o}gddlmZmZddlmZddlmZddlmZddl Z ddl m Z ddl mZded ed efd Zd ed ed efdZded e jfdZ ddedededededededed e jfdZdS))date timedelta)partial)sleep) monthrangeN) ResponseError)TrendReqyearmonthreturncLt||t||dS)zGiven a year and a month returns an instance of the date class containing the last day of the corresponding month. Source: https://stackoverflow.com/questions/42950/get-last-day-of-the-month-in-python )rr)r r s R/var/www/py-google-trends/myenv/lib/python3.11/site-packages/pytrends/dailydata.pyget_last_date_of_monthr s% eZe44Q7 8 88startstopc\|dd|dS)zGiven two dates, returns a stringified version of the interval between the two dates which is used to retrieve data for a specific time frame from Google Trends. z%Y-%m-%d )strftime)rrs rconvert_dates_to_timeframers0 nnZ(( F F4==+D+D F FFr timeframecBd\}}|s ||d}ns#t$rf}t|tddd|zzdtdd|zz|dz }|d krtd Yd }~nYd }~nd }~wwxYw||S) z>Attempts to fecth data and retries in case of a ResponseError.)rF)rTzTrying again in <z seconds.rz'Failed after 3 attemps, abort fetching.N)rprintrinterest_over_time)pytrends build_payloadrattemptsfetchederrs r _fetch_datar$s Hg   MI . . . .GG    #JJJ AR!h,%6AAA B B B "q8|# $ $ $ MH!||?@@@||||     & & ( ((s  BABBUST@word start_year start_mon stop_yearstop_mongeoverbose wait_timect||d}t||} tdd} t| j|gd|d} t | | t || } i} |}|| kr|t|j|j}t ||}|rt|d|t | | || |<|td z}t||| k|tj | d g }|| d d }||d d||d dz |d<||d |jz||<|S)a Given a word, fetches daily search volume data from Google Trends and returns results in a pandas DataFrame. Details: Due to the way Google Trends scales and returns data, special care needs to be taken to make the daily data comparable over different months. To do that, we download daily data on a month by month basis, and also monthly data. The monthly data is downloaded in one go, so that the monthly values are comparable amongst themselves and can be used to scale the daily data. The daily data is scaled by multiplying the daily value by the monthly search volume divided by 100. For a more detailed explanation see http://bit.ly/trendsscaling Args: word (str): Word to fetch daily data for. start_year (int): the start year start_mon (int): start 1st day of the month stop_year (int): the end year stop_mon (int): end at the last day of the month geo (str): geolocation verbose (bool): If True, then prints the word and current time frame we are fecthing the data for. Returns: complete (pd.DataFrame): Contains 4 columns. The column named after the word argument contains the daily search volume already scaled and comparable through time. The column f'{word}_unscaled' is the original daily data fetched month by month, and it is not comparable across different months (but is comparable within a month). The column f'{word}_monthly' contains the original monthly data fetched at once. The values in this column have been backfilled so that there are no NaN present. The column 'scale' contains the scale used to obtain the scaled daily data. rzen-USih)hltzr)kw_listcatr,gprop:)days isPartial)columns _unscaled_monthly)lsuffixrsuffixT)inplacedscale)rrr rr r$rr r rrrpdconcatvaluesdropjoinffillr@)r'r(r)r*r+r,r-r. start_date stop_daterr monthlyresultscurrentlast_date_of_monthrdailycompletes rget_daily_datarO0sZj)Q//J&y(;;I7s+++HH2%)Fs"FFFM(M3J JJLLGGG I  3GL'-PP.w8JKK  ) T''I'' ( ( (&x JJ$ya'8'8'88 i I   Ignn&& ' ' , ,k] , C CEzz'; zKKH    %%d%333 D!2!2!23c9HW0001HNBHTN Or)r%Tr&)datetimerr functoolsrtimercalendarrpandasrApytrends.exceptionsrpytrends.requestr intrstrr DataFramer$boolfloatrOrrr]s$$$$$$$$------%%%%%%99S9T9999GdG$G3GGGG)C)BL))))0!!%$'OOO OO O O  O  O"O-/LOOOOOOr