Is there a way to create multiple plots, based on values of a field (facet plots) in aws quicksight?
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I want to have multiple values in "plotly" dash, Previously I was using Tableau for this purpose. In tableau this was possible due to text boxes in tableau. In pandas or plotly, i think this is not possible.
I am setting up a BigQuery transfer service to transfer a CSV stored in a GCS bucket into BigQuery.
However, I don't need all the columns in the CSV file. Is there a way of limiting the columns I transfer without having to manually remove the columns before the transfer?
Or, if I limited the columns in my BQ table to the ones I need, will BQ just ignore the other columns in the CSV file?
I have read the relevant page in the documentation but there is no mention of limiting columns.
You can accomplish what you want if you manually specify the target table schema with the columns that you need. Then when you use the transfer service you need to set the option ignore_unknown_values to true.
Let's say I have a CSV on Google Cloud Storage with the following data:
"First"|"Second"|"Ignored"
"Third"|"Fourth"|"Ignored"
Then I have the table with the name test and schema like:
first_col STRING NULLABLE
second_col STRING NULLABLE
After configuring the transfer service with web UI and checking the checkbox "Ignore unknown values" I get the following data in the table:
first_col
second_col
First
Second
Third
Fourth
Read more about it in this section.
I would like to create dataframes using excel spreadsheets as source data. I need to transform the data series from the format used to store the data in the excel spreadsheets to the dataframe variable end product.
I would like to know if users have experience in using various python methods to accomplish the following:
-data series transform: I have a series that includes one data value per month, but would like to expand the table of values to include one value per day using an index (or perhaps column with date values). So if table1 has a month based index and table2 has a daily index how can I convert table1 values to the table2 based index.
-dataframe sculpting: the data I am working with is not similar in length, some data sets are longer than others. By what methods is it possible to find the shortest series length in a column in the context of a multicolumn dataframe?
Essentially, I would like to take individual tables from workbooks and combine them into a single dataframe that uses a single index value as the basis for their presentation. My workbook tables may have data point frequencies of daily, weekly, or monthly and I would like to build a dataframe that uses the daily index as a basis for the table elements while including an element for each day in series that are weekly and monthly.
I am looking at the Pandas library, but perhaps there are other libraries that I have overlooked with additional functionality.
Thanks for helping!
For your first question, try something like:
df1 = df1.resample('1d').first()
df2.merge(df1)
That will upsample your monthly or weekly dataframes and merge it with your daily dataframe. Take a look at the interpolate method to fill in missing values. To get the name of the shortest column try this out:
df.count().idxmin()
Hope that helps!
I have a doubleclick csv file with 20 columns (Timestamp,AdvertiserId,ActionName,Brower,OSID ...) without any header. I would like to ingest only first 3 columns into a BQ table. Is there any way to achieve that without mapping each and every column one-by-one manually into BQ's UI (create new_table ->"Schema" section)?
Fields in CSV is comma separated and newlines are defined as semi-colon';'.
There are two possible ways to do that: BigQuery: Load from CSV, skip columns
In your case I would probably suggest the second approach. Set the ignoreUnknownValues flag and pass in a schema with just the first three columns. For example:
bq load --ignore_unknown_values dataset.new_table gs://path/to/file.csv ~/path/to/schema.json
I would like to create a report / line chart in Splunk which shows mulitple series of data points for weekly loads. Below is a simple example in a spreadsheet, but I am having difficulty finding if this is even possible in Splunk and if so, how it could be implemented. I am using
"| dbquery mydatabaseconn "Select load_date, source, sum(transactions) from mytable group by load_date, source "
as my Search.
This should be possible in Splunk. From the documentation chapter Data structure requirements for visualizations:
Column, line, and area charts are two-dimensional charts supporting
one or more series. They plot data on a Cartesian coordinate system,
working from tables that have at least two columns. In tables for
column, line, and area charts, the first column contains x-axis values
and subsequent columns contain y-axis values (each column represents a
series).
So your data will need to look something like:
2015-10-01, 25, 17
2015-10-01, 50, 45
etc.
where column 2 represents "Source 1" and column 3 represents "Source 2".