Source code for statsmodels.datasets.utils

from statsmodels.compat.python import (range, StringIO, urlopen, HTTPError,
                                       lrange, cPickle, urljoin)
import sys
import shutil
from os import environ
from os import makedirs
from os.path import expanduser
from os.path import exists
from os.path import join

import numpy as np
from numpy import array
from pandas import read_csv, DataFrame, Index

[docs]def webuse(data, baseurl='', as_df=True): """ Parameters ---------- data : str Name of dataset to fetch. baseurl : str The base URL to the stata datasets. as_df : bool If True, returns a `pandas.DataFrame` Returns ------- dta : Record Array A record array containing the Stata dataset. Examples -------- >>> dta = webuse('auto') Notes ----- Make sure baseurl has trailing forward slash. Doesn't do any error checking in response URLs. """ # lazy imports from statsmodels.iolib import genfromdta url = urljoin(baseurl, data+'.dta') dta = urlopen(url) dta = StringIO( # make it truly file-like if as_df: # could make this faster if we don't process dta twice? return DataFrame.from_records(genfromdta(dta)) else: return genfromdta(dta)
class Dataset(dict): def __init__(self, **kw): # define some default attributes, so pylint can find them self.endog = None self.exog = None = None self.names = None dict.__init__(self, kw) self.__dict__ = self # Some datasets have string variables. If you want a raw_data # attribute you must create this in the dataset's load function. try: # some datasets have string variables self.raw_data =, len(self.names))) except: pass def __repr__(self): return str(self.__class__) def process_recarray(data, endog_idx=0, exog_idx=None, stack=True, dtype=None): names = list(data.dtype.names) if isinstance(endog_idx, int): endog = array(data[names[endog_idx]], dtype=dtype) endog_name = names[endog_idx] endog_idx = [endog_idx] else: endog_name = [names[i] for i in endog_idx] if stack: endog = np.column_stack(data[field] for field in endog_name) else: endog = data[endog_name] if exog_idx is None: exog_name = [names[i] for i in range(len(names)) if i not in endog_idx] else: exog_name = [names[i] for i in exog_idx] if stack: exog = np.column_stack(data[field] for field in exog_name) else: exog = data[exog_name] if dtype: endog = endog.astype(dtype) exog = exog.astype(dtype) dataset = Dataset(data=data, names=names, endog=endog, exog=exog, endog_name=endog_name, exog_name=exog_name) return dataset def process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=None, index_idx=None): data = DataFrame(data, dtype=dtype) names = data.columns if isinstance(endog_idx, int): endog_name = names[endog_idx] endog = data[endog_name] if exog_idx is None: exog = data.drop([endog_name], axis=1) else: exog = data.filter(names[exog_idx]) else: endog = data.ix[:, endog_idx] endog_name = list(endog.columns) if exog_idx is None: exog = data.drop(endog_name, axis=1) elif isinstance(exog_idx, int): exog = data.filter([names[exog_idx]]) else: exog = data.filter(names[exog_idx]) if index_idx is not None: # NOTE: will have to be improved for dates endog.index = Index(data.ix[:, index_idx]) exog.index = Index(data.ix[:, index_idx]) data = data.set_index(names[index_idx]) exog_name = list(exog.columns) dataset = Dataset(data=data, names=list(names), endog=endog, exog=exog, endog_name=endog_name, exog_name=exog_name) return dataset def _maybe_reset_index(data): """ All the Rdatasets have the integer row.labels from R if there is no real index. Strip this for a zero-based index """ if data.index.equals(Index(lrange(1, len(data) + 1))): data = data.reset_index(drop=True) return data def _get_cache(cache): if cache is False: # do not do any caching or load from cache cache = None elif cache is True: # use default dir for cache cache = get_data_home(None) else: cache = get_data_home(cache) return cache def _cache_it(data, cache_path): if sys.version_info[0] >= 3: # for some reason encode("zip") won't work for me in Python 3? import zlib # use protocol 2 so can open with python 2.x if cached in 3.x open(cache_path, "wb").write(zlib.compress(cPickle.dumps(data, protocol=2))) else: open(cache_path, "wb").write(cPickle.dumps(data).encode("zip")) def _open_cache(cache_path): if sys.version_info[0] >= 3: # NOTE: don't know why but decode('zip') doesn't work on my # Python 3 build import zlib