with incorrect ids scattered throughout a single column (id). I have a another df (dataframe B) with all the incorrect ids of dataframe A matched with corrected ids. How can I use dataframe B to fix the errors in the id column of dataframe… Read More R: Find and replace character in one df col with character from another df
Advertisements Suppose I have a groupby object (grouped on Col1) like below: Col1 Col2 Col3 Col4 Col5 —————————————- AAA 001 456 846 239 row1 002 374 238 904 row2 003 456 846 239 row3 BBB 001 923 222 398 row1 002 923 222 398 row2 003 755 656 949 row3 CCC 001 324 454 565… Read More Check for duplicate rows for a subset of columns in a Pandas DataFrameGroupBy object