Date/time utilities in the pandas module are more flexible/powerful than that in the datetime module. It is suggested that you use date/time utilities in the pandas module when you use DataFrame/Series in the pandas module.

1. pandas.to_datetime works on an iterable object, handles missing values and nano seconds.

2. pandas.Series.dt.strftime

pandas.Series.str¶

1. The attribute pandas.Series.str can only be used with Series of str values. You will either encounter an AttributionError (Can only use .str accessor with string values, which use np.object_ dtype in pandas) or find it to yield a Series of NaN's if you invoke it on a Series of non-string values. If you have control of the DataFrame, the preferred way is to cast the type the column to str

https://www.shanelynn.ie/summarising-aggregation-and-grouping-data-in-python-pandas/

Comment¶

1. The order of elements within each group are preserved (as the original order).

2. groupby works exactly the same on index if the index is named.

3. The order of columns in groupby matters if you want unstack the results later.

4. groupby works on columns too and it can group by some level of a MultiIndex.

https://docs.dask.org/en/latest/

Installation¶

1. You have to install the complete version of Dask (using the command pip3 install dask[complete]) if you need support of extended memory (for handling big data) and schedulers (for performance). The default installation version (pip3 install dask) of Dask does not include those features out-of-box.