Pandas

Pandas is a widely used Python data analysis toolkit. Since it uses fsspec to read and write remote data, you can use the Hugging Face paths (hf://) to read and write data on the Hub.

Load a DataFrame

You can load data from local files or from remote storage like Hugging Face Datasets. Pandas supports many formats including CSV, JSON and Parquet:

>>> import pandas as pd
>>> df = pd.read_csv("path/to/data.csv")

To load a file from Hugging Face, the path needs to start with hf://. For example, the path to the stanfordnlp/imdb dataset repository is hf://datasets/stanfordnlp/imdb. The dataset on Hugging Face contains multiple Parquet files. The Parquet file format is designed to make reading and writing data frames efficient, and to make sharing data across data analysis languages easy. Here is how to load the file plain_text/train-00000-of-00001.parquet:

>>> import pandas as pd
>>> df = pd.read_parquet("hf://datasets/stanfordnlp/imdb/plain_text/train-00000-of-00001.parquet")
>>> df
                                                    text  label
0      I rented I AM CURIOUS-YELLOW from my video sto...      0
1      "I Am Curious: Yellow" is a risible and preten...      0
2      If only to avoid making this type of film in t...      0
3      This film was probably inspired by Godard's Ma...      0
4      Oh, brother...after hearing about this ridicul...      0
...                                                  ...    ...
24995  A hit at the time but now better categorised a...      1
24996  I love this movie like no other. Another time ...      1
24997  This film and it's sequel Barry Mckenzie holds...      1
24998  'The Adventures Of Barry McKenzie' started lif...      1
24999  The story centers around Barry McKenzie who mu...      1

For more information on the Hugging Face paths and how they are implemented, please refer to the the client library’s documentation on the HfFileSystem.

Save a DataFrame

You can save a pandas DataFrame using to_csv/to_json/to_parquet to a local file or to Hugging Face directly.

To save the DataFrame on Hugging Face, you first need to Login with your Hugging Face account, for example using:

huggingface-cli login

Then you can Create a dataset repository, for example using:

from huggingface_hub import HfApi

HfApi().create_repo(repo_id="username/my_dataset", repo_type="dataset")

Finally, you can use Hugging Face paths in Pandas:

import pandas as pd

df.to_parquet("hf://datasets/username/my_dataset/imdb.parquet")

# or write in separate files if the dataset has train/validation/test splits
df_train.to_parquet("hf://datasets/username/my_dataset/train.parquet")
df_valid.to_parquet("hf://datasets/username/my_dataset/validation.parquet")
df_test .to_parquet("hf://datasets/username/my_dataset/test.parquet")

Use Images

You can load a folder with a metadata file containing a field for the names or paths to the images, structured like this:

Example 1:            Example 2:
folder/               folder/
├── metadata.csv      ├── metadata.csv
├── img000.png        └── images
├── img001.png            ├── img000.png
...                       ...
└── imgNNN.png            └── imgNNN.png

You can iterate on the images paths like this:

import pandas as pd

folder_path = "path/to/folder/"
df = pd.read_csv(folder_path + "metadata.csv")
for image_path in (folder_path + df["file_name"]):
    ...

Since the dataset is in a supported structure (a metadata.csv or .jsonl file with a file_name field), you can save this dataset to Hugging Face and the Dataset Viewer shows both the metadata and images on Hugging Face.

from huggingface_hub import HfApi
api = HfApi()

api.upload_folder(
    folder_path=folder_path,
    repo_id="username/my_image_dataset",
    repo_type="dataset",
)

Image methods and Parquet

Using pandas-image-methods you enable PIL.Image methods on an image column. It also enables saving the dataset as one single Parquet file containing both the images and the metadata:

import pandas as pd
from pandas_image_methods import PILMethods

pd.api.extensions.register_series_accessor("pil")(PILMethods)

df["image"] = (folder_path + df["file_name"]).pil.open()
df.to_parquet("data.parquet")

All the PIL.Image methods are available, e.g.

df["image"] = df["image"].pil.rotate(90)

Use Audios

You can load a folder with a metadata file containing a field for the names or paths to the audios, structured like this:

Example 1:            Example 2:
folder/               folder/
├── metadata.csv      ├── metadata.csv
├── rec000.wav        └── audios
├── rec001.wav            ├── rec000.wav
...                       ...
└── recNNN.wav            └── recNNN.wav

You can iterate on the audios paths like this:

import pandas as pd

folder_path = "path/to/folder/"
df = pd.read_csv(folder_path + "metadata.csv")
for audio_path in (folder_path + df["file_name"]):
    ...

Since the dataset is in a supported structure (a metadata.csv or .jsonl file with a file_name field), you can save it to Hugging Face, and the Hub Dataset Viewer shows both the metadata and audio.

from huggingface_hub import HfApi
api = HfApi()

api.upload_folder(
    folder_path=folder_path,
    repo_id="username/my_audio_dataset",
    repo_type="dataset",
)

Audio methods and Parquet

Using pandas-audio-methods you enable soundfile methods on an audio column. It also enables saving the dataset as one single Parquet file containing both the audios and the metadata:

import pandas as pd
from pandas_image_methods import SFMethods

pd.api.extensions.register_series_accessor("sf")(SFMethods)

df["audio"] = (folder_path + df["file_name"]).sf.open()
df.to_parquet("data.parquet")

This makes it easy to use with librosa e.g. for resampling:

df["audio"] = [librosa.load(audio, sr=16_000) for audio in df["audio"]]
df["audio"] = df["audio"].sf.write()

Use Transformers

You can use transformers pipelines on pandas DataFrames to classify, generate text, images, etc. This section shows a few examples with tqdm for progress bars.

Pipelines don’t accept a tqdm object as input but you can use a python generator instead, in the form x for x in tqdm(...)

Text Classification

from transformers import pipeline
from tqdm import tqdm

pipe = pipeline("text-classification", model="clapAI/modernBERT-base-multilingual-sentiment")

# Compute labels
df["label"] = [y["label"] for y in pipe(x for x in tqdm(df["text"]))]
# Compute labels and scores
df[["label", "score"]] = [(y["label"], y["score"]) for y in pipe(x for x in tqdm(df["text"]))]

Text Generation

from transformers import pipeline
from tqdm import tqdm

pipe = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B-Instruct")

# Generate chat response
prompt = "What is the main topic of this sentence ? REPLY IN LESS THAN 3 WORDS. Sentence: '{}'"
df["output"] = [y["generated_text"][1]["content"] for y in pipe([{"role": "user", "content": prompt.format(x)}] for x in tqdm(df["text"]))]

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