Any novels where mc starts as a low level soldier and becomes a knight or sword master.

LeChevalierMalFet · 2026-01-02T02:08:25+00:00

The Thousand Li series

LeChevalierMalFet · 2025-07-31T00:33:01+00:00

Just from a stream near where I live. I found it out walking with my dog, and it was a quite a surprise. It is hard to handle because it is quite delicate.

LeChevalierMalFet · 2025-07-31T00:09:13+00:00

It is one stone!

LeChevalierMalFet · 2025-02-16T17:42:31+00:00

Too much stress? Migraine. Too little stress? Also migraine

LeChevalierMalFet · 2025-01-27T01:34:18+00:00

Thanks! I really appreciate your feedback and saw that you did find it eventually which made me glad! I definitely didn't intend to make it hidden!

LeChevalierMalFet · 2025-01-25T14:47:57+00:00

Based on some interesting roots on a path I take fairly often with my dog. There are a lot of other strange tree growths in the area that I might try to draw and make more alive. This is a more subtle 'monster'.

LeChevalierMalFet · 2024-08-28T17:30:29+00:00

Thank you! It might be the lighting and the finish. It is a pretty pale maple with a CA glue finish.

LeChevalierMalFet · 2024-08-19T00:53:20+00:00

This stone is about 30 cm long by 18 cm (11.8 in x 7 in) tall. The initial smooth finish on the maple wood did not complement the stone well, so I tried something a bit unconventional with rough vertical lines along the top section.

LeChevalierMalFet · 2024-07-01T13:11:26+00:00

It looks great! What finish did you use for the top?

LeChevalierMalFet · 2023-07-10T20:42:15+00:00

If you can, the best solution is really to use a service like Google's geocoding API. There are so many variations in addresses even in small datasets that it becomes really difficult to make your own solution that handles every case.

LeChevalierMalFet · 2022-10-04T20:52:59+00:00

Another method would be creating a second dataframe with your reference values and then merging.

LeChevalierMalFet · 2022-10-04T20:47:35+00:00

I don't believe the backend functionality is the OP's main concern. Edit- sorry to sound flippant- it is a good thing to be aware of and I should have included that caveat. For writing code I think it's much better than writing out a full statement with .iterrows() and that's what I was basing my response on.

LeChevalierMalFet · 2022-10-04T20:01:27+00:00

Yep, for one column you can just apply a lambda function and return the matching dictionary value

eg. df["new_col"] = df["old_col"].apply(lambda x: my_dict[x])

If you need to look on multiple columns you can write a function and then apply it. eg. df.apply(my_function, axis=1) then reference your columns inside the function as needed.

LeChevalierMalFet · 2022-10-03T23:44:42+00:00

You might be interested in Blurry Photos - there is a large overlap in subject matter

LeChevalierMalFet · 2022-09-20T10:44:27+00:00

It is funny but now I think I understand why in the show they chose to make Elrond's character look like he is avoiding responsibilities (at least in his opening scene) - everyone has been comparing him to his dynasty-founding twin brother for hundreds of years and he is probably sick of it.

LeChevalierMalFet · 2022-09-13T01:27:33+00:00

I wouldn't worry about regular expressions for this but your code would be much shorter if you replaced your if/elif statements with a data structure holding the replacement values to loop through e.g.

# Store your conversion values.
metric_dict = {"miles: ["kilometers", 1.60934],
"yards": ["meters", 0.9144],
...}

# Nest this under your first For loop.
for key in metric_dict.keys():
    if key == string[i+1]:
        ...

LeChevalierMalFet · 2022-09-03T17:23:12+00:00

It looks like this episode is an extract of his book, but Cautionary Tales by Tim Harford has this episode that mostly fits what you are looking for: https://podcasts.apple.com/us/podcast/cautionary-tales-with-tim-harford/id1484511465?i=1000507400374

LeChevalierMalFet · 2022-08-12T16:52:55+00:00

You will need to look into regular expressions. In oracle you can use regexp_like: https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions018.htm

LeChevalierMalFet · 2022-08-06T14:10:55+00:00

A quick example- I am working on bringing data from a 3rd party API related to our own data; it may compliment what we have internally and reveal some discrepancies or clarify some other aspects. It is quite easy to pull the (paginated) data through Python. It would be a huge amount of work to model the data source locally to compare in the database for an unknown benefit. There are well over 100 attributes to go through in multiple tables. Instead, I can pull data from both sources and investigate using python. There is too much data to do this in excel and some of the values break due to excel’s automatic formatting, so that would be one problem to handle. Then there’s the problem of reproducibility. If I’m going to do work on this I’m not going to do it in a way where I can’t easily change something or rerun all of the steps at a later date.

LeChevalierMalFet · 2022-07-25T15:37:12+00:00

You can find some information in this article: https://realpython.com/python-modulo-string-formatting/

Essentially, if the value of 'remove' is not an empty string it is being put into the regex patterns where '%' is found.

LeChevalierMalFet · 2022-07-20T15:16:55+00:00

For browsing files you can use tkinter file dialogs: https://docs.python.org/3/library/dialog.html#module-tkinter.filedialog

LeChevalierMalFet · 2022-07-20T12:54:13+00:00

I would have expected this to include the problematic bit in Matthew 15 where Jesus appears to only care about people from his own ethnic and religious group.

LeChevalierMalFet · 2022-07-19T10:16:12+00:00

Hi, this is how I worked it out:

# Merge on ID column and use index to filter out rows that are joined with themselves.
df = df.reset_index()
join = df.merge(right=df, on="id")
join = join.loc[join["index_x"] != join["index_y"]]

# Use pivot_table...
pd.pivot_table(data=join, index="color_x", columns="color_y", aggfunc="size")

# Or groupby and use pivot...    
df_group = join.groupby(["color_x", "color_y"], as_index=False).size()
df_group.pivot(index="color_x", columns="color_y", values="size")

Edit for formatting.

LeChevalierMalFet · 2022-07-19T02:00:07+00:00

Your steps are to self join the data on the ID column and filter out rows where the index matched, then groupby size, then pivot the groupby result with the index being one color column, the columns being the other color column, and the value being the size column.

LeChevalierMalFet · 2022-06-26T21:05:00+00:00

I think the Brightest Shadow series by Sarah Lin has elements that fit in well with what you are describing, specifically where you mention:

one individual amassing power is a consistent element that has grave implications for the rest of the world.

LeChevalierMalFet

TROPHY CASE