How to plot stacked time histogram starting from a Pandas DataFrame?

Question

Consider the following DataFrame df :

Date                   Kind
2018-09-01 13:15:32    Red
2018-09-02 16:13:26    Blue
2018-09-04 22:10:09    Blue
2018-09-04 09:55:30    Red
...                    ...

In which you have a column with a datetime64[ns] dtype and another which contains a np.object which can assume only a finite number of values (in this case, 2).

You have to plot a date histogram in which you have:

• On the x-axis, the dates (per-day histogram showing month and day);
• On the y-axis, the number of items belonging to that date, showing in a stacked bar the difference between Blue and Red.

How is it possible to achieve this using Matplotlib?

I was thinking to do a set_index and resample as follows:

df.set_index('Date', inplace=True)
df.resample('1d').count()

But I'm losing the information on the number of items per Kind . I also want to keep any missing day as zero.

Any help very appreciated.

Answer 1

Use groupby , count and unstack to adjust the dataframe:

df2 = df.groupby(['Date', 'Kind'])['Kind'].count().unstack('Kind').fillna(0)

Next, re-sample the dataframe and sum the count for each day. This will also add any missing days that are not in the dataframe (as specified). Then adjust the index to only keep the date part.

df2 = df2.resample('D').sum()
df2.index = df2.index.date    

Now plot the dataframe with stacked=True :

df2.plot(kind='bar', stacked=True)

---

Alternatively, the plt.bar() function can be used for the final plotting:

cols = df['Kind'].unique() # Find all original values in the column 
ind = range(len(df2))

p1 = plt.bar(ind, df2[cols[0]])
p2 = plt.bar(ind, df2[cols[1]], bottom=df2[cols[0]])

Here it is necessary to set the bottom argument of each part to be the sum of all the parts that came before.