{"id":18614028,"url":"https://github.com/enthought/quandl-python","last_synced_at":"2025-09-11T17:48:24.757Z","repository":{"id":8988834,"uuid":"10736871","full_name":"enthought/quandl-python","owner":"enthought","description":"Default Repo description from terraform module","archived":false,"fork":false,"pushed_at":"2013-06-18T09:35:36.000Z","size":153,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":23,"default_branch":"master","last_synced_at":"2025-08-22T00:42:12.206Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/enthought.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2013-06-17T12:31:03.000Z","updated_at":"2021-06-01T19:54:25.000Z","dependencies_parsed_at":"2022-09-11T02:50:36.539Z","dependency_job_id":null,"html_url":"https://github.com/enthought/quandl-python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/enthought/quandl-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2Fquandl-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2Fquandl-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2Fquandl-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2Fquandl-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/enthought","download_url":"https://codeload.github.com/enthought/quandl-python/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2Fquandl-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274681475,"owners_count":25330237,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-11T02:00:13.660Z","response_time":74,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-07T03:24:49.639Z","updated_at":"2025-09-11T17:48:24.702Z","avatar_url":"https://github.com/enthought.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Quandl API for Python\n=====================\nBasic wrapper to return datasets from the Quandl website as Pandas dataframe objects with a timeseries index, or as a numpy array. This allows interactive manipulation of the results via IPython or storage of the datasets using Pandas I/O functions. You will need a familarity with [pandas](http://pandas.pydata.org/) to get the most out of this.\n\nSee the [Quandl API](http://www.quandl.com/api) for more information.\n\n\nUsage\n=====\nUsage is simple and mirrors the functionality found at [Quandl/API](http://www.quandl.com/api).\n\nA request with a full list of options would be the following.\n\n```python\ndata = Quandl.get('PRAGUESE/PX', authtoken='xxxxxx', trim_start='2001-01-01',\n                  trim_end='2010-01-01', collapse='annual',\n                  transformation='rdiff', rows=4, returns='numpy')\n```\n\nAll options beyond specifying the dataset (PRAUGESE/PX) are optional,though it is helpful to specify an authtoken at \nleast once to increase download limits.\n\nYou can then view the dataframe with `data.head()`.\n\nSee the [pandas documentation](http://pandas.pydata.org/) for a wealth of options on data manipulation.\n\nAuthtokens are saved as pickled files in the local directory so it is unnecessary to enter them more than once,\nunless you change your working directory. To replace simply save the new token or delete the `authtoken.p` file.\n\n\n## Search Example\nUsing the search function you can search Quandl, specifying what sources to search, and what page you wish to show.\n\n\tQuandl.search(query = \"Search terms\", source = \"Source you wish to search\", page = 1)\n\nAn example of searching for datasets having to do with oil: \n\n\tpython\n\timport Quandl\n\tdatasets = Quandl.search('OIL')\n\tdatasets[0]\n\n\nwill output:\n\n\t{u'code': u'NSE/OIL',\n\t u'colname': [u'Date',\n  \t u'Open',\n  \t u'High',\n  \tu'Low',\n  \tu'Last',\n  \tu'Close',\n  \tu'Total Trade Quantity',\n  \tu'Turnover (Lacs)'],\n \tu'desc': u'Historical prices for Oil India Limited (OIL), (ISIN: INE274J01014),  \t National Stock Exchange of India.',\n \tu'freq': u'daily',\n \tu'name': u'Oil India Limited'}\n\n\n## Get Example\nAn example of creating a pandas time series for IBM stock data, with a weekly frequency:\n\n```python\ndata = Quandl.get('GOOG/NYSE_IBM', collapse='weekly')\ndata.head()\n```\n\nwill output\n\n```\nNo authentication tokens found,usage will be limited\nReturning Dataframe for  GOOG/NYSE_IBM\n\n              Open    High     Low   Close   Volume\nDate\n2013-03-28  209.83  213.44  209.74  213.30  3752999\n2013-03-15  215.38  215.90  213.41  214.92  7937244\n2013-03-08  209.85  210.74  209.43  210.38  3700986\n2013-03-01  200.65  202.94  199.36  202.91  3309434\n2013-02-22  199.23  201.09  198.84  201.09  3107976\n```\n\n\n## Push Example\nYou can now upload your own data to Quandl through the Python package.\n\nAt this time the only accepted format is a date indexed Pandas DataSeries.\n\nThings to do before you upload:\n\n* Make an account and set your authentication token within the package with the Quandl.auth() function.\n* Get your data into a data frame with the dates in the first column.\n* Pick a code for your dataset - only capital letters, numbers and underscores are acceptable.\n\nThen call the following to push the data:\n\n```python\nQuandl.push(data, code='TEST', name='Test', desc='test')\n```\n\nAll parameters but desc are necessary\n\nIf you wish to override the existing set at code `TEST` add `override=True`.\n\nUploading a pandas DataSeries with random data:\n\n```python\nimport pandas\nimport numpy\n\nindex = ['Dec 12 2296', 'Dec 21 1998', 'Oct 9 2000', 'Oct 19 2001',\n         'Oct 30 2003', 'Nov 12 2003']\ndata = pandas.DataFrame(numpy.random.randn(6, 3), index=index,\n                        columns=['D', 'B', 'C'])\nprint Quandl.push(data, code='F32C', name='Test', desc='test',\n                  authtoken='xxxxxx')\n```\n\nWill print the link to your newly uploaded data.\n\n\nRecommended Usage\n================\nThe IPython notebook is an excellent python environment for interactive data work. Spyder is also a superb IDE for analysis and more numerical work.\n\nI would suggest downloading the data in raw format in the highest frequency possible and preforming any data manipulation\nin pandas itself.\n\nSee [this link](http://pandas.pydata.org/pandas-docs/dev/timeseries.html) for more information about timeseries in pandas.\n\n\nQuestions/Comments\n==================\nPlease send any questions, comments, or any other inquires about this package to \u003cChris@quandl.com\u003e.\n\n\nInstallation\n============\nThe stable version of Quandl can be installed with pip:\n\n    pip install Quandl\n\n\nDependencies\n============\nPandas :: \u003chttps://code.google.com/p/pandas/\u003e\n\ndateutil (should be installed as part of pandas) :: \u003chttp://labix.org/python-dateutil\u003e\n\n\nLicense\n=======\n[MIT License](http://opensource.org/licenses/MIT)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenthought%2Fquandl-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fenthought%2Fquandl-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenthought%2Fquandl-python/lists"}