Pandas replace method and object datatypes
If the rest of the data in your columns is numeric then you should use pd.to_numeric(df, errors='coerce')
I am using df= df.replace('No data', np.nan) on a csv file containing ‘No data’ instead of blank/null entries where there is no numeric data. Using the head() method I see that the replace method does replace the ‘No data’ entries with NaN. When I use df.info() it says that the datatypes for each of the series is an object. 
When I open the csv file in Excel and manually edit the data using find and replace to change ‘No data’ to blank/null entries, although the dataframes look exactly the same when I use df.head(), when I use df.info() it says that the datatypes are floats.
I was wondering why this was and how can I make it so that the datatypes for my series are floats, without having to manually edit the csv files.