I am quite a beginner with Python but I have a programming-related project to work on, so, I really would like to ask some help. I didn´t find many simple solutions to organize the data such a way that I could do some analysis with that. First, I have multiple csv-files, which I read in as DataFrame objects. In the end, I need them all to analyze together (right now the files are separated to the list of DataFrames but later on I probably will need those as one DataFrame object). However, I have a problem with organizing and separating the data. These are thousands of rows in one column, a part of it is presented:
CIP;Date;Hour;Cons;REAL/ESTIMATED EN025140855608477018TC2L;11/03/2020;1;0 057;R EN025140855608477018TC2L;11/03/2020;2;0 078;R EN025140855608477018TC2L;11/03/2020;3;0 033;R EN025140855608477018TC2L;11/03/2020;4;0 085;R EN025140855608477018TC2L;11/03/2020;5;0 019;R ... EN025140855608477018TC2L;11/04/2020;20;0 786;R EN025140855608477018TC2L;11/04/2020;21;0 288;R EN025140855608477018TC2L;11/04/2020;22;0 198;R EN025140855608477018TC2L;11/04/2020;23;0 728;R EN025140855608477018TC2L;11/04/2020;24;0 275;R The area, where the huge space between, the number should be merged together, for example, 0.057, which information represents "Cons" (actually it is the most important information). I should be able to split data into 5 columns in order to proceed with the analysis. However, it should be a universal tool for different csv-files without knowing the including symbols. But the structure of the content and the heading is always the same.
I would be happy if anyone might know to recommend a way to work with this kind of data.
1 Answer
Sounds like what you are trying to do is convert the Cons column so that the spaces become a dot.
df = pd.read_csv("file.txt", sep=";") df['Cons'] = df['Cons'].str.replace("\s+",".") df['Cons'].head() Output:
0 0.057 1 0.078 2 0.033 3 0.085 4 0.019 
