Time series data visualization issue

Question

I have a time series data like below where the data consists of year and week. So, the data is from 2014 1st week to 2015 52 weeks.

Now, below is the line plot of the above mentioned data

As you can see the x axis labelling is not quite what I was trying to achieve since the point after 201453 should be 201501 and there should not be any straight line and it should not be up to 201499. How can I rescale the xaxis exactly according to Due_date column? Below is the code

rand_products = np.random.choice(Op_2['Sp_number'].unique(), 3)
selected_products = Op_2[Op_2['Sp_number'].isin(rand_products)][['Due_date', 'Sp_number', 'Billing']]

plt.figure(figsize=(20,10))
plt.grid(True)

g = sns.lineplot(data=selected_products, x='Due_date', y='Billing', hue='Sp_number', ci=False, legend='full', palette='Set1');

Answer 1

the issue is because 201401... etc. are read as numbers and that is the reason the line chart has that gap. To fix it, you will need to change the numbers to date format and plot it. As the full data is not available, below is the two column dataframe which has the Due_date in the form of integer YYYYWW. Billing column is a bunch of random numbers. Use the method here to convert the integers to dateformat and plot. The gap will be removed....

import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
import seaborn as sns

Due_date = list(np.arange(201401,201454)) #Year 2014
Due_date.extend(np.arange(201501,201553)) #Year 2915
Billing = random.sample(range(500, 1000), 105) #billing numbers
df = pd.DataFrame({'Due_date': Due_date, 'Billing': Billing})
df.Due_date = df.Due_date.astype(str)
df.Due_date = pd.to_datetime(df['Due_date']+ '-1',format="%Y%W-%w") #Convert to date

plt.figure(figsize=(20,10))
plt.grid(True)

ax = sns.lineplot(data=df, x='Due_date', y='Billing', ci=False, legend='full', palette='Set1')

Output graph