NAICS | RESTful API in Python using Flask | Machine Learning library

Below is the sample data and the desired outcome. This is a much simplified version of the actual data set. In the actual data set, there are 20 years and 4 quarters apiece. Looking to have each unique company entry listed once and the employment data series running from beginning to end from left to right. In the event that there is no data for Vision Inc in 2019 quarter 3, then I would want it to return a O and not an NA.

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I am having difficulty executing correctly a vespa query. i want to query 2 different index fields with or between them, i want to to the equivalent of elastic match query.

i got a lot of soft timeouts so i increased timeout to get the true result and check how much time it took.

this is the query i sent:

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Here is the sample code that I am using

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I know that there are similar questions asked. I’m not able to find one that I can leverage. I keep getting the following error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Not sure what is wrong with code, as I’m still learning Python.

File H:\ code library\Python\T900_Dashboard.py Line 57, in SQLCaseWhen1 = [(T900_DashboardFile[‘NAICS CD (Parent)’] in Power_Sector),\

File “C:\Users\DC\Anaconda3\lib\site-packages\pandas\core\generic.py”, line 1478, in nonzero raise ValueError(

My code looks like:

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I have data data set below... as you can see some months are missing (my data frame should be quarterly). I need to add the missing latest two quarters 2020-04-01 and 2020-07-01.. but the columns GEO, NAICS, shoule repeat the existing variables. ONLY NA should be filled in the VALUE column. Is there a way of doing this? I am using the following code, but it is not working...

REF_DATE GEO NAICS VALUE 2020-01-01 AB fishin 33 2020-01-01 AB mining 233 2020-01-01 AB constr 53 2020-01-01 BC fishin 353 2020-01-01 BC mining 253 2020-01-01 BC constr 953 2020-10-01 AB fishin 33 2020-10-01 AB mining 293 2020-10-01 AB constn 343 2020-10-01 BC fishin 633 2020-10-01 BC mining 363 2020-10-01 BC constr 523

I should have these data inserted

REF_DATE GEO NAICS VALUE 2020-04-01 AB fishin NA 2020-04-01 AB mining NA 2020-04-01 AB constr NA 2020-04-01 BC fishin NA 2020-04-01 BC mining NA 2020-04-01 BC constr NA ...

I am running into some problems again and hope that someone can help me. I am doing research on the effect of ELI on ROS for firms and if the pandemic has an effect on this. For this research, my supervisor for my thesis has asked me to do a regression analysis per year grouped by industries (NAICS) and I am at a loss as to how to do this. I have firms in 46 different industries (NAICS) and 11 years of firm data per firm (2010-2020). Now I would like to run a regression ROS ~ ELI + ELI*Pandemic, for all industries for each year and then capture the resulting N (number of firms per industry) and R-squared in one file. The image below is an example of what I am trying to achieve:

I hope that someone can help me because I am at an absolute loss and I can't seem to find a similar question/answer on SO.

Here is the dput(head()) as an example. NAICS is the industry.

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I am doing research on the pandemic on Net Sales figures of companies in different industries. For this I have a dataset containing Net Sales figures of companies of the different industries. Now I would like to create plots per industry on one graph with 1 line corresponding to the aggregated Net Sales per year (from 2010-2020), and the other being a trend line from 2010-2019 onto 2020 (so the expected Net Sales for 2020 taking the previous years into account). This way I have a visual aid to see whether 2020 has seen significantly worse numbers. I have gotten the first graphs (aggregated Net Sales per year per industry) sorted using dplyr with:

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I am doing research on the effect of lean inventory management on the financial performance of firms and to do this I need to create a new variable.

This variable is calculated by 2 steps:

1. By regressing the natural logarithm of sales on the natural logarithm of inventory for each of the i industries (NAICS) and t years. The formula is as follows:

1. The variable for each firm (f) is obtained by studentising the residual (u) and multiplying it by -1.

So mathematically I know how I should go on to do this, but my dataset has more than 3000 observations and that is going to take ages to do by hand.

My dataset is as follows (from the dput(head())). This dataset only shows the same NAICS (315) but there are a lot more, 46 in total.

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I am trying to access an element in Snowflake. The input looks like as follows;

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