I have used the above example, but that just pulls DAILY data into a dataframe when I would like to pull weekly. It doesn't seem like get_data_yahoo has a parameter where you can select perhaps from daily, weekly or monthly like the options made available on yahoo itself. Any other packages or ideas that you know of that might be able to facilitate this?
Using resample (as @tshauck has shown) is another possibility. Use asfreq if you want to guarantee that the values in your downsample are values found in the original data set. Use resample if you wish to aggregate groups of rows from the original data set (for example, by taking a mean). reindex might introduce NaN values if the original data set does not have a value on the date specified by the reindex -- though (as @behzad.nouri points out) you could use method=pad to propagate last observations here as well.
Yf.download Weekly Data
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If you check the latest pandas source code on github, you will see that interval param is included in the latest master branch. You can manually modify your local copy by overwriting the same data.py under your Site-Packages/pandas/io folder
As a suggestion, take Wednesdays because that tend to have least missing values. ( i.e. fewer NYSE holidays falls on Wednesday ). I think Yahoo weekly data gives the stock price each Monday, which is worst weekly frequency based on S&P data from 2000 onwards:
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Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.
Apple introduced ResearchKit in March 2015 as an open source software framework that enables a new mode of clinical research by leveraging the capabilities of the iPhone for remote, continuous, bi-directional transmission of data with individual research participants. The Asthma Health App (AHA) was one of the first of five ResearchKit apps that launched in March 2015 and was developed by the Icahn School of Medicine at Mount Sinai, in collaboration with Apple, Sage Bionetworks, and other partners2.
Asthma is one of the most common and costly chronic diseases, impacting 25 million Americans. In order to achieve optimal control of persistent asthma, patients should avoid exacerbating factors, monitor their symptoms, adhere to their prescribed treatment regimens, and adjust treatments during periods of worsening symptoms3. The AHA was designed to conduct large scale mobile health research by collecting continuous prospective, longitudinal data, while promoting self-education and monitoring, as well as providing real time feedback to the participants on their asthma status and protocol adherence1. Specifically, the AHA encouraged users to maintain an electronic asthma diary that tracked asthma symptoms and potential triggers. The AHA also provided automated medication reminders, and offered various optional educational videos on asthma as well as geographically specific weather and pollution information (using the iPhone GPS to determine coordinates of the user), allowing for correlation between factors identified in the environment and asthma symptoms. Moreover, based on user input, the app provided feedback regarding their asthma control using standard criteria (e.g. Global Initiative for Asthma score, etc.)4. The full working code for the Asthma Health app is open source and serves as a template for others in designing mobile health apps using ResearchKit.
The app recorded all data collected for this study through interactions with Bridge Server, a set of web services developed and operated by Sage Bionetworks. Supplementary Fig. 7b in Chan et al.2 provides a detailed description of the backend design on health data encryption.
Coded study data, consisting of survey responses and GPS coordinates, were exported to Synapse for distribution to researchers. Synapse, a general-purpose data and analysis sharing service, enables researchers to work collaboratively to analyze data and share insights. Synapse was developed and operated by Sage Bionetworks as a service to the biomedical research community5.
Because of an initial technical issue with the integration of HealthKit and ResearchKit data, demographic information is missing from a number of participants. Multiple versions of the AHA were released during the study period to address these software-related concerns and to implement new features (see Supplementary Table 6 in Chan et al.2).
One novel aspect of this study dataset is its link between Asthma symptoms and geography. For the subset of participants who consented to share their geolocation data with qualified researchers, we report their time course geographic data of 3 digit zip codes. Note, during the electronic informed consent process, participants agreed to share data with the understanding that their personally identifiable information (PII)/ protected health information (PHI) removed. Thus, more refined geographic information, such as full zip code or longitudes/latitudes are not released.
The symptom data generated in this smartphone study are based on self-reported surveys. Lee et al (2003) noted that different methods of defining asthma severity provide different distribution of patients across categories of disease (mild to severe). However, our study methodology did not differ from common practice in most asthma epidemiologic studies, where symptom-based surveys were used without corresponding biometric measurements (i.e. lung function)2.
Given the aforementioned limitations and learnings from the AMHS, we believe research hypotheses with the following characteristics are appropriate for the current ResearchKit methodology: minimal risk clinical studies, allowing the use of electronic consent, requirement for rapid enrollment across diverse geographical locations and frequent data collection, a hypothesis that can be answered within a short time period, data collection that is passive (GPS, physical activity, etc.), no assumption that results will be generalizable to participants recruited via traditional methods, and a sample size and statistical analysis plan that account for the known attrition/missing data historically seen in internet/mobile app studies2.
All coded data sets are stored and accessible via the Synapse platform in a public project with associated metadata and documentation ( ). Please see Table 1 for a summary on survey contents and releasing frequencies, as well as numbers of surveys and activities completed by study participants who agreed to share their data broadly [Data Citations 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11].
(a) 7 day rolling mean of daily survey question response counts across participants. (b) 4 week rolling mean of weekly survey question response counts across participants. The survey question labels used in the figure legends correspond to the column names of survey data matrices, and are annotated in Supplementary Table 1. Substantially overlapping curves were plotted with a single line and grouped in the legend. Y-axis is plotted on a log10 scale with y-axis ticks displaying back-transformed counts.
For the 1959 participants who supplied location data (after May 5, 2015), we illustrate their geographic distribution [Data Citation 11] by state in Fig. 5. The three states with the largest number of participants are California, New York and Texas.
In consultation with data governance experts, outlier values for height (78 inches) and weight (350 pounds) were censored to protect individuals who may have uniquely identifiable traits. In addition, for one-time surveys, occasionally multiple versions were completed due to re-installation of the app. For these cases, the first survey that was filled out was reported.
In addition, in our app, we purposely eliminated default selections in all survey questions to avoid any biases caused by this mechanism. Other features that help to reduce fraudulent response include allowing users to skip any survey questions, and only showing one question per page (straightlining is common when answers are presented in matrix form).
Due to the novel nature and collection method for these data, governance structures have been put in place in order to respect the balance between the desire of participants to share their data with qualified researchers and the respect for privacy of those participants. Researchers who are interested in accessing these data need to complete the following steps:
The study is funded by the Icahn School of Medicine at Mount Sinai, and with technology support from LifeMap Solutions. We would like to acknowledge the contributions of all our academic and technology partners who helped with the project, with special thanks to our Nature Biotechnology coauthors: Rogers L., Genes N., Krock E., Badgeley M., Edgar R., Violante S., Wright R., Powell C.A., and Dudley J.T. 152ee80cbc
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