Mapping Column Value to Another Column Based on Condition

In this article, we will explore a common use case in data manipulation using pandas, where we need to map values from one column based on the condition of another column. Specifically, we are given a pandas dataframe with three columns: datum2, value3, and datum3. We want to map the value from datum3 to datum2 and the value from value3 to value2 when datum2 is equal to “NGVD29”.

Problem Description

The problem description provides a sample dataframe with the following structure:

We want to map the values from datum3 and value3 to datum2 and value2, respectively, when datum2 is equal to “NGVD29”.

Solution

To achieve this mapping, we can use the following code:

m = df['Datum2'].eq('NGVD29')
df.loc[m, ['Datum2', 'Value2']] = df[m][['Datum3', 'Value3']].values

This code uses boolean indexing to select only the rows where datum2 is equal to “NGVD29”. Then, it maps the values from datum3 and value3 to datum2 and value2, respectively.

How it Works

Here’s a step-by-step explanation of how this code works:

1. m = df['Datum2'].eq('NGVD29'): This line creates a boolean mask that selects only the rows where datum2 is equal to “NGVD29”. The result is stored in the variable m.
2. df.loc[m, ['Datum2', 'Value2']]: This line uses the boolean mask m to select only the columns Datum2 and Value2 from the original dataframe.
3. = df[m][['Datum3', 'Value3']].values: This line maps the values from datum3 and value3 to datum2 and value2, respectively, for the selected rows.

Example Output

The resulting dataframe will have the following structure:

|   ID | Value | datum1 | value1      | datum2      | Value2       |
| --- | ---- | ----- | ------------ | ---------- | ------- |
| 111 | -2.18 | NAVD88 | 3.54601048 | NGVD29     | Local Assumed Datum    |
| 111 | -2.4  | NAVD88 | 3.32601048 | Local Assumed Datum | -1.15       |
| ...  | ...  | ...   | ...          | ...       | ...         |

The values from datum3 and value3 have been mapped to datum2 and value2, respectively, for the rows where datum2 is equal to “NGVD29”.

Conclusion

In this article, we explored a common use case in data manipulation using pandas, where we need to map values from one column based on the condition of another column. We provided an example code snippet that demonstrates how to achieve this mapping, and explained each step in detail. By following this solution, you should be able to map values from datum3 and value3 to datum2 and value2, respectively, for rows where datum2 is equal to “NGVD29”.

Last modified on 2024-02-04