Metadata-Version: 2.1
Name: DailyTrends
Version: 4.0
Summary: A package to receive full-range daily Google Trends data
Home-page: https://github.com/le0x99/DailyTrends/
Author: Leonard Vorbeck
Author-email: leomxyy@googlemail.com
License: UNKNOWN
Description: #  ✨ DailyTrends [FRESH VERSION (3.1)] ✨
        [![Downloads](https://pepy.tech/badge/dailytrends/week)](https://pepy.tech/project/dailytrends/week)
        [![Downloads](https://pepy.tech/badge/dailytrends/month)](https://pepy.tech/project/dailytrends/month)
        
        
        #### [!] All bugs fixed. 
        
        - The timerange can now be specified approximately.
        - The region (geo) can now be specified.
        
        
        ###  Purpose
        
        This lightweight API solves the problem of getting only monthly-based data for large time series when collecting Google Trends data. No login required. For unlimited requests, I will implement a Tor-based solution soon.
        
        ### Installation
        
        ```bash
        $ pip install DailyTrends
        ```
        
        
        ### Usage
        
        ```python3
        >>> from DailyTrends.collect import collect_data
        
        # Get the data directly into python.
        # The returned dataframe is already indexed and ready for storage/analysis.
        # the end of the series defaults to "TODAY".
        # the start of the series defaults to "2004-01-01".
        # The geo parameter defaults to "", which yields global results.
        
        >>> data = collect_data("AMD stock",start="2004-01-01", end="2019-07-06",
                            geo="", save=False, verbose=False)    
        
        >>> data.info()
        ```
        
        ```python3
        <class 'pandas.core.frame.DataFrame'>
        DatetimeIndex: 5666 entries, 2004-01-01 to 2019-07-06
        Freq: D
        Data columns (total 1 columns):
        AMD stock: (Worldwide)    5666 non-null float64
        dtypes: float64(1)
        memory usage: 88.5 KB
        ```
        
        ```python
        # Plotting some rolling means of the daily data
        >>> ax = data.rolling(10).mean().plot();
        >>> data.rolling(25).mean().plot(ax=ax);
        >>> data.rolling(50).mean().plot(ax=ax)
        ```
        
        ![image.png](1.png)
        
        ### Add your own data
        ```python3
        # In this case the historic prices of the stock
        >>> import pandas as pd
        >>> price_data = pd.read_csv("price_data.csv")
        >>> merged = pd.merge(price_data, data,
                          left_index=True, right_index=True)
        >>> merged[["AMD stock: (Worldwide)", "Open"]].rolling(30).mean().plot()
        ```
        ![image.png](2.png)
        
        ### Load multiple queries
        
        ```python
        >>> data = collect_data(["Intel", "AMD"],start="2004-01-01", end="TODAY",
                            geo="DE", save=False, verbose=False)      
                        
        ```
        
        
        
        
        ### To-Do
        
        - Add Tor-Network-based requests
        
        
        
        
        
        
        
        ## **Disclaimer**
        
        This API is *not* supported by Google and is for experimental purposes only.
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.5
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
