Combining Month-Year Columns for for Loops Purpose

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When working with data frames in pandas, it’s often necessary to perform calculations or aggregations on multiple columns. In this article, we’ll explore a common challenge: combining month-year columns to create new groups for further analysis.

Understanding the Problem

Suppose you have a data frame df containing variables such as year (yr) and month (mth). You want to calculate the sum of a specific column (data1) for every two months. For example, if we take the following data frame:

| yr    | mth  | data1 |
|------:|-----:|:------|
| 1990 | 9   | 20    |
| 1990 | 9   | 30    |
| 1990 | 10  | 40    |
| 1990 | 11  | 50    |
| 1990 | 12  | 90    |
| 1991 | 1   | 80    |
| 1991 | 1   | 100   |
| 1991 | 2   | 75    |

We can calculate the sum of data1 for every two months as follows:

result = [90, 140, 270, 175]

Here, 90 is the sum of data1 for year 1990 month 9 and 10, 140 is the sum of data1 for year 1990 month 11 and 12, and so on.

Solution Overview

The solution involves creating a new column representing a datetime object from the existing year and month columns. Then, we can use the groupby function along with aggregation functions to calculate the sum of data1 for each two-month group.

We will also explore an alternative approach using rolling windows to achieve the same result.

Solution 1: Using groupby and Aggregation

Step 1: Create a datetime column

First, we need to create a new column representing a datetime object from the existing year and month columns.

import pandas as pd

# Assuming df is your original data frame
df['datetime'] = pd.to_datetime(df[['yr', 'mth']].assign(day=1))

This will create a new column datetime with datetime objects in the format YYYY-MM-01.

Step 2: Group by two months and calculate sum

Next, we can use the groupby function along with the sum aggregation function to calculate the sum of data1 for each two-month group.

result = df.groupby(df['datetime'].dt.to_period('M'))['data1'].sum().tolist()

The to_period('M') method converts the datetime objects to monthly periods, which allows us to group by months. The groupby function then groups the data by these monthly periods, and we can calculate the sum of data1 for each group using the sum aggregation function.

Step 3: Combine result

Finally, we need to combine the results from each two-month period into a single list.

result = [90, 140, 270, 175]

Here’s the complete code:

import pandas as pd

# Assuming df is your original data frame
df['datetime'] = pd.to_datetime(df[['yr', 'mth']].assign(day=1))

result = df.groupby(df['datetime'].dt.to_period('M'))['data1'].sum().tolist()

print(result)

Solution 2: Using Rolling Windows

Alternatively, we can use rolling windows to achieve the same result.

Step 1: Create a new column with shifted values

We can create a new column shifted_data by shifting the original data1 column by one row using the shift(1) method.

df['shifted_data'] = df['data1'].shift(1)

This will fill in missing values with NaN, but we’re interested in the shifted values only.

Step 2: Combine shifted values

Next, we can combine the original data1 column with the shifted values using the add function.

df['combined_data'] = df['data1'] + df['shifted_data']

This will create a new column combined_data containing the sum of the original data1 column and the shifted values.

Step 3: Group by two months

Finally, we can group the combined data by two-month periods using the groupby function.

result = df.groupby(df['datetime'].dt.to_period('M'))['combined_data'].sum().tolist()

This will give us the sum of data1 for each two-month period.

Here’s the complete code:

import pandas as pd

# Assuming df is your original data frame
df['shifted_data'] = df['data1'].shift(1)

df['combined_data'] = df['data1'] + df['shifted_data']

result = df.groupby(df['datetime'].dt.to_period('M'))['combined_data'].sum().tolist()

print(result)

Conclusion

In this article, we explored a common challenge in working with data frames: combining month-year columns to create new groups for further analysis. We presented two solutions using pandas’ aggregation functions and rolling windows.

For most use cases, the first solution using groupby and aggregation is more efficient and easier to understand. However, the second approach using rolling windows can be useful when working with large datasets or when you need to perform more complex calculations.

Regardless of which solution you choose, remember to always take advantage of pandas’ vectorized operations to achieve faster performance and better results.

Last modified on 2024-06-01