pytrends(谷歌趋势)每日频率

本文介绍了pytrends(谷歌趋势)每日频率的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在使用 pytrends python 包来拉取搜索词的流行度.看起来默认频率是每周，但我需要每日数据.有没有可以调整的参数?我似乎找不到任何东西.这里有一些代码可以让你到达同一个地方......

导入pytrends导入 matplotlib.pyplot 作为 plt%matplotlib 内联从 pytrends.request 导入 TrendReqpytrends = TrendReq(hl='en-US', tz=360)pytrends.build_payload(["sp500", "dogs"], cat=0, timeframe='today 5-y', geo='', gprop='')df = pytrends.interest_over_time()df.tail()

如您所见，返回的数据帧每周采样一次.我如何才能获得 5 年前但每天的相同数据?

解决方案

我正在访问这个问题，因为我正在搜索这个主题.截至今天(2019 年)，pytrends 库有一个方法可以返回关键字的每日数据.

我建议查看他们的 github 存储库.>

我应该指出，我没有测试我们可以在多大程度上获得数据，但我确信文档中的某个地方有这个信息.

示例:

from pytrends.request import TrendReq从 pytrends 导入每日数据df = dailydata.get_daily_data('cinema', 2019, 1, 2019, 10, geo = 'BR')打印(df)

输出

Cinema_unscaled Cinema_monthly isPartial scale Cinema日期2019-01-01 60 NaN NaN NaN NaN2019-01-02 76 NaN NaN NaN NaN2019-01-03 82 NaN NaN NaN NaN2019-01-04 71 NaN NaN NaN NaN2019-01-05 100 NaN NaN NaN NaN………………2019-10-26 74 38.0 NaN 0.38 28.122019-10-27 74 33.0 真 0.33 24.422019-10-28 53 33.0 NaN 0.33 17.492019-10-29 37 33.0 NaN 0.33 12.212019-10-30 34 33.0 NaN 0.33 11.22

I am using the pytrends python package to pull search term popularity. It looks like the default frequency is weekly but I need daily data. Is there a parameter to adjust for that? I can't seem to find anything. Here is some code to get you to the same place...

import pytrends import matplotlib.pyplot as plt %matplotlib inline from pytrends.request import TrendReq pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload(["sp500", "dogs"], cat=0, timeframe='today 5-y', geo='', gprop='') df = pytrends.interest_over_time() df.tail()

as you can see, the dataframe returned is sampled weekly. How can I get the same data going back 5 yrs, but daily?

解决方案

I'm visiting this question because i was searching this topic. As of today (2019), the pytrends library has a method that returns daily data for a keyword.

I recommend checking their github repository.

I should point out that i didn't test it how far we can get data, but i'm sure that somewhere in the documentation has this information.

Example:

from pytrends.request import TrendReq from pytrends import dailydata df = dailydata.get_daily_data('cinema', 2019, 1, 2019, 10, geo = 'BR') print(df)

Output

cinema_unscaled cinema_monthly isPartial scale cinema date 2019-01-01 60 NaN NaN NaN NaN 2019-01-02 76 NaN NaN NaN NaN 2019-01-03 82 NaN NaN NaN NaN 2019-01-04 71 NaN NaN NaN NaN 2019-01-05 100 NaN NaN NaN NaN ... ... ... ... ... 2019-10-26 74 38.0 NaN 0.38 28.12 2019-10-27 74 33.0 True 0.33 24.42 2019-10-28 53 33.0 NaN 0.33 17.49 2019-10-29 37 33.0 NaN 0.33 12.21 2019-10-30 34 33.0 NaN 0.33 11.22