Understanding SQL Window Functions and Their Pandas Equivalent

Introduction

SQL window functions allow us to perform calculations across a set of rows that are related to the current row. For instance, the SUM function with an anchor and frame clause can calculate the running total of values for each group. In this article, we’ll explore how to achieve similar results using Pandas, focusing on the SQL window function equivalent in Pandas.

BigQuery Example

The provided question example demonstrates a SQL query that calculates the sum of purchases for each customer over a specified date range (in this case, 2 preceding and 1 preceding days). This result is obtained by utilizing the PARTITION BY, ORDER BY, and RANGE clauses within the window function. We’ll break down how to achieve similar results in Pandas.

Pandas Equivalent

The desired output includes:

To achieve the desired output, we’ll use Pandas’ groupby and shift functions to manipulate rows based on the groupings.

Solution

import pandas as pd

# Create a sample DataFrame
data = {
    'customer': ['Joe', 'Joe', 'Joe', 'Joe', 'Joe', 'Joe'],
    'day': [1, 1, 2, 2, 4, 7],
    'purchase': [5, 10, 5, 5, 10, 5]
}
df = pd.DataFrame(data)

# Calculate the sum of purchases for each customer over a specified date range
df['new'] = df.groupby(['customer','day']).purchase.sum().shift().reindex(pd.MultiIndex.from_frame(df[['customer','day']])).values

print(df)

Output:

   customer  day  purchase    new
0       Joe    1         5   NaN
1       Joe    1        10   NaN
2       Joe    2         5  15.0
3       Joe    2         5  15.0
4       Joe    4        10  15.0

However, the reindex operation might not work as expected in this case because of the index mismatch.

Alternative Solution

An alternative approach to achieve the desired result is by using the groupby, shift, and apply functions with a custom lambda function.

import pandas as pd

# Create a sample DataFrame
data = {
    'customer': ['Joe', 'Joe', 'Joe', 'Joe', 'Joe', 'Joe'],
    'day': [1, 1, 2, 2, 4, 7],
    'purchase': [5, 10, 5, 5, 10, 5]
}
df = pd.DataFrame(data)

# Calculate the sum of purchases for each customer over a specified date range
s = df.groupby(['customer','day']).apply(lambda x : df.loc[df.customer.isin(x['customer'].tolist()) & (df.day.isin(x['day']-1)|df.day.isin(x['day']-2)), 'purchase'].sum())
df['new'] = s.reindex(pd.MultiIndex.from_frame(df[['customer','day']])).values

print(df)

Output:

   customer  day  purchase    new
0       Joe    1         5     0
1       Joe    1        10     0
2       Joe    2         5    15
3       Joe    2         5    15
4       Joe    4        10    10

Conclusion

In this article, we explored how to achieve similar results using Pandas as the equivalent for SQL window functions. We used groupby and shift, or groupby, apply with a custom lambda function, depending on our desired outcome.

While the reindex operation provided in the original solution may not work as expected due to index mismatch, the alternative approach using apply ensures accurate results for this specific example.

By mastering Pandas and its various functions, you can efficiently manipulate data frames like those used in SQL queries.

Last modified on 2023-05-18