Dropping a Column in a DataFrame Based on the Next Column Value Not Being a Value in a Given List

In this article, we will explore how to filter a pandas DataFrame by checking if a specific condition is met. We will use the filter function along with conditional logic to achieve this.

Introduction

The problem at hand involves filtering out rows from a pandas DataFrame based on a certain condition. In this case, we have a DataFrame where each row has a diagnosis code prefixed with ‘DIAGX’ and its corresponding diagnosis type prefixed with ‘DTYPX’. The diagnosis type is the next column value in the DataFrame.

We want to filter out these rows only if their corresponding diagnosis type does not exist in a predefined list of values.

Problem Statement

Suppose we have a pandas DataFrame df_patients as follows:

We want to drop the rows that have diagnosis codes ‘DIAGX1’, ‘DIAGX2’, and ‘DIAGX4’ where their corresponding diagnosis types are not in a predefined list types_to_include.

Solution

To solve this problem, we can use the following steps:

Step 1: Create a mask to filter rows based on the condition

We will create a boolean mask m that filters the rows where the diagnosis type exists in the types_to_include list.

import pandas as pd

# Create the DataFrame
patients = [('pat1', 'Z509', '3', 'M33', 'M', 'M32', 1, 'M315', 'Y'),
             ('pat2', 'I099', '3', 'I278', '6', 'M05', 4, 'F01', 2),
             ('pat3', 'N057', '3', 'N057', 'M', 'N058', 'X', 'N057', 'X')]
labels = ['patient_num', 'DIAGX1', 'DTYPX1', 'DIAGX2', 'DTYPX2', 'DIAGX3', 'DTYPX3', 'DIAGX4', 'DTYPX4']
df_patients = pd.DataFrame.from_records(patients, columns=labels)

types_to_include = ['3', 'M', 'W', 'X', 'Y']

# Create a mask to filter rows based on the condition
m = df_patients.filter(like='DTYPX').isin(types_to_include).values

Step 2: Filter out the rows that don’t meet the condition

We will use the filter function with conditional logic to create a new DataFrame where all rows that do not have their corresponding diagnosis type in the types_to_include list are replaced with ‘NULL’.

# Filter out the rows that don't meet the condition
new = df_patients.filter(like='DIAG').where(m, 'NULL')

Step 3: Update the original DataFrame

We will use the update function to update the original DataFrame with the new filtered DataFrame.

# Update the original DataFrame
df_patients.update(new)

Example Use Case

Suppose we have a larger dataset and want to filter out rows that do not meet certain conditions. We can use the above steps to achieve this:

import pandas as pd

# Create a larger dataset
data = {
    'patient_num': [1, 2, 3, 4, 5],
    'DIAGX1': ['A', 'B', 'C', 'D', 'E'],
    'DTYPX1': [1, 2, 3, 4, 5],
    'DIAGX2': ['F', 'G', 'H', 'I', 'J'],
    'DTYPX2': [6, 7, 8, 9, 10],
    'DIAGX3': ['K', 'L', 'M', 'N', 'O'],
    'DTYPX3': [11, 12, 13, 14, 15],
    'DIAGX4': ['P', 'Q', 'R', 'S', 'T'],
    'DTYPX4': [16, 17, 18, 19, 20]
}
df_large = pd.DataFrame(data)

types_to_include = [1, 2, 3]

# Create a mask to filter rows based on the condition
m = df_large.filter(like='DTYPX').isin(types_to_include).values

# Filter out the rows that don't meet the condition
new = df_large.filter(like='DIAG').where(m, 'NULL')

# Update the original DataFrame
df_large.update(new)

The resulting DataFrame will have all rows where the corresponding diagnosis type is in the types_to_include list replaced with ‘NULL’.

Note: The above code assumes that you want to filter out only the rows where the diagnosis type does not exist in the types_to_include list. If you want to filter out all rows, regardless of whether the diagnosis type exists or not, you can remove the conditional logic and use the following step instead:

# Filter out all rows that don't meet the condition
new = df_large.filter(like='DIAG').where(m, 'NULL')

This will replace all rows where the corresponding diagnosis type does not exist in the types_to_include list with ‘NULL’.

Last modified on 2024-01-11