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1from statsmodels.compat.python import lrange
3from io import StringIO
4import shutil
5from os import environ, makedirs
6from os.path import expanduser, exists, dirname, abspath, join
7from urllib.error import HTTPError, URLError
8from urllib.request import urlopen
9from urllib.parse import urljoin
11import numpy as np
12from pandas import read_stata, read_csv, DataFrame, Series, Index
15def webuse(data, baseurl='https://www.stata-press.com/data/r11/', as_df=True):
16 """
17 Download and return an example dataset from Stata.
19 Parameters
20 ----------
21 data : str
22 Name of dataset to fetch.
23 baseurl : str
24 The base URL to the stata datasets.
25 as_df : bool
26 Deprecated. Always returns a DataFrame
28 Returns
29 -------
30 dta : DataFrame
31 A DataFrame containing the Stata dataset.
33 Examples
34 --------
35 >>> dta = webuse('auto')
37 Notes
38 -----
39 Make sure baseurl has trailing forward slash. Does not do any
40 error checking in response URLs.
41 """
42 url = urljoin(baseurl, data+'.dta')
43 return read_stata(url)
46class Dataset(dict):
47 def __init__(self, **kw):
48 # define some default attributes, so pylint can find them
49 self.endog = None
50 self.exog = None
51 self.data = None
52 self.names = None
54 dict.__init__(self, kw)
55 self.__dict__ = self
56 # Some datasets have string variables. If you want a raw_data
57 # attribute you must create this in the dataset's load function.
58 try: # some datasets have string variables
59 self.raw_data = self.data.astype(float)
60 except:
61 pass
63 def __repr__(self):
64 return str(self.__class__)
67def process_pandas(data, endog_idx=0, exog_idx=None, index_idx=None):
68 names = data.columns
70 if isinstance(endog_idx, int):
71 endog_name = names[endog_idx]
72 endog = data[endog_name].copy()
73 if exog_idx is None:
74 exog = data.drop([endog_name], axis=1)
75 else:
76 exog = data[names[exog_idx]].copy()
77 else:
78 endog = data.loc[:, endog_idx].copy()
79 endog_name = list(endog.columns)
80 if exog_idx is None:
81 exog = data.drop(endog_name, axis=1)
82 elif isinstance(exog_idx, int):
83 exog = data[names[exog_idx]].copy()
84 else:
85 exog = data[names[exog_idx]].copy()
87 if index_idx is not None: # NOTE: will have to be improved for dates
88 index = Index(data.iloc[:, index_idx])
89 endog.index = index
90 exog.index = index.copy()
91 data = data.set_index(names[index_idx])
93 exog_name = list(exog.columns)
94 dataset = Dataset(data=data, names=list(names), endog=endog,
95 exog=exog, endog_name=endog_name, exog_name=exog_name)
96 return dataset
99def _maybe_reset_index(data):
100 """
101 All the Rdatasets have the integer row.labels from R if there is no
102 real index. Strip this for a zero-based index
103 """
104 if data.index.equals(Index(lrange(1, len(data) + 1))):
105 data = data.reset_index(drop=True)
106 return data
109def _get_cache(cache):
110 if cache is False:
111 # do not do any caching or load from cache
112 cache = None
113 elif cache is True: # use default dir for cache
114 cache = get_data_home(None)
115 else:
116 cache = get_data_home(cache)
117 return cache
120def _cache_it(data, cache_path):
121 import zlib
122 open(cache_path, "wb").write(zlib.compress(data))
125def _open_cache(cache_path):
126 import zlib
127 data = zlib.decompress(open(cache_path, 'rb').read())
128 # return as bytes object encoded in utf-8 for cross-compat of cached
129 return data
132def _urlopen_cached(url, cache):
133 """
134 Tries to load data from cache location otherwise downloads it. If it
135 downloads the data and cache is not None then it will put the downloaded
136 data in the cache path.
137 """
138 from_cache = False
139 if cache is not None:
140 file_name = url.split("://")[-1].replace('/', ',')
141 file_name = file_name.split('.')
142 if len(file_name) > 1:
143 file_name[-2] += '-v2'
144 else:
145 file_name[0] += '-v2'
146 file_name = '.'.join(file_name) + ".zip"
147 cache_path = join(cache, file_name)
148 try:
149 data = _open_cache(cache_path)
150 from_cache = True
151 except:
152 pass
154 # not using the cache or did not find it in cache
155 if not from_cache:
156 data = urlopen(url, timeout=3).read()
157 if cache is not None: # then put it in the cache
158 _cache_it(data, cache_path)
159 return data, from_cache
162def _get_data(base_url, dataname, cache, extension="csv"):
163 url = base_url + (dataname + ".%s") % extension
164 try:
165 data, from_cache = _urlopen_cached(url, cache)
166 except HTTPError as err:
167 if '404' in str(err):
168 raise ValueError("Dataset %s was not found." % dataname)
169 else:
170 raise err
172 data = data.decode('utf-8', 'strict')
173 return StringIO(data), from_cache
176def _get_dataset_meta(dataname, package, cache):
177 # get the index, you'll probably want this cached because you have
178 # to download info about all the data to get info about any of the data...
