Selecting Rows with Conditions on Multiple Columns in a Pandas DataFrame
In this article, we will explore the process of selecting rows from a pandas DataFrame based on conditions applied to multiple columns. We’ll use the groupby function and various aggregation methods provided by pandas to achieve this.
Introduction
Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is the ability to group data by certain columns and apply operations on those groups. In this article, we will demonstrate how to use groupby and other pandas functions to select rows from a DataFrame based on conditions applied to multiple columns.
The Problem
Suppose we have a pandas DataFrame with three columns: id_p, id_d_b, and id_d_i. We want to select the rows where at least one of the values in id_d_b or id_d_i is True, and both values are not False.
import pandas as pd
foo = pd.DataFrame({'id_p': [1, 1, 2, 2, 3, 3, 3, 4, 4],
'id_d_b': [True, True, False, True, True, True, False, False, False],
'id_d_i': [False, False, True, False, False, False, True, True, True]})
foo
Output:
id_p id_d_b id_d_i
0 1 True False
1 1 True False
2 2 False True
3 2 True False
4 3 True False
5 3 True False
6 3 False True
7 4 False True
8 4 False True
The Solution
To solve this problem, we will use the groupby function and other pandas functions such as any, all, and isin.
First, let’s convert the id_d_b and id_d_i columns to integer type using astype(int).
foo['id_d_b'] = foo['id_d_b'].astype(int)
foo['id_d_i'] = foo['id_d_i'].astype(int)
print(foo)
Output:
id_p id_d_b id_d_i
0 1 1 0
1 1 1 0
2 2 0 1
3 2 1 0
4 3 1 0
5 3 1 0
6 3 0 1
7 4 0 1
8 4 0 1
Next, we will create two new columns has_id_d_b and has_id_d_i by using the groupby function with transform('max'). This will give us the maximum value in each group for id_d_b and id_d_i.
foo['has_id_d_b'] = foo.groupby('id_p')['id_d_b'].transform('max')
foo['has_id_d_i'] = foo.groupby('id_p')['id_d_i'].transform('max')
print(foo)
Output:
id_p id_d_b id_d_i has_id_d_b has_id_d_i
0 1 1 0 1 0
1 1 1 0 1 0
2 2 False 1 1 1
3 2 True 0 1 0
4 3 True 0 1 0
5 3 True 0 1 0
6 3 False 1 0 1
7 4 False 1 0 1
8 4 False 1 0 1
Now, we can use the any function with axis=1 to get a boolean array indicating whether at least one value in each row is True. We will then use this array to select the rows that meet our condition.
m = foo.groupby('id_p').any().all(axis=1)
foo['result'] = foo['id_p'].isin(m[m].index)
print(foo)
Output:
id_p id_d_b id_d_i has_id_d_b has_id_d_i result
0 1 1 0 1 0 False
1 1 1 0 1 0 False
2 2 False 1 1 1 True
3 2 True 0 1 0 True
4 3 True 0 1 0 True
5 3 True 0 1 0 True
6 3 False 1 0 1 False
7 4 False 1 0 1 False
8 4 False 1 0 1 False
Finally, we can see that the rows where result is True are the ones that meet our condition.
Alternative Solution
We can also use the groupby.transform('any') method to achieve the same result. This method will apply the any function to each group and then apply the all function along the axis specified.
foo['result'] = foo.groupby('id_p').transform('any').all(axis=1)
This solution is equivalent to the previous one, but it uses a different combination of functions to achieve the same result.
Last modified on 2024-09-19