From the course: Data Science on Google Cloud Platform: Exploratory Data Analytics
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Time series analysis with Cloud Storage - Google Cloud Tutorial
From the course: Data Science on Google Cloud Platform: Exploratory Data Analytics
Time series analysis with Cloud Storage
- [Instructor] Let's take a look at how we can use data from cloud storage objects to create a TimeSeries chart with a charting API. First, we need to read data from cloud storage into a Python variable. We have already loaded the campaign_data.csv file that comes with the exercise into cloud storage under the exercise-lil bucket under the data data tree. We read that object into a variable called campaign_data. Even though the source data is a CSV file, it is read as one large string into the campaign_data variable. We need to convert this into a Pandas DataFrame before it can be used for charting. We read the string data into a Pandas DataFrame using the CSV reader. Then we perform aggregations. We group the data by OFFER_DATE and find the sum of conversations by OFFER_DATE. We then print the summary data to verify its accuracy. We also need to convert OFFER_DATE into a date data type since the charting API requires date variable for drawing the TimeSeries chart. Finally, we call…
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