This code creates an empty Pandas DataFrame named train
and initializes it with image and label data by calling the createdataframe
function, which returns the training set data. The returned data is then unpacked and assigned as separate columns to the DataFrame.
train = pd.DataFrame()
train['image'], train['label'] = createdataframe(TRAIN_DIR)
```markdown
# Import necessary libraries
import pandas as pd
# Define a function to create the train dataframe
def create_train_dataframe(train_dir):
"""
Creates a train dataframe from the given directory.
Args:
train_dir (str): Path to the directory containing the training data.
Returns:
tuple: A tuple containing the train dataframe and the label list.
"""
# Create an empty dataframe to store the training data
train_df = pd.DataFrame()
# Create a dataframe from the training data
train_df['image'], train_df['label'] = createdataframe(train_dir)
# Return the train dataframe
return train_df
# Load the train data from the specified directory
train_dir = TRAIN_DIR
train_df = create_train_dataframe(train_dir)
```
Note: Without the definition of `createdataframe()`, I assumed that it returns a tuple of two lists or dataframes. The code above assumes that `createdataframe()` returns a tuple where the first element is a list of image paths and the second element is a list of labels.
train = pd.DataFrame()
train
.train['image'], train['label'] = createdataframe(TRAIN_DIR)
createdataframe
function, passing the TRAIN_DIR
directory as an argument.image
and label
.train
DataFrame: image
and label
.createdataframe
function is not shown in this code snippet, but it is assumed to return the image and label data for the training set.