Calculating cumulative sum in Pandas DataFrame with NumPy array

Last update on December 21 2024 07:43:15 (UTC/GMT +8 hours)

Calculate the cumulative sum of a NumPy array and store the results in a new Pandas DataFrame column.

Sample Solution:

Python Code:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'Values': [100, 200, 300, 400, 500]}

df = pd.DataFrame(data)

# Create a NumPy array from the 'Values' column
numpy_array = np.array(df['Values'])

# Calculate the cumulative sum and store in a new column 'Cumulative_Sum'
df['Cumulative_Sum'] = np.cumsum(numpy_array)

# Display the updated DataFrame
print(df)

Output:

   Values  Cumulative_Sum
0     100             100
1     200             300
2     300             600
3     400            1000
4     500            1500

Explanation:

In the exerciser above -

• First we create a sample DataFrame (df) with a column 'Values'.
• Next we convert the 'Values' column to a NumPy array using np.array().
• The np.cumsum(numpy_array) function calculates the cumulative sum of the NumPy array.
• The result is assigned to a new column 'Cumulative_Sum' in the DataFrame.
• The updated DataFrame is then printed to the console.

Flowchart:

Python Code Editor:

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