[FIXED] The best way to plot time series data for a short period of time

Issue

I have a dataset with two columns "new_date" and "Sales". The dataset captures the daily sales for a company over 3 months in just one year 2020, "i.e., Jan, Feb, and March". The size of the dataset is about 8000 rows. One day might have different transaction or different sales.

 new_date     Sales
2020-01-26     453
2020-01-26     232
2020-02-03     123
2020-02-03     223
2020-03-13     333
2020-03-23     657

My question is that is it possible to plot the time series for this short period date? And what is the best choice.

I simply tried to use plot

df.plot(legend=False)

But the results is not as good as I was expect.

Is there any better way to visualize and organize this time series data?

Solution

I'm not sure what you are exactly looking for, but based on the data I guess you could sum the Sales first by new_date.

df.groupby('new_date').sum().plot(legend=False)

When you want to sum Sales per week, you can use resample:

import pandas as pd
import random

df = pd.DataFrame({
    'new_date' : pd.date_range('2022-01-01', '2022-03-31'),
    'Sales' : random.sample(range(100, 1000), 90)}).set_index('new_date')

df.resample('W').sum().plot(legend=False)

Answered By - Rene