Converting a NumPy Array Shape (32 by xxx) into a Single Column DataFrame

Problem Statement and Background

In this post, we’ll explore how to convert a NumPy array with shape (32, xxx) into a single column DataFrame while discarding the last ten objects of this array. We’ll also discuss the importance of handling NaN values in DataFrames.

The problem arises when trying to create a DataFrame from a NumPy array that has been reshaped or flattened to accommodate the last few missing values. In our case, we have a binary classification NLP model that produces predictions with shape (32, 300), where the last batch of size 24 is represented by the last 16 rows.

The code snippet provided demonstrates how the author attempts to create a DataFrame from this prediction array:

predictions.append(logits.argmax(1))
df.labels = pd.DataFrame(predictions)

However, the resulting DataFrame has shape (32,), and we want it to have shape (32, 1) with integer values (0 or 1). Moreover, the last ten objects of the array should be discarded.

Understanding NaN Values in DataFrames

Before delving into the solution, let’s discuss why NaN values are problematic when working with DataFrames. In Pandas, NaN (Not a Number) is used to represent missing or undefined values. When working with numerical data types, NaN values can lead to issues during calculations and transformations.

Flattening the NumPy Array

To create a single column DataFrame from the flattened NumPy array, we need to flatten it first:

predictions = predictions.flatten()

This step is crucial because our prediction variable has shape (32, 10), but we want it to have shape (32, 1) for better performance in certain operations.

Appending the Last Batch

To append the last batch of size 24 to our flattened array, we can use the following code:

to_append = logits.argmax(1)
predictions = predictions.append(to_append)

However, this approach has a flaw. When appending a row to a DataFrame with a different shape, Pandas will automatically fill in NaN values for columns that are not present in the new row. In our case, we’ll end up with NaN values from 23 to 32.

Handling NaN Values

To avoid filling in NaN values and instead discard them, we can use the following code:

df = df.dropna()

However, this approach will drop all rows that contain at least one NaN value. Instead, we want to keep only the first 32 rows (excluding the last 10) while discarding the remaining NaN values.

Imputing Missing Values with a Constant Value

Another possible solution is to impute missing values with a constant integer value, such as 888. We can use the following code:

df = df.fillna(888)

Then, we can transform all values to int using the astype function:

df = df.astype('int16')

Solution

To create a single column DataFrame from our flattened NumPy array while discarding the last ten objects, we’ll use a combination of the above steps. Here’s the complete code snippet:

import numpy as np
import pandas as pd

# Create a sample prediction array with shape (32, 300)
np.random.seed(0)
predictions = np.random.randint(2, size=(32, 300))

# Flatten the NumPy array
predictions = predictions.flatten()

# Append the last batch of size 24 to our flattened array
last_batch_argmax = np.argmax(predictions[23:], axis=1)[:, :20]
predictions = np.concatenate((predictions[:23], last_batch_argmax), axis=0)

# Create a DataFrame from the flattened NumPy array
df = pd.DataFrame(predictions, columns=['labels'])

# Fill NaN values with a constant integer value (888)
df = df.fillna(888)

# Transform all values to int using the astype function
df = df.astype('int16')

print(df.head())

By following these steps and understanding how Pandas handles NaN values and array shapes, we can create a single column DataFrame from our flattened NumPy array while discarding the last ten objects.

Last modified on 2024-07-28