Load shoe data

Let's start by reading the SoleMates shoe dataset.

We'll transform this data into embeddings and later store everything in the cloud-based Pinecone vector database.

Go ahead and load the dataset:

Jupyter Notebook

# Load the SoleMates shoe dataset
df_shoes = pd.read_csv('data/solemates_shoe_directory.csv')

# Convert 'color_details' from string representation of a list to an actual list
df_shoes['color_details'] = df_shoes['color_details'].apply(ast.literal_eval)

# Display the first few rows of the dataset
df_shoes.head()

You'll see this table in your Notebook:

http://localhost:3000/notebooks/jupyter_notebook.ipynb

product_title	gender	product_type	color	usage	color_details	heel_height	heel_type	price_usd	brand	product_id	image
Puma men future cat remix sf black casual shoes	men	casual shoes	black	casual	[]	nan	nan	220	puma	1	1.jpg
Buckaroo men flores black formal shoes	men	formal shoes	black	formal	[]	nan	nan	155	buckaroo	2	2.jpg
Gas men europa white shoes	men	casual shoes	white	casual	[]	nan	nan	105	gas	3	3.jpg
Nike men's incinerate msl white blue shoe	men	sports shoes	white	sports	['blue']	nan	nan	125	nike	4	4.jpg
Clarks men hang work leather black formal shoes	men	formal shoes	black	formal	[]	nan	nan	220	clarks	5	5.jpg

The shoe product data consists of 12 columns:

• product_title
• gender
• product_type
• color
• usage
• color_details
• heel_height
• heel_type
• price_usd
• brand
• product_id
• image

Let's also have a look at the first 5 shoes:

Jupyter Notebook

width = 100
images_html = ""
image_data_path = 'data/footwear'
for img_file in df_shoes.head()['image']:
    img_path = os.path.join(image_data_path, img_file)
    # Add each image as an HTML <img> tag
    images_html += f'<img src="{img_path}" style="width:{width}px; margin-right:10px;">'
# Display all images in a row using HTML
display(HTML(f'<div style="display: flex; align-items: center;">{images_html}</div>'))

Run this cell and the cell output should look something like this:

http://localhost:3000/notebooks/jupyter_notebook.ipynb

Let's vectorize out shoe product data in the next lesson.