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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 Deal With Missing Data: Pandas Fillna() and Dropna()

Here are the best examples of Pandas fillna(), dropna() and sum() methods. We have explained the process in two steps - Counting and Replacing the Null values.


Check and Replace Column Nulls


Count Nulls

## count null values column-wise

null_counts = df.isnull().sum()


print(null_counts)

```


Output:

```

Column1    1

Column2    1

Column3    5

dtype: int64

```

In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise:


```

df.isnull().sum()

```


##Code snippet to count null values row-wise:


```

df.isnull().sum(axis=1)

```


In the above code, `df` is the Pandas DataFrame for which you want to count the null values. The `isnull()` function returns a DataFrame with the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. 

The `sum()` function is then applied to the resulting DataFrame to count the number of null values.

Fill null values with zeros in Pandas


```

import pandas as pd


# create a sample dataframe

data = {'Column1': [1, 2, 3, 4, None],

        'Column2': ['A', 'B', None, 'C', 'D'],

        'Column3': [None, None, None, None, None]}

df = pd.DataFrame(data)


Fill Nulls

To fill null values with '0' in Pandas DataFrame, you can use the `fillna()` function. Here's an example code snippet to do this:


```

import pandas as pd


# create a sample dataframe

data = {'Column1': [1, 2, 3, 4, None],

        'Column2': ['A', 'B', None, 'C', 'D'],

        'Column3': [None, None, None, None, None]}

df = pd.DataFrame(data)


# fill null values with 0

df.fillna(0, inplace=True)


print(df)

```


Output:


```

   Column1 Column2  Column3

0      1.0      A      0.0

1      2.0      B      0.0

2      3.0      0      0.0

3      4.0      C      0.0

4      0.0      D      0.0

```

In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then we use the `fillna()` function to replace all null values in the DataFrame with '0'. The `inplace=True` parameter ensures that the original DataFrame is modified and not a copy. Finally, we print the modified DataFrame with null values filled with '0'.


Note that the `axis` parameter is set to 0 by default in the `sum()` function, which means that it counts null values column-wise. To count null values row-wise, you need to set `axis` to 1.


Drop Nulls


df.dropna() 

It drops rows with any columns having the Nulls.

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