def cache_zipped_file(file_name: str, url: str) -> Path:
"""Check if file_name already is in the data path, otherwise download it from url.
Args:
file_name: the file name
url: the URL of the dataset
Returns:
The relative path to the dataset
"""
data_path = get_data_path()
data_path.mkdir(exist_ok=True)
file_path = data_path / file_name
# If not exists, download and create file
if not file_path.exists():
response = requests.get(url)
if response.status_code != 200:
raise FileNotFoundError("Could not download resource")
tmp_path = data_path / "tmp.zip"
tmp_path.write_bytes(response.content)
with zipfile.ZipFile(tmp_path, "r") as zip_file:
zip_file.extract(file_path.name, data_path)
tmp_path.unlink()
return file_path
def cache_file(file_name, url):
# Cache all datasets here
data_path = get_data_path()
data_path.mkdir(exist_ok=True)
# If not exists, download and create file
if not (data_path / file_name).exists():
data = requests.get(url)
(data_path / file_name).write_bytes(data.content)
return data_path / file_name
def cache_file(file_name: str, url: str) -> Path:
"""Check if file_name already is in the data path, otherwise download it from url.
Args:
file_name: the file name
url: the URL of the dataset
Returns:
The relative path to the dataset
"""
data_path = get_data_path()
data_path.mkdir(exist_ok=True)
# If not exists, download and create file
if not (data_path / file_name).exists():
data = requests.get(url)
(data_path / file_name).write_bytes(data.content)
return data_path / file_name
import kaggle
import pandas as pd
import pandas_profiling
from pandas_profiling.utils.paths import get_data_path
# The dataset in this example is obtained using the `kaggle` api.
# If you haven't done so already, you should set up the api credentials:
# https://github.com/Kaggle/kaggle-api#api-credentials
kaggle.api.authenticate()
# Download the dataset. Note that we use a small dataset as this example is automated.
# However, popular larger files shouldn't be a problem (LFW, CelebA).
data_path = get_data_path() / "cat-and-dog"
kaggle.api.dataset_download_files(
"tongpython/cat-and-dog",
path=str(data_path),
quiet=False,
unzip=True,
)
# At the first run, we find that the dataset not only contains images, "_DS_Store" and "cat-and-dog.zip" are present too.
# We remove these by narrowing our glob search
files = [f for f in data_path.rglob("*.jpg") if f.is_file()]
series = pd.Series(files, name="files")
# PP only accepts absolute paths
series = series.apply(lambda x: x.absolute()).apply(str)
df = pd.DataFrame(series)
import kaggle
import pandas as pd
import pandas_profiling
from pandas_profiling.utils.paths import get_data_path
# The dataset in this example is obtained using the `kaggle` api.
# If you haven't done so already, you should set up the api credentials:
# https://github.com/Kaggle/kaggle-api#api-credentials
kaggle.api.authenticate()
# Download the dataset. Note that we use a small dataset as this example is automated.
# However, popular larger files shouldn't be a problem (LFW, CelebA).
data_path = get_data_path() / "celebrity-faces"
kaggle.api.dataset_download_files(
"dansbecker/5-celebrity-faces-dataset",
path=str(data_path),
quiet=False,
unzip=True,
)
p = data_path / "data/train/"
# As not all datasets have an index of files, we generate that ourselves.
files = [f for f in p.rglob("*") if f.is_file()]
series = pd.Series(files, name="files")
# PP only accepts absolute paths
series = series.apply(lambda x: x.absolute()).apply(str)
df = pd.DataFrame(series)
