The error message indicates that df2 is of type pd.Series. You need to convert df2 .to_frame() as .merge() needs a pd.DataFrame() input (see docs): while you probably also just could: Alternatively, you can use pd.DataFrame.join() which accepts a pd.Series.
datetime64[ns] is a general dtype, while <M8[ns] is a specific dtype. General dtypes map to specific dtypes, but may be different from one installation of NumPy to the next. On a machine whose byte order is little endian, there is no difference between np.dtype(‘datetime64[ns]’) and np.dtype(‘<M8[ns]’): However, on a big endian machine, np.dtype(‘datetime64[ns]’) would equal np.dtype(‘>M8[ns]’). So datetime64[ns] maps to either <M8[ns] or >M8[ns] depending on the endian-ness of the … Read more
You can use apply to index into leader and exchange values with DatasetLabel, although it’s not very pretty. One issue is that Pandas won’t let us index with NaN. Converting to str provides a workaround. But that creates a second issue, namely, column 9 is of type float (because NaN is float), so 5 becomes … Read more
I would like to create a column in a pandas data frame that is an integer representation of the number of days in a timedelta column. Is it possible to use ‘datetime.days’ or do I need to do something more manual? timedelta column 7 days, 23:29:00 day integer column 7
Here’s an example DataFrame which show this, it has duplicate values with the same index. The question is, do you want to aggregate these or keep them as multiple rows? One solution is to reset_index (and get back to df) and use pivot_table. Another option (if you don’t want to aggregate) is to append a … Read more
& has higher precedence than ==. Write:
To modify the float output do this:
You need to make sure your pandas dataframe columns are appropriate for the type spark is inferring. If your pandas dataframe lists something like: And you’re getting that error try: Now, make sure .astype(str) is actually the type you want those columns to be. Basically, when the underlying Java code tries to infer the type from an … Read more
Your approach will work despite the warning, but it’s best not to rely on implicit, unclear behavior. Solution 1, make the selection of indices in a_list a boolean mask: Solution 2, do it in two steps: Solution 3, if you want a one-liner, use a trick found here: The warning comes from the fact that the boolean vector df.a_col.isnull() is … Read more
Your replace format is off