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SQL Query: 3 Methods for Calculating Cumulative SUM

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SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

How to Convert Dictionary to Dataframe: Pandas from_dict

 Pandas is a data analysis Python library.  The example shows you to convert a dictionary to a data frame. The point to note here is DataFrame will take only 2D data. So you need to supply 2D data. 


Dictionary to Data frame example


Pandas Dictionary to Dataframe


import pandas as pd

import numpy as np

data_dict = {'item1' : np.random.randn(4), 'item2' : np.random.randn(4)}

df3=pd.DataFrame.from_dict(data_dict, orient='index')

print(df3)


Output


0 1 2 3 item1 -0.109300 -0.483624 0.375838 1.248651 item2 -0.274944 -0.857318 -1.203718 -0.061941


Explanation

Using the NumPy package, created a dictionary with random values. There are two items - item 1 and item 2. The data_dict is input to the data frame. The from_dict method needs two parameters. These are data_dict and index. Here's the syntax you can refer to quickly.


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