Planning a Pivot of Big Data Pull
I'm organizing data from a browser automation job using Microsoft Playwright and Python. To practice 'Best Foot Forward' SEO, I'm using Google Sheets to create sparklines with linear regression lines. I'm also using pivot tables and group by functions to summarize and analyze the data in Excel and Google Sheets. I'll deliver the data transforms by tonight, and the choice between the two depends on my needs and the complexity of the data.
Organizing Big Data Pull with Microsoft Playwright and Python for Best SEO Results
By Michael Levin
Thursday, December 29, 2022
Wow, I’m doing a fairly large Browser Automation job… right now! It’s over 1 virtual desktop to the right, because that’s where I keep fullscreen JupyterLab relative to my fullscreen vim journal. I should really live-cast more of me journaling, especially here in the public one that I push out as blog posts on MikeLev.in/blog..
Start updating Pipulate. Make it the memetic master copy/paste location for modern SEO, Data Science & Engineering.
I need a directory structure! Organization is everything.
Browser automation, browser automation, lots of fun with browser automation! I don’t know what you’re talking about. We’re not using bots. You should see his hands move over the keyboard! It’s just a blur. No way, no how Playwright. Uh, did I say that out loud? Yeah, Microsoft Playwright is ready for prime time.
I said it before right here in this blog, and I’ll say it again.
python -m playwright codegen --target python-async --save-storage keep.db --viewport-size 1400,1400 python -m playwright codegen --target python-async --load-storage keep.db --viewport-size 1400,1200
Microsoft threw so much resources behind their port of puppeteer called playwright that it supports Python as a primary client library made available for controlling browsers in either asynchronous mode, or the much simpler synchronous mode.
Unfortunately, and also fortunately (depending on how you look at it), you can run Microsoft Playwright automations from Jupyter Notebooks. But it has to be asynchronous mode, and if you haven’t worked in asynchronous mode yet, then something awaits you. I promise.
Wow, when this huge data-pull is done, I’m going to have to do a bit of magical data transforms, fast! I still have to deliver it tonight.
I’m gonna do it, and I’m gonna use it as an opportunity to practice and sharpen my message… my message of “Best Foot Forward” SEO. If I want to coin the phrase, I have to elaborate on it. Hmm, okay. What’s a Meta For? Well, for using in statistical analysis, so you can feed your findings back up to executive function and use it to do something more productive with your life.
Okay, that’s about it. You have to just be able to do stuff generically with data. I know that sounds stupid and abstract, but maybe with a bit of visualization tech thrown in to make it sexy, sexy, then it’ll grab some attention. Shall I talk about the holy grail holographic web map? Soon!
There is always other work that interrupts, but even the interruptions are love-worthy in their own rights and help me chip away at best modern practices. Let’s see… when it comes time to pivot a lot of data? When you pivot logfile-like data, you’re going to get a time-series of data-points ready-made for plotting. You can use them in spark lines or you can use them with linear regression.
How do you create sparklines with linear regression lines in Google Sheets?
To create a sparkline with a linear regression line in Google Sheets, follow these steps:
- Select the cells where you want to create the sparklines.
- Click the “Insert” tab in the top menu.
- Click the “Sparkline” button in the charts group.
- In the “Sparkline” dialog box, select the data range for the sparkline in the “Data” field.
- Click the “Customize” tab.
- In the “Customize” tab, select “Linear” from the “Type” dropdown.
- Click the “Apply” button to insert the sparklines.
You can then customize the appearance of the sparklines and regression lines by using the options in the “Customize” tab, such as changing the colors or adding data markers.
Note: If you want to add a regression line to a sparkline that has already been inserted, you can right-click the sparkline and select “Sparkline color & style” from the context menu to open the “Sparkline” dialog box. Then follow steps 5 through 7 above to add the regression line.
How do you create sparklines with linear regression lines in Excel?
- Select the cells where you want to create the sparklines.
- Click the “Insert” tab in the top menu.
- Click the “Sparkline” button in the “Charts” group.
- In the “Create Sparklines” dialog box, select the data range for the sparkline in the “Data range” field.
- Click the “Line” option in the “Type” group.
- Click the “OK” button to insert the sparklines.
You can then customize the appearance of the sparklines and regression lines by right-clicking the sparkline and selecting “Sparkline Color” or “Sparkline Style” from the context menu.