data = zlib.decompress(open(cache_path, 'rb').read()) # return as bytes object encoded in utf-8 for cross-compat of cached data = cPickle.loads(data).encode('utf-8') else: data = open(cache_path, 'rb').read().decode('zip') data = cPickle.loads(data) return data def _urlopen_cached(url, cache): """ Tries to load data from cache location otherwise downloads it. If it downloads the data and cache is not None then it will put the downloaded data in the cache path. """ from_cache = False if cache is not None: cache_path = join(cache, url.split("://")[-1].replace('/', ',') + ".zip") try: data = _open_cache(cache_path) from_cache = True except: pass # not using the cache or didn't find it in cache if not from_cache: data = urlopen(url).read() if cache is not None: # then put it in the cache _cache_it(data, cache_path) return data, from_cache def _get_data(base_url, dataname, cache, extension="csv"): url = base_url + (dataname + ".%s") % extension try: data, from_cache = _urlopen_cached(url, cache) except HTTPError as err: if '404' in str(err): raise ValueError("Dataset %s was not found." % dataname) else: raise err data = data.decode('utf-8', 'strict') return StringIO(data), from_cache def _get_dataset_meta(dataname, package, cache): # get the index, you'll probably want this cached because you have # to download info about all the data to get info about any of the data... index_url = ("" "datasets.csv") data, _ = _urlopen_cached(index_url, cache) # Python 3 if sys.version[0] == '3': # pragma: no cover data = data.decode('utf-8', 'strict') index = read_csv(StringIO(data)) idx = np.logical_and(index.Item == dataname, index.Package == package) dataset_meta = index.ix[idx] return dataset_meta["Title"].item()
[docs]def get_rdataset(dataname, package="datasets", cache=False): """download and return R dataset Parameters ---------- dataname : str The name of the dataset you want to download package : str The package in which the dataset is found. The default is the core 'datasets' package. cache : bool or str If True, will download this data into the STATSMODELS_DATA folder. The default location is a folder called statsmodels_data in the user home folder. Otherwise, you can specify a path to a folder to use for caching the data. If False, the data will not be cached. Returns ------- dataset : Dataset instance A `` instance. This objects has attributes:: * data - A pandas DataFrame containing the data * title - The dataset title * package - The package from which the data came * from_cache - Whether not cached data was retrieved * __doc__ - The verbatim R documentation. Notes ----- If the R dataset has an integer index. This is reset to be zero-based. Otherwise the index is preserved. The caching facilities are dumb. That is, no download dates, e-tags, or otherwise identifying information is checked to see if the data should be downloaded again or not. If the dataset is in the cache, it's used. """ # NOTE: use raw github bc html site might not be most up to date data_base_url = ("" "master/csv/"+package+"/") docs_base_url = ("" "master/doc/"+package+"/rst/") cache = _get_cache(cache) data, from_cache = _get_data(data_base_url, dataname, cache) data = read_csv(data, index_col=0) data = _maybe_reset_index(data) title = _get_dataset_meta(dataname, package, cache) doc, _ = _get_data(docs_base_url, dataname, cache, "rst") return Dataset(data=data,, package=package, title=title, from_cache=from_cache) # The below function were taken from sklearn
[docs]def get_data_home(data_home=None): """Return the path of the statsmodels data dir. This folder is used by some large dataset loaders to avoid downloading the data several times. By default the data dir is set to a folder named 'statsmodels_data' in the user home folder. Alternatively, it can be set by the 'STATSMODELS_DATA' environment variable or programatically by giving an explit folder path. The '~' symbol is expanded to the user home folder. If the folder does not already exist, it is automatically created. """ if data_home is None: data_home = environ.get('STATSMODELS_DATA', join('~', 'statsmodels_data')) data_home = expanduser(data_home) if not exists(data_home): makedirs(data_home) return data_home
[docs]def clear_data_home(data_home=None): """Delete all the content of the data home cache.""" data_home = get_data_home(data_home) shutil.rmtree(data_home)