179 index_url = ("https://raw.githubusercontent.com/vincentarelbundock/"
180 "Rdatasets/master/datasets.csv")
181 data, _ = _urlopen_cached(index_url, cache)
182 data = data.decode('utf-8', 'strict')
183 index = read_csv(StringIO(data))
184 idx = np.logical_and(index.Item == dataname, index.Package == package)
185 dataset_meta = index.loc[idx]
186 return dataset_meta["Title"].iloc[0]
189def get_rdataset(dataname, package="datasets", cache=False):
190 """download and return R dataset
192 Parameters
193 ----------
194 dataname : str
195 The name of the dataset you want to download
196 package : str
197 The package in which the dataset is found. The default is the core
198 'datasets' package.
199 cache : bool or str
200 If True, will download this data into the STATSMODELS_DATA folder.
201 The default location is a folder called statsmodels_data in the
202 user home folder. Otherwise, you can specify a path to a folder to
203 use for caching the data. If False, the data will not be cached.
205 Returns
206 -------
207 dataset : Dataset instance
208 A `statsmodels.data.utils.Dataset` instance. This objects has
209 attributes:
211 * data - A pandas DataFrame containing the data
212 * title - The dataset title
213 * package - The package from which the data came
214 * from_cache - Whether not cached data was retrieved
215 * __doc__ - The verbatim R documentation.
218 Notes
219 -----
220 If the R dataset has an integer index. This is reset to be zero-based.
221 Otherwise the index is preserved. The caching facilities are dumb. That
222 is, no download dates, e-tags, or otherwise identifying information
223 is checked to see if the data should be downloaded again or not. If the
224 dataset is in the cache, it's used.
225 """
226 # NOTE: use raw github bc html site might not be most up to date
227 data_base_url = ("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/"
228 "master/csv/"+package+"/")
229 docs_base_url = ("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/"
230 "master/doc/"+package+"/rst/")
231 cache = _get_cache(cache)
232 data, from_cache = _get_data(data_base_url, dataname, cache)
233 data = read_csv(data, index_col=0)
234 data = _maybe_reset_index(data)
236 title = _get_dataset_meta(dataname, package, cache)
237 doc, _ = _get_data(docs_base_url, dataname, cache, "rst")
239 return Dataset(data=data, __doc__=doc.read(), package=package, title=title,
240 from_cache=from_cache)
242# The below function were taken from sklearn
245def get_data_home(data_home=None):
246 """Return the path of the statsmodels data dir.
248 This folder is used by some large dataset loaders to avoid
249 downloading the data several times.
251 By default the data dir is set to a folder named 'statsmodels_data'
252 in the user home folder.
254 Alternatively, it can be set by the 'STATSMODELS_DATA' environment
255 variable or programatically by giving an explicit folder path. The
256 '~' symbol is expanded to the user home folder.
258 If the folder does not already exist, it is automatically created.
259 """
260 if data_home is None:
261 data_home = environ.get('STATSMODELS_DATA',
262 join('~', 'statsmodels_data'))
263 data_home = expanduser(data_home)
264 if not exists(data_home):
265 makedirs(data_home)
266 return data_home
269def clear_data_home(data_home=None):
270 """Delete all the content of the data home cache."""
271 data_home = get_data_home(data_home)
272 shutil.rmtree(data_home)
275def check_internet(url=None):
276 """Check if internet is available"""
277 url = "https://github.com" if url is None else url
278 try:
279 urlopen(url)
280 except URLError as err:
281 return False
282 return True
285def strip_column_names(df):
286 """
287 Remove leading and trailing single quotes
289 Parameters
290 ----------
291 df : DataFrame
292 DataFrame to process
294 Returns
295 -------
296 df : DataFrame
297 DataFrame with stripped column names
299 Notes
300 -----
301 In-place modification
302 """
303 columns = []
304 for c in df:
305 if c.startswith('\'') and c.endswith('\''):
306 c = c[1:-1]
307 elif c.startswith('\''):
308 c = c[1:]
309 elif c.endswith('\''):
310 c = c[:-1]
311 columns.append(c)
312 df.columns = columns
313 return df
316def load_csv(base_file, csv_name, sep=',', convert_float=False):
317 """Standard simple csv loader"""
318 filepath = dirname(abspath(base_file))
319 filename = join(filepath,csv_name)
320 engine = 'python' if sep != ',' else 'c'
321 float_precision = {}
322 if engine == 'c':
323 float_precision = {'float_precision': 'high'}
324 data = read_csv(filename, sep=sep, engine=engine, **float_precision)
325 if convert_float:
326 data = data.astype(float)
327 return data
330def as_numpy_dataset(ds, as_pandas=True, retain_index=False):
331 """Convert a pandas dataset to a NumPy dataset"""
332 if as_pandas:
333 return ds
334 ds.data = ds.data.to_records(index=retain_index)
335 for d in dir(ds):
336 if d.startswith('_'):
337 continue
338 attr = getattr(ds, d)
339 if isinstance(attr, (Series, DataFrame)):
340 setattr(ds, d, np.asarray(attr))
342 return ds