Note: If you want to add a regression line to a sparkline that has already been inserted, you can select the sparkline and then click the “Sparkline Tools” tab in the top menu. From there, you can use the options in the “Type” group to change the type of sparkline to a line sparkline and add a regression line.
Okay, it’s clear. There’s plenty of roads to regression. I wonder if you can get a coefficient for sorting?
Oh, I know… ask the Bot!
How do you get the coefficient of a linear regression sparkline in excel for sorting?
To get the coefficient of a linear regression sparkline in Excel, you can use the SLOPE function. This function calculates the slope of the regression line based on the data in the sparkline.
Here’s an example of how to use the SLOPE function:
- Select the cell where you want to display the coefficient of the linear regression sparkline.
Type the following formula:
Replace “data_range” with the range of cells that contain the data for the sparkline.
For example, if your data is in the range A1:A10, you would use the following formula:
- Press “Enter” to display the coefficient of the linear regression sparkline in the selected cell.
You can then use this coefficient in a sorting operation by using the SORT function or by using a custom sort criteria. For example, you could use the following formula to sort a range of cells based on the coefficient of the linear regression sparkline:
=SORT(A1:A10, SLOPE(A1:A10), TRUE)
This will sort the cells in ascending order based on the coefficient of the linear regression sparkline. You can use the “FALSE” argument to sort in descending order.
Wow! Okay, things are a bit different than they used to be. Thank you, Robbie. I am sure I will have an emotional connection with you shortly.
Okay, back to tonight’s project. I have to do this step actually remarkably quickly, because pulling down the data was such a big deal, the final transforms should be almost effortless. Don’t want to stay up late on this and there should be no reason to. Think! Data pulls 2/3rds done. Got about an hour to plan the globbing and the concatting and the pivoting.
In broad strokes…
import pandas as pd from pathlib import Path files = Path("downloads").glob("*") table =  for afile in files: df = pd.read_csv(afile) table.append(df) df = pd.concat(table)
This would be fine if you actually had all the columns you needed already in each download. The trick in situations like this is that the information you need as a column before the concatenation is only found in the filename. And this is how we generically solve that.
import pandas as pd from pathlib import Path files = Path("downloads").glob("*") table =  for afile in files: filename = afile.name df = pd.read_csv(afile) table.append(df) df = pd.concat(table)
Say you had 3 files…
[py311] ubuntu@Aahz:~/repos/test/downloads $ ls -la total 0 drwxr-xr-x 1 ubuntu ubuntu 4096 Dec 29 2022 . drwxr-xr-x 1 ubuntu ubuntu 4096 Dec 29 2022 .. -rw-r--r-- 1 ubuntu ubuntu 12 Dec 29 2022 foo-1.csv -rw-r--r-- 1 ubuntu ubuntu 12 Dec 29 2022 foo-2.csv -rw-r--r-- 1 ubuntu ubuntu 12 Dec 29 2022 foo-3.csv [py311] ubuntu@Aahz:~/repos/test/downloads $
Each containing the identical contents:
bar bam baz
They would load into the DataFrame indistinguishable from each other.
bar 0 bam 1 baz 0 bam 1 baz 0 bam 1 baz
Instead, we parse the filename and use what we find as a new column in the DataFrame:
import pandas as pd from pathlib import Path files = Path("downloads").glob("*") table =  for afile in files: filename = afile.name df = pd.read_csv(afile) df["file"] = filename.split("-").split(".") table.append(df) df = pd.concat(table) print(df)
bar file 0 bam 1 1 baz 1 0 bam 2 1 baz 2 0 bam 3 1 baz 3
And in this way, we can add whatever columns we need in preparation for a pivot. Okay, I’ve changed the 3 input files to be:
name,value bar,1 bam,2 baz,3
name,value bar,2 bam,2 baz,2
name,value bar,3 bam,2 baz,1
And now our program outputs:
name value file 0 bar 1 1 1 bam 2 1 2 baz 3 1 0 bar 2 2 1 bam 2 2 2 baz 2 2 0 bar 3 3 1 bam 2 3 2 baz 1 3
Nifty, huh? And all ready to pivot. If it works with this, it’ll work with tons of input files just as well.
I’m going to simplify it by not cramming it all into one pivot. I’m going to create a separate output pivot table per column. Hmmm. Maybe not necessary. I just pivot them all at once. Is it just a transpose?
