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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 Fill Nulls in Pandas: bfill and ffill

In Pandas, bfill and ffill are two important methods used for filling missing values in a DataFrame or Series by propagating the previous (forward fill) or next (backward fill) valid values respectively. These methods are particularly useful when dealing with time series data or other ordered data where missing values need to be filled based on the available adjacent values.


Bfill and Ffill methods


ffill (forward fill):

When you use the ffill method on a DataFrame or Series, it fills missing values with the previous non-null value in the same column. It propagates the last known value forward.

This method is often used to carry forward the last observed value for a specific column, making it a good choice for time series data when the assumption is that the value doesn't change abruptly.

Example:


import pandas as pd


data = {'A': [1, 2, None, 4, None, 6],

        'B': [None, 'X', 'Y', None, 'Z', 'W']}


df = pd.DataFrame(data)

print(df)

# Output:

#      A     B

# 0  1.0  None

# 1  2.0     X

# 2  NaN     Y

# 3  4.0  None

# 4  NaN     Z

# 5  6.0     W


# Forward fill missing values

df_filled = df.ffill()

print(df_filled)

# Output:

#      A     B

# 0  1.0  None

# 1  2.0     X

# 2  2.0     Y

# 3  4.0     Y

# 4  4.0     Z

# 5  6.0     W


bfill (backward fill):

The bfill method fills missing values with the next non-null value in the same column. It propagates the next known value backward.

Like ffill, this method can be useful in time series data, especially when values are missing at the beginning of the dataset.

Example:


import pandas as pd


data = {'A': [None, 2, None, 4, 5, None],

        'B': ['X', 'Y', None, None, 'Z', None]}


df = pd.DataFrame(data)

print(df)

# Output:

#      A     B

# 0  NaN     X

# 1  2.0     Y

# 2  NaN  None

# 3  4.0  None

# 4  5.0     Z

# 5  NaN  None


# Backward fill missing values

df_filled = df.bfill()

print(df_filled)

# Output:

#      A     B

# 0  2.0     X

# 1  2.0     Y

# 2  4.0  None

# 3  4.0     Z

# 4  5.0     Z

# 5  NaN  None

These methods can be very useful for handling missing data when the order of the data is important, such as in time series analysis.

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