I have a dataframe, where I would like to make a time series plot with three different lines that each show the daily occurrences (the number of rows per day) for each of the values in another column.

To give an example, for the following dataframe, I would like to see the development for how many a's, b's and c's there have been each day.

df = pd.DataFrame({'date':pd.to_datetime(['2019-10-10','2019-10-14','2019-10-09','2019-10-10','2019-10-08','2019-10-14','2019-10-10','2019-10-08','2019-10-08','2019-10-13','2019-10-08','2019-10-12','2019-10-11','2019-10-09','2019-10-08']),'letter':['a','b','c','a','b','b','b','b','c','b','b','a','b','a','c']})

When I try the command below (my best guess so far), however, it does not filter for the different dates (I would like three lines representing each of the letters.

Any ideas on how to solve this?

df.groupby(['date']).count().plot()['letter']

I have also tried a solution in Matplotlib, though this one gives an error..

fig, ax = plt.subplots()
ax.plot(df['date'], df['letter'].count())

Based on your question, I believe you are looking for a line plot which has dates in X-axis and the counts of letters in the Y-axis. To achieve this, these are the steps you will need to do...

1. Group the dataframe by date and then letter - get the number of entries/rows for each which you can do using size()
2. Flatten the grouped dataframe using reset_index(), rename the new column to Counts and sort by letter column (so that the legend shows the data in the alphabetical format)... these are more to do with keeping the new dataframe and graph clean and presentable. I would suggest you do each step separately and print, so that you know what is happening in each step
3. Plot each line plot separately using filtering the dataframe by each specific letter
4. Show legend and rotate date so that it comes out with better visibility

The code is shown below....

df = pd.DataFrame({'date':pd.to_datetime(['2019-10-10','2019-10-14','2019-10-09','2019-10-10','2019-10-08','2019-10-14','2019-10-10','2019-10-08','2019-10-08','2019-10-13','2019-10-08','2019-10-12','2019-10-11','2019-10-09','2019-10-08']),'letter':['a','b','c','a','b','b','b','b','c','b','b','a','b','a','c']})df_grouped = df.groupby(by=['date', 'letter']).size().reset_index() ## New DF for grouped data
df_grouped.rename(columns = {0 : 'Counts'}, inplace = True)
df_grouped.sort_values(['letter'], inplace=True)
colors = ['r', 'g', 'b'] ## New list for each color, change as per your preferencefor i, ltr in enumerate(df_grouped.letter.unique()):plt.plot(df_grouped[df_grouped.letter == ltr].date, df_grouped[df_grouped.letter == ltr].Counts, '-o', label=ltr, c=colors[i])
plt.gcf().autofmt_xdate() ## Rotate X-axis so you can see dates clearly without overlap
plt.legend() ## Show legend

Output graph