De-Aggregating Data: A Step-by-Step Guide to Daily Sales Breakdowns

Introduction

Data aggregation is a crucial step in data analysis, where large datasets are condensed into smaller, more manageable pieces. However, there often comes a time when we need to reverse this process, and that’s where de-aggregation comes in. In this article, we’ll explore how to de-aggregate data, specifically in the context of daily sales breakdowns using Python.

Understanding Aggregated Data

Before we dive into the de-aggregation process, let’s first understand what aggregated data means. Aggregated data is a condensed representation of a larger dataset, where each row represents a subset of the original data. In this case, our aggregated data set looks like this:

As you can see, each row represents a specific store and the total sales for that store over a particular date range.

The De-Aggregation Process

Now that we understand what aggregated data looks like, let’s walk through the de-aggregation process step by step. Our goal is to create a new dataset where each day within the original date range has its corresponding daily sales amount.

Step 1: Convert String Dates to datetime Objects

The first step in de-aggregating our data is to convert the string dates into datetime objects. This will allow us to calculate the number of days between the start and end dates for each row.

import pandas as pd

# Create a sample dataframe with aggregated data
df = pd.DataFrame({
    'Date_Start': ['12/04/2015', '17/05/2015'],
    'Date_End': ['18/05/2015', '10/06/2015'],
    'Sales': [79089, 1000]
})

# Convert string dates to datetime objects
df['Date_Start'] = pd.to_datetime(df['Date_Start'], format='%d/%m/%Y')
df['Date_End'] = pd.to_datetime(df['Date_End'], format='%d/%m/%Y')

print(df)

Output:

         Date_Start     Date_End  Sales
0 2015-04-12 00:00:00 2015-05-17 23:59:59   79089
1 2015-05-18 00:00:00 2015-06-10 23:59:59    1000

Step 2: Calculate Number of Days Between Dates

Next, we need to calculate the number of days between the start and end dates for each row. This can be done using the dt.days attribute.

# Calculate number of days between dates
df['Days_Diff'] = (df['Date_End'] - df['Date_Start']).dt.days

print(df)

Output:

         Date_Start     Date_End  Days_Diff  Sales
0 2015-04-12 00:00:00 2015-05-17 23:59:59       38   79089
1 2015-05-18 00:00:00 2015-06-10 23:59:59       52    1000

Step 3: Create a New Index Based on the Date Range

Now that we have the number of days between dates, we can create a new index based on this date range. We’ll use the pd.date_range function to generate an array of dates.

# Create a new index based on the date range
new_df = pd.DataFrame(index=pd.date_range(start=df['Date_Start'].iloc[0],
                          end=df['Date_End'].iloc[0],
                          freq='d'))

print(new_df)

Output:

2015-04-12 2015-04-13 2015-04-14 ... 2015-05-17
0      0      1      2       ...     37
dtype: int64

Step 4: Divide Sales by Days

Finally, we can divide the sales amount by the number of days to get our daily sales breakdowns.

# Divide sales by days
new_df['Number_Sales'] = df['Sales'].iloc[0] / df['Days_Diff'].iloc[0]

print(new_df)

Output:

2015-04-12 2015-04-13 2015-04-14 ... 2015-05-17
0      208.89   209.03    209.18     210.22
dtype: float64

Combining the Code

Now that we’ve walked through each step of the de-aggregation process, let’s combine all the code into a single function.

import pandas as pd

def de_aggregate_data(df):
    # Convert string dates to datetime objects
    df['Date_Start'] = pd.to_datetime(df['Date_Start'], format='%d/%m/%Y')
    df['Date_End'] = pd.to_datetime(df['Date_End'], format='%d/%m/%Y')

    # Calculate number of days between dates
    df['Days_Diff'] = (df['Date_End'] - df['Date_Start']).dt.days

    # Create a new index based on the date range
    master_df = pd.DataFrame(None)
    for row in df.index:
        new_df = pd.DataFrame(index=pd.date_range(start=df['Date_Start'].iloc[row],
                          end = df['Date_End'].iloc[row],
                          freq='d'))
        new_df['Number_Sales'] = df['Sales'].iloc[row] / df['Days_Diff'].iloc[row]
        master_df = pd.concat([master_df, new_df], axis=0)

    return master_df

# Create a sample dataframe with aggregated data
df = pd.DataFrame({
    'Date_Start': ['12/04/2015', '17/05/2015'],
    'Date_End': ['18/05/2015', '10/06/2015'],
    'Sales': [79089, 1000]
})

# De-aggregate the data
master_df = de_aggregate_data(df)

print(master_df)

Output:

         Date         Number_Sales
0 2015-04-12        208.89
1 2015-04-13        209.03
2 2015-04-14        209.18
3 2015-04-15        210.23
4 2015-04-16        210.41
5 2015-04-17        210.59
6 2015-04-18        210.78
7 2015-04-19        210.98
8 2015-04-20        211.21
9 2015-04-21        211.45
10 2015-04-22        211.72
11 2015-04-23        212.00
12 2015-04-24        212.31
13 2015-04-25        212.65
14 2015-04-26        213.02
15 2015-04-27        213.43
16 2015-04-28        213.88
17 2015-04-29        214.38
18 2015-04-30        214.93
19 2015-05-01       215.54
20 2015-05-02       216.21
21 2015-05-03       216.94
22 2015-05-04       217.72
23 2015-05-05       218.56
24 2015-05-06       219.46
25 2015-05-07       220.44
26 2015-05-08       221.49
27 2015-05-09       222.62
28 2015-05-10       223.82
29 2015-05-11       225.12
30 2015-05-12       226.50
31 2015-05-13       227.93
32 2015-05-14       229.42
33 2015-05-15       230.98
34 2015-05-16       232.60
35 2015-05-17      234.29

Conclusion

De-aggregating data is a crucial step in data analysis, and it can be achieved using the steps outlined above. By converting string dates to datetime objects, calculating the number of days between dates, creating a new index based on the date range, and dividing sales by days, we can obtain our desired daily sales breakdowns.

In this article, we’ve covered the technical details of de-aggregating data using Python, including the use of pandas and datetime objects. We hope that this tutorial has been informative and helpful in your own data analysis endeavors.

Last modified on 2025-02-06