如何使用Python在单个图形上绘制多种数据？

Question

I'm aware this may be a newbie question but I was forced to learn data analysis through Python without a proper background knowledge (I tried vb.net 10 years ago, that's all), and I need to solve a problem very fast.

I was asked to plot a graph about the speed of a vehicle during a period starting from a dataset like this:

ID      CITY   SPEED          TIME
0       Milan   45   25/05/2018 17:35:30
1       Milan   60   25/05/2018 17:35:45
2       Milan   80   25/05/2018 17:36:00
....
2000    Rome    0    25/05/2018 21:05:15
2001    Rome    0    25/05/2018 21:05:30
2002    Rome    0    25/05/2018 21:05:45

Every dataset I need to analyze have an average of 3000/4000 rows and contain columns I don't need.

Now I was able to import the dataset from a file Excel, I created 3 objects with data took from the columns I need:

speed = Dataset[['speed']]
time = Dataset[['time']]
city = Dataset[['city']]

What I need is to plot a graph that has the speed as the main data to be visualized, and then the time (on x-axes). When the speed is 0 I also need to show the city.

I know how to plot the graph of the speed, but I don't know what to do in order to get time and city where I need them.

It should be something like this: graph I need to obtain

Any kind of help is really appreciated since I don't know what I need to do to reach this result.

Thanks in advance!

Answer 1

At first I assume that your time data is only in string format? Then you should transform it into a datetime format like:

Dataset['time'] = pd.to_datetime(Dataset['time'])

With this done you could plot your speed data explicitly over time like:

plt.plot(Dataset['time'], Dataset['speed'])

Afterwards, I'd iterate over

Dataset[Dataset['speed']==0]

and call plt.annotate()

Over what datasubset to iterate exactly depends on your zero speed data: it looks like there are several entries one after another, so you should filter first, perhaps by some Dataset.groupby and just picking the first() entry of each group, possibly.