to it. In this case, you're passing in a dataframe and it's rendering as an
interactive table.
st.write tries to do the right thing based on
the data type of the input. If it isn't doing what you expect you can use a
specialized command like st.dataframe
instead. For a full list, see API reference.
Draw a histogram
Now that you've had a chance to take a look at the dataset and observe what's
available, let's take things a step further and draw a histogram to see what
Uber's busiest hours are in New York City.
To start, let's add a subheader just below the raw data section:
python
   st.subheader('Number of pickups by hour')
Use NumPy to generate a histogram that breaks down pickup times binned by
   hour:
python
   hist_values = np.histogram(
       data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
