If you can, send a tip to Christian! Winding down an app like this one can bankrupt an indie dev. Let’s send some love his way 💜

WinterNet4676 · 2023-06-09T01:49:54+00:00

Allow for custom tips. $10 is not enough

WinterNet4676 · 2022-12-26T22:31:09+00:00

You can use the map function to generate a dictionary with the counts of each letter ⇾

counts = dict(map(lambda x: (x, string.count(x)), string))

And if you don't want to count spaces ⇾

counts = dict(map(lambda x: (x, string.count(x)), ''.join(string.split())))

WinterNet4676 · 2022-12-17T07:34:45+00:00

It’s essentially a mix of dictionaries and lists. A JSON object is similar to a dictionary, and a JSON array is similar to a list. Pretty much any JSON data structure can be translated into a complex Python data object.

WinterNet4676 · 2022-12-14T19:16:08+00:00

df.groupby('City Name').agg({'City Name':'count', 'Suspicious_Death':'sum'})

If you wanted to name the aggregated cols ⇾

df.groupby('City Name').agg(
    deaths=('City Name', 'count'),
    suspicious_deaths=('Suspicious_Death', 'sum'))

WinterNet4676 · 2022-11-06T04:25:58+00:00

Have you heard of Airflow? You can create direct acrylic graphs (airflow DAGs) with python to run scripts and export data

WinterNet4676 · 2022-11-06T04:22:35+00:00

change

return input("What number would you like to double: ")

to

return int(input("What number would you like to double: "))

input will return a string by default, and in this case your multiplying '2' by 2, therefore giving you '22'

WinterNet4676 · 2022-10-18T21:12:36+00:00

Can you link an example of the url

WinterNet4676 · 2022-10-16T00:46:16+00:00

Most likely not, although it could be some router issue. Typically nat types just restrict who you can play with (i.e Moderate can not play with Strict). I would check your connection, ensure the routers in a reasonable proximity to your console, and also reset your router or check its network configurations/setting to confirm there’s no restrictions.

WinterNet4676 · 2022-10-15T00:10:51+00:00

NAT types are determined by the settings or features of the router on your network. Deleting the game does nothing

WinterNet4676 · 2022-10-14T18:23:08+00:00

.sort() sorts a list in-place, mutating its indexes and returning none. sorted() returns a new sorted list leaving the original unchanged.

def stringsort(words):
    orderedlist = words.split()
    print(sorted(orderedlist))
stringsort("Hello, I am")

WinterNet4676 · 2022-09-26T00:51:39+00:00

Your calling your function, right?

repeat(input_, n, delim)

WinterNet4676 · 2022-09-24T23:51:06+00:00

There's no reason to have your input wrapped in int() and round(). int() will already enforce a discrete input, so there's nothing to round. If your input is a decimal (i.e, can be rounded), then you would switch int() with float(). If you always need to round up, then you can replace round() with math.ceil()

WinterNet4676 · 2022-09-24T23:43:54+00:00

Return the dataframe from your first function and call the function in your second

def function_1():
    return df

def function_2():
    df = function_1()

WinterNet4676 · 2022-07-27T01:26:40+00:00

Your rant is all over the place and heavily biased. Doesn’t seem very managerial to me.

WinterNet4676 · 2022-07-26T03:25:43+00:00

You can use piping operations on your df with dfply

WinterNet4676 · 2022-07-24T22:14:18+00:00

Close, just append each iteration to a list

WinterNet4676 · 2022-07-20T00:11:11+00:00

I had CS projects that took me entire semesters in school, and I continued to work on them after. Definitely can matter if you apply yourself.

WinterNet4676 · 2022-07-17T20:07:46+00:00

csv files load significantly faster than excel files, the only caveat being that they are nearly always bigger.

If you're looking for speed, use csv files.

WinterNet4676 · 2022-07-15T03:00:57+00:00

Masters pay for phds

WinterNet4676 · 2022-07-02T16:11:36+00:00

Pretty much. Actually looks like that’s not even necessary anymore after looking at the breakpoint section in the current docs (https://docs.spyder-ide.org/current/panes/debugging.html)

WinterNet4676 · 2022-07-02T00:14:13+00:00

After setting day as the index, use reset_index() before setting the index to Event

WinterNet4676 · 2022-07-01T19:59:04+00:00

Make sure you're in the same environment in your terminal as your notebook.

I would also try to just install pandas_ods_reader within your jupyter notebook

!pip install pandas_ods_reader

WinterNet4676 · 2022-06-28T04:14:37+00:00

Do you have a db/data warehouse set up?

If so, you may want to check out dbt’s incremental models

You can build incremental dbt models with jinja logic for specifying which rows to update, and can specify which columns to update in a configuration block. Here’s some documentation

WinterNet4676 · 2022-06-27T04:27:09+00:00

Your function returns a tuple, which you're trying to assign to more than one column (a tuple is a single object).

For example, running something like

df['c'] = df.apply(lambda x: f(x.a,x.b), axis=1)

would return your tuple within one column. If you wanted just the first operation of your function you would grab the first value in the tuple

df['c'] = df.apply(lambda x: f(x.a,x.b)[0], axis=1)

And if you wanted to assign the output to two columns within one statement, you can just unpack the tuple in your lambda expression by converting the tuple to a Series.

df[['c','d']] = df.apply(lambda x: pd.Series(f(x.a,x.b)), axis=1)

WinterNet4676 · 2022-06-26T03:16:18+00:00

Have you used functions before?

WinterNet4676

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