Say I have a dataframe with date as index. I would like to plot a line plot of some values in a column A over a given time frame. Say for the month of August. In column A I have several entries for example for the 02/08/2020 and a four different values on 03/08/2020 and so I would like to plot the sum of those values.

Is there an easy one line that would do it?


1 Answer 1


You could aggregate and sum values in a single line by pandas groupby() function. Here's an example:

  1. Making a simple dummy dataset:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

values = np.random.randint(1, high=100, size=50)
df = pd.DataFrame(data=values, columns=['values'])
  1. Adding some dates as indices:
def get_days():
    return sorted(np.random.choice(range(1, 31), size=25, replace=True))

year = '2021'
months = ['08', '09']
dates = []
for month in months:
    days = get_days()
    for day in days:

df.index = dates

Dataframe looks like the following:


  1. Aggregate and sum the data:
answers = df.groupby(df.index, sort=False).sum()  # since index already sorted

Printing answers:


  1. Simply plot it
dates = answers.index 
sums = answers.values 

fig, ax = plt.subplots()

ax.plot(range(len(dates)), sums, 'ro-')
ax.set_xticks(range(0, len(dates), 3))       # need to adjust to ensure plot is readable
ax.set_xticklabels(dates[::3], rotation=90)
ax.set_title("Sum Over Dates")

The plot to the above code looks like the following:


You can now control how you want to aggregate in several ways. You can add temporary columns as days, months, years and aggregate them hierarchically(and in a single line) if that is what you desire as shown here.

Plotting it in a single line will not only be difficult, but less readable too, so I'd suggest avoiding that.


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