Converting csv data to various type || Data Management And Visualization
We all want to do a lot of practice, but as we move forward it becomes tough for us to "Level UP! ". Why? Because we become used to that procedure. Hoooo !! But in my case, you can say it is fortunate that whenever I start doing something, some new kind of error will arise and I gotta sort it out from scratch. So, Here's the story..... I was doing this course from Coursera called "Data Management and Visualization" by Wesleyan University So, they provided a link to the dataset of "GAPMINDER" (you can visit it here: GitHub Link ) And, my first dataset is something else. Man! it had so many blank cells and when I tried dataframe.dtypes() , it told me that, all columns are in object form. But, as we are aware, to have a statistical analysis of the data, we need it in int64 or float form. So, I tried using .to_numerical and astype() functions but none of them worked. Because pd.to_numerical can't parse the string,...