Working with Multiple Pandas DataFrames in Python
In this article, we will explore how to create a multidimensional numpy array from multiple pandas DataFrames. This problem is often encountered when dealing with image classification tasks, where each image contains one or more classes of objects.
Introduction to the Problem
The problem at hand involves taking 5 pandas DataFrames, each representing a class of objects in images, and merging them into a single multidimensional numpy array while maintaining the unique image_id for each object.
Let’s start by looking at an example DataFrame:
| image_id | x | y |
|-------------|--------|---|
| image_0 | 4835 | 106|
| image_0 | 2609 | 309|
| image_0 | 2891 | 412|
| image_0 | 1823 | 431|
| image_0 | 3309 | 449|
| image_945 | 950 | 1238|
| image_945 | 34 | 1362|
| image_945 | 821 | 2059|
| image_945 | 1448 | 2896|
This DataFrame represents class 1 and has 9 rows.
We can create similar DataFrames for classes 2-5, each with its own unique image_id values:
# Class 2
| image_id | x | y |
|--------------|--------|---|
| image_0 | 4835 | 106|
| image_0 | 2609 | 309|
| image_0 | 2891 | 412|
| image_0 | 1823 | 431|
| image_0 | 3309 | 449|
| image_945 | 9530 | 128|
| image_945 | 354 | 162|
| image_945 | 8321 | 259|
| image_945 | 1448 | 2596|
# Class 3
| image_id | x | y |
|--------------|--------|---|
| image_0 | 1234 | 456|
| image_0 | 2345 | 789|
| image_0 | 3456 | 012|
| image_0 | 4567 | 123|
| image_0 | 5678 | 234|
| image_945 | 90 | 1111|
| image_945 | 12 | 2222|
| image_945 | 33 | 3333|
| image_945 | 44 | 4444|
# Class 4
| image_id | x | y |
|--------------|--------|---|
| image_0 | 1011 | 1314|
| image_0 | 1516 | 2323|
| image_0 | 2022 | 3131|
| image_0 | 2534 | 4044|
| image_0 | 3047 | 5055|
| image_945 | 101010 | 131313|
| image_945 | 151515 | 232323|
| image_945 | 202020 | 313333|
| image_945 | 253254 | 404444|
# Class 5
| image_id | x | y |
|--------------|--------|---|
| image_0 | 4545 | 6566|
| image_0 | 5050 | 7677|
| image_0 | 5561 | 8788|
| image_0 | 6074 | 9899|
| image_945 | 45 | 66
| image_945 | 56 | 77
| image_945 | 67 | 88
| image_945 | 78 | 99
As you can see, each class has its own unique image_id values.
Creating a Dictionary of DataFrames
To create a multidimensional numpy array from these DataFrames, we need to group them by their respective classes and then apply the following operations:
- Grouping by
image_idusing pandas’groupbyfunction - Applying a lambda function that applies the
zipoperation to each group of values (x and y) in the grouped DataFrame - Unstacking the resulting array
Let’s start with grouping the DataFrames by their respective classes:
# Import necessary libraries
import pandas as pd
# Create DataFrames for each class
cls1 = pd.DataFrame({
'image_id': ['image_0', 'image_0', 'image_0', 'image_0', 'image_0'],
'x': [4835, 2609, 2891, 1823, 3309],
'y': [106, 309, 412, 431, 449]
})
cls2 = pd.DataFrame({
'image_id': ['image_0', 'image_0', 'image_0'],
'x': [4835, 2609, 2891],
'y': [106, 309, 412]
})
# Add more DataFrames for classes 3-5
We can create similar DataFrames for classes 3-5:
cls3 = pd.DataFrame({
'image_id': ['image_0', 'image_0'],
'x': [1234, 2345],
'y': [456, 789]
})
cls4 = pd.DataFrame({
'image_id': ['image_0', 'image_0'],
'x': [1011, 1516],
'y': [1314, 2323]
})
cls5 = pd.DataFrame({
'image_id': ['image_0', 'image_945'],
'x': [4545, 45],
'y': [6566, 66]
})
Now that we have all the DataFrames for each class, let’s create a dictionary to hold them:
# Create a dictionary of DataFrames
clss = {
'class 1': cls1,
'class 2': cls2,
'class 3': cls3,
'class 4': cls4,
'class 5': cls5
}
Next, let’s group the DataFrames by their respective classes and apply the lambda function:
# Group the DataFrames by their respective classes
catted = pd.concat(clss)
# Apply a lambda function that applies the zip operation to each group of values (x and y) in the grouped DataFrame
g = catted.groupby(['image_id', pd.Grouper(level=0)])[['x', 'y']]
Now we can apply the apply method to the grouped DataFrame:
# Apply a lambda function that applies the zip operation to each group of values (x and y) in the grouped DataFrame
g.apply(lambda x: list(zip(*x.values.T))).unstack()
This will create a multidimensional numpy array where each row corresponds to an object in the original DataFrames, with its image_id, x-coordinate, and y-coordinate.
Here’s the complete code:
# Import necessary libraries
import pandas as pd
# Create DataFrames for each class
cls1 = pd.DataFrame({
'image_id': ['image_0', 'image_0', 'image_0', 'image_0', 'image_0'],
'x': [4835, 2609, 2891, 1823, 3309],
'y': [106, 309, 412, 431, 449]
})
cls2 = pd.DataFrame({
'image_id': ['image_0', 'image_0', 'image_0'],
'x': [4835, 2609, 2891],
'y': [106, 309, 412]
})
cls3 = pd.DataFrame({
'image_id': ['image_0', 'image_0'],
'x': [1234, 2345],
'y': [456, 789]
})
cls4 = pd.DataFrame({
'image_id': ['image_0', 'image_0'],
'x': [1011, 1516],
'y': [1314, 2323]
})
cls5 = pd.DataFrame({
'image_id': ['image_0', 'image_945'],
'x': [4545, 45],
'y': [6566, 66]
})
# Create a dictionary of DataFrames
clss = {
'class 1': cls1,
'class 2': cls2,
'class 3': cls3,
'class 4': cls4,
'class 5': cls5
}
# Group the DataFrames by their respective classes
catted = pd.concat(clss)
# Apply a lambda function that applies the zip operation to each group of values (x and y) in the grouped DataFrame
g = catted.groupby(['image_id', pd.Grouper(level=0)])[['x', 'y']]
g.apply(lambda x: list(zip(*x.values.T))).unstack()
When you run this code, it will output a multidimensional numpy array where each row corresponds to an object in the original DataFrames, with its image_id, x-coordinate, and y-coordinate.
This is a basic example of how to create a multidimensional numpy array from multiple pandas DataFrames. Depending on your specific use case, you may need to modify this code or add additional operations to suit your needs.
Last modified on 2025-03-10