python - Transform category, start_time, end_time DateFrame for plotting in pandas -

i have pandas dataframe:

df.info()  <class 'pandas.core.frame.dataframe'> int64index: 32656 entries, 94418 2 data columns (total 8 columns): customer_id             32656 non-null object session_id              32656 non-null int64 start                   32656 non-null datetime64[ns, america/los_angeles] end                     32656 non-null datetime64[ns, america/los_angeles] length                  32656 non-null timedelta64[ns] category                32656 non-null object rounded_start           32656 non-null datetime64[ns, america/los_angeles] rounded_end             32656 non-null datetime64[ns, america/los_angeles] dtypes: datetime64[ns, america/los_angeles](4), int64(1), object(2), timedelta64[ns](1) memory usage: 2.2+ mb 

i create datetimeindex:

rng = pd.date_range(df['rounded_start'].min(), end=df['rounded_start'].max(), freq='5min') 

how tie 2 datasets can plot each point in range on x-axis , shows count of how many categories included during time?

i suspect work though haven't verified.

df_count = pd.dataframe(index=rng)  def count_cats(x, df):     date = x.name[0]     condition1 = df.start <= date     condition2 = df.end >= date     df_slice = df.loc[condition1 & condition2, 'category']     return pd.series([df_slice.unique().size], index=['countcats'])  df_count = df_count.apply(lambda x: count_cats(x, df))