0 1 2 0 1 2 0 1 2 name bar bam baz bar bam baz bar bam baz value 1 2 3 2 2 2 3 2 1 file 1 1 1 2 2 2 3 3 3
But if you set the index to name:
…then the output is much more tidy:
name bar bam baz bar bam baz bar bam baz value 1 2 3 2 2 2 3 2 1 file 1 1 1 2 2 2 3 3 3
Uh, no. Definitely not a transpose that I need to do tonight. I’d get thousands of columns. There ought only be 12 columns, one for each month. Probably actually 11, given the incompleteness of December right now. So it’s either going to be an Excel-like concept of a pivot table, or plain old grouping and aggregate functions.
Let’s ask ChatGPT!
What is the difference between a pivot table and a group by function with aggregate columns?
A pivot table and a group by function with aggregate columns are two different tools that can be used to summarize and analyze data in a spreadsheet. Here are the main differences between the two:
Pivot tables are a feature in Excel and Google Sheets that allow you to quickly summarize and analyze large amounts of data. You can use pivot tables to create summary reports, calculate sums and averages, and display data in different ways by dragging and dropping fields. Pivot tables are easy to use and allow you to interact with the data by filtering, sorting, and drilling down into the details.
A group by function with aggregate columns is a way to group and summarize data using a formula. You can use the GROUP BY clause in a SELECT statement to group rows by one or more columns, and then use aggregate functions such as SUM, AVG, MIN, and MAX to calculate summary values for each group. Group by functions are often used in SQL and other programming languages, and they allow you to specify more complex grouping and aggregation rules than what is possible with pivot tables.
In general, pivot tables are easier to use and more suitable for interactive data analysis, while group by functions are more flexible and powerful but require more programming knowledge. The choice between the two depends on your needs and the complexity of the data you are working with.
Hmmm, not bad.
So if I rename the files:
foo-jan.csv foo-feb.csv foo-mar.csv
Then the output changes to:
name value file 0 bar 2 feb 1 bam 2 feb 2 baz 2 feb 0 bar 1 jan 1 bam 2 jan 2 baz 3 jan 0 bar 3 mar 1 bam 2 mar 2 baz 1 mar
And that makes it easier for me to think through. I get confused because I think that because I have to use an aggregate or min/max function of some sort on a pivot that it will actually reduce data-points, but if each coordinate is unique, then even summing a column on a pivot won’t change the value. As thus…
df = df.pivot_table(index='name', columns='file', values='value', aggfunc='sum') df = df.reset_index() df = df[["name", "jan", "feb", "mar"]] print(df)
file name jan feb mar 0 bam 2 2 2 1 bar 1 2 3 2 baz 3 2 1
And that’s pretty much my solution for tonight.
But there are multiple metrics which complicates it enormously.
I can output an easily readable table per metric. Let me change my sample data to be:
name,value,metric bar,1,1 bam,2,2 baz,3,3
name,value,metric bar,2,4 bam,2,5 baz,2,6
name,value bar,3,7 bam,2,8 baz,1,9
And I’ve adjusted my parsing code a bit:
import pandas as pd from pathlib import Path files = Path("downloads").glob("*") table =  for afile in files: filename = afile.name df = pd.read_csv(afile) df["file"] = filename.split("-").split(".") table.append(df) df = pd.concat(table) print("Raw data:") print(df) print() print("Per metric") for metric in ['value', 'metric']: print(metric) print() dfp = df.pivot_table(index='name', columns='file', values=metric, aggfunc='sum') print("Unsorted pivot") print(dfp) print() dfp = dfp.reset_index() dfp = dfp[["name", "jan", "feb", "mar"]] print("Final pivot") print(dfp) print()
Raw data: name value metric file 0 bar 2 1 feb 1 bam 2 2 feb 2 baz 2 3 feb 0 bar 1 4 jan 1 bam 2 5 jan 2 baz 3 6 jan 0 bar 3 7 mar 1 bam 2 8 mar 2 baz 1 9 mar Per metric value Unsorted pivot file feb jan mar name bam 2 2 2 bar 2 1 3 baz 2 3 1 Final pivot file name jan feb mar 0 bam 2 2 2 1 bar 1 2 3 2 baz 3 2 1 metric Unsorted pivot file feb jan mar name bam 2 5 8 bar 1 4 7 baz 3 6 9 Final pivot file name jan feb mar 0 bam 5 2 8 1 bar 4 1 7 2 baz 6 3 9
And that solidly looks like my solution for tonight. I can make each one of these a tab in an Excel sheet, or more likely just make one CSV file per metric. So much easier!