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        "author": "Leonard Vorbeck",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "#  \u2728 DailyTrends 5.0 (final/stable) \u2728\n[![Downloads](https://static.pepy.tech/personalized-badge/dailytrends?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/dailytrends)\n\n\n\ud83d\udea8 ALL BUGS FIXED\ud83d\udea8 - Update to version 5.0\n- Fully operational after new versions of pandas broke the restructuring pipe.\n- Google API change, so the code was adjusted (again).\n- The timerange can now be specified approximately.\n- The region (geo) can now be specified.\n\n###  Purpose\n\nThis lightweight API solves the problem of getting only monthly-based data for large time series when collecting Google Trends data. No login required. \n\n### Installation\n\nInstall via PyPi:\n\n```bash\n$ pip install DailyTrends\n```\n\n\n### Usage\n\n```python3\n>>> from DailyTrends.collect import collect_data\n```\nQueries are submitted in a gentle manner, which can be slow (but safe) for very large series.\n\n```python3\n>>> data = collect_data(\"AMD stock\",start=\"2004-01-01\", end=\"2019-07-06\",\n                    geo=\"\", save=False, verbose=False)  \n```                 \n           \n```python3\n>>> data.info()\n\n<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 5666 entries, 2004-01-01 to 2019-07-06\nFreq: D\nData columns (total 1 columns):\nAMD stock: (Worldwide)    5666 non-null float64\ndtypes: float64(1)\nmemory usage: 88.5 KB\n```\n- The returned dataframe is already indexed and ready for storage/analysis.\n- The end of the series defaults to \"TODAY\".\n- The start of the series defaults to \"2004-01-01\".\n- The geo parameter defaults to \"\", which yields global results.\n\n```python\n# Plotting some rolling means of the daily data\n>>> ax = data.rolling(10).mean().plot();\n>>> data.rolling(25).mean().plot(ax=ax);\n>>> data.rolling(50).mean().plot(ax=ax)\n```\n\n![image.png](1.png)\n\n### Add your own data\n```python3\n# In this case the historic prices of the stock\n>>> import pandas as pd\n>>> price_data = pd.read_csv(\"price_data.csv\")\n>>> merged = pd.merge(price_data, data,\n                  left_index=True, right_index=True)\n>>> merged[[\"AMD stock: (Worldwide)\", \"Open\"]].rolling(30).mean().plot()\n```\n![image.png](2.png)\n\n### Load multiple queries\n\n```python\n>>> data = collect_data([\"Intel\", \"AMD\"],start=\"2004-01-01\", end=\"TODAY\",\n                    geo=\"DE\", save=False, verbose=False)      \n                \n```\n\n\n\n\n### To-Do\n\n- Add Tor-Network-based requests\n\n\n\n\n\n\n\n## **Disclaimer**\n\nThis API is *not* supported by Google and is for experimental purposes only.\n\n\n\n\n",
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