More pandas docs

Author: lhoestq

docs/hub/datasets-pandas.md

@@ -1,9 +1,44 @@
 # Pandas
 
 [Pandas](https://github.com/pandas-dev/pandas) is a widely used Python data analysis toolkit.
-Since it uses [fsspec](https://filesystem-spec.readthedocs.io) to read and write remote data, you can use the Hugging Face paths ([`hf://`](/docs/huggingface_hub/guides/hf_file_system#integrations)) to read and write data on the Hub:
+Since it uses [fsspec](https://filesystem-spec.readthedocs.io) to read and write remote data, you can use the Hugging Face paths ([`hf://`](/docs/huggingface_hub/guides/hf_file_system#integrations)) to read and write data on the Hub.
 
-First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
+## 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 Paequet:
+
+```python
+>>> 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](https://huggingface.co/datasets/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`:
+
+```python
+>>> 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
+```
+
+To have more information on the Hugging Face paths and how they are implemented, please refer to the [the client library's documentation on the HfFileSystem](/docs/huggingface_hub/guides/hf_file_system).
+
+## 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](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
 huggingface-cli login
@@ -22,26 +57,93 @@ Finally, you can use [Hugging Face paths](/docs/huggingface_hub/guides/hf_file_s
 ```python
 import pandas as pd
 
-df.to_parquet("hf://datasets/username/my_dataset/data.parquet")
+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")
 ```
 
-This creates a dataset repository `username/my_dataset` containing your Pandas dataset in Parquet format.
-You can reload it later:
+## Use Images
+
+From a metadata file containing a "file_name" field for the names or paths to the images:
+
+```
+data/                 data/
+├── metadata.csv      ├── metadata.csv
+├── img000.png        └── images
+├── img001.png            ├── img000.png
+...                       ...
+└── imgNNN.png            └── imgNNN.png
+```
 
 ```python
 import pandas as pd
 
-df = pd.read_parquet("hf://datasets/username/my_dataset/data.parquet")
+folder_path = "path/to/data/"
+df = pd.read_csv(folder_path + "metadata.csv")
+for image_path in (folder_path + df["file_name"]):
+    ...
+```
 
-# or read from separate files if the dataset has train/validation/test splits
-df_train = pd.read_parquet("hf://datasets/username/my_dataset/train.parquet")
-df_valid = pd.read_parquet("hf://datasets/username/my_dataset/validation.parquet")
-df_test  = pd.read_parquet("hf://datasets/username/my_dataset/test.parquet")
+Since the dataset is in a supported structure, you can save this dataset to Hugging Face and the Dataset Viewer shows both the metadata and images on HF.
+
+```python
+from huggingface_hub import HfApi
+api = HfApi()
+
+api.upload_folder(
+    folder_path=folder_path,
+    repo_id="username/my_image_dataset",
+    repo_type="dataset",
+)
 ```
 
-To have more information on the Hugging Face paths and how they are implemented, please refer to the [the client library's documentation on the HfFileSystem](/docs/huggingface_hub/guides/hf_file_system).
+Using [pandas-image-methods](https://github.com/lhoestq/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:
+
+```python
+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.
+
+```python
+df["image"] = df["image"].pil.rotate(90)
+```
+
+## Use Transformers
+
+You can use `transformers` pipelines on pandas DataFrames to classify, generate text, images, etc.
+This section shows a few examples.
+
+### Text Classification
+
+```python
+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
+
+```python
+from transformers import pipeline
+from tqdm import tqdm
+
+p = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B-Instruct")
+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"]))]
+```