“**Cumulative Percentages**” help you compare how many records are lower than a certain number in a group of records. For instance, if we have a group of students who took a test, the cumulative percentage shows us how many students got the same or lower score than a certain number. If you are working with data or math, finding cumulative percentages is an important feature.

In Python, you can easily find cumulative percentages using the “**pandas**” library. In this blog, we will show you how to get cumulative percentages in Python utilizing “pandas”.

**How to Calculate/Find Cumulative Percentages Using Pandas in Python?**

Follow the following steps to find the cumulative percentages using “pandas” in Python:

**Step 1: Importing Required Libraries**

The first step is to import the required library named “**pandas**” (used for data manipulation and analysis):

**Step 2: Creating DataFrame**

Now, let’s create a data frame to find cumulative percentages based on it:

df = pandas.DataFrame({'year': [11, 22, 33, 44, 55, 66],

'sale': [100, 175, 737, 847, 114, 234]})

print(df)

In the above code block, the “**DataFrame**” is created using the “**pandas.DataFrame()**” function having the stated values.

**Output**

Based on the above output, the data frame has been created appropriately.

**Step 3: Calculating Cumulative Percentages**

To calculate cumulative percentages, we need to sort the data in ascending order and calculate the cumulative sum of the values. We can achieve this using the “**cumsum()**” function in Pandas. The “**cumsum()**” and “**sum()**” functions are used in Python to find the cumulative percentages.

**Syntax**

The syntax of the “**cumsum()**” function in Python pandas is shown below:

Here is the example code:

df = pandas.DataFrame({'year': [11, 22, 33, 44, 55, 66],

'sale': [100, 175, 737, 847, 114, 234]})

df_sorted = df.sort_values('sale')

df_sorted['cumulative_sum'] = df_sorted['sale'].cumsum()

print('Cumulative Sum:')

print(df_sorted)

total_sum = df_sorted['sale'].sum()

print('\nTotal Sum: ')

print(total_sum)

df_sorted['cumulative_percentage'] = 100 * df_sorted['cumulative_sum'] / total_sum

print('\nCumulative Percentage')

print(df_sorted)

In the above code:

- The “
**pandas**” library is loaded at the start. - The “
**pandas.DataFrame()**” function creates a data frame with two columns: “**year**” and “**sale**”, respectively. - The “
**df.sort_values()**” function is used to sort the data frame by the values in the “sale” column, from lowest to highest. - The “
**cumsum()**” function is used to create a new column in the sorted data frame named “**cumulative_sum**” and calculate the cumulative sum of the values in the “sale” column, which means adding up the values from top to bottom in an incremented manner. - It is such that the previous value(s) will be appended as a sum to the next one and so on.
- Now, the “
**sum()**” function calculates the total sum of the values in the “sale” column. - After dividing each value in the “
**cumulative_sum**” column by the total sum, the “**cumulative****percentage**” of the values in the “**sale**” column is calculated.

**Output**

As analyzed, the cumulative sum, total sum, and cumulative percentage have been calculated, respectively.

**Conclusion**

The “**cumsum()**” and “**sum()**” functions are used in Python to find the cumulative percentages. The “cumsum()” function adds up the column’s values and the “sum()” function gives the total value of a column. The purpose of this Python article was to demonstrate how to find the cumulative percentage utilizing pandas.