I have a dataset contains ID and year of their occurrence. This is the sample of my dataset:

ID Year
1234 2018
1234 2019
5678 2017
5678 2020
.... ....

I would like to visualize the ID's and the year when they are occurring. Is there any idea how I should visualize it using Python? Fyi the dataset has 48 ID and every ID always occur 2 times (which means they occur in 2 years).


1 Answer 1


In Pandas you can use groupby() to create an aggregate for every ID and counting the year where the ID occurs in your dataset:

import pandas as pd

data = {
    "id": [1234, 1234, 5678, 5678],
    "year": [2018, 2019, 2017, 2020]
df = pd.DataFrame(data)

df_grouped = df.groupby(["id", "year"]).size().unstack(fill_value=0)

Where size() counts the occurrences and unstack() creates a new level of column labels whose inner-most level consists of the pivoted index labels.


year  2017  2018  2019  2020
1234     0     1     1     0
5678     1     0     0     1

As for the visualization, I don't know what your preferences are, but I found a bar diagram to be a poor choice. So I opted for a confusion matrix style of visualization:

import seaborn as sn
import matplotlib.pyplot as plt

sn.heatmap(df_grouped, annot=True, square=True, cbar=False)


The benefit of the confusion matrix is, for example, if ID=1234 occurs twice in the same year, the count would be 2 instead of 1 in the visualization.


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