How to Make a Cumulative Plot in Python

In this tutorial, we will learn how to make a cumulative plot using Python. A cumulative plot can be a powerful tool in data visualization, as it provides a clear and concise way to represent cumulative data over time.

This can be especially useful when dealing with time-series data, inventory levels, sales data, or any type of data that grows over time. We will be using Python’s Matplotlib and Pandas libraries to create our plots. So, let’s get started.

Step 1: Import Necessary Libraries

To begin with, you need to import the necessary Python libraries. This includes matplotlib for plotting and pandas for data handling.

1 2	import matplotlib.pyplot as plt import pandas as pd

Step 2: Prepare Your Data

In this step, prepare or load your dataset. For this tutorial, we will be using a simple dataset. We will use Pandas to create a DataFrame and add some data.

data = {'dates': pd.date_range(start='1/1/2022', periods=100),

'value': range(1, 101)}

df = pd.DataFrame(data)

The DataFrame, df, now contains a ‘dates’ column representing dates from 1st Jan 2022, and a ‘value’ column representing a range of numbers from 1 to 100.

Step 3: Plot the Cumulative Total

We will now use Matplotlib to plot a line graph, showcasing the cumulative total of values over time.

df['cumulative'] = df['value'].cumsum()

plt.plot(df['dates'], df['cumulative'])

plt.show()

The ‘cumulative’ column is created using the cumsum() function, which returns the cumulative sum of the ‘value’ column. Then, we plot these cumulative totals against the ‘dates’ column. The result is a line plot with dates on the X-axis and the cumulative total of the values on the Y-axis.

Here’s the Full Code:

import matplotlib.pyplot as plt

import pandas as pd