Something I love about pandas is that often you can pass a URL in place of a file name.

The other day I needed to scrape data from a table on a webpage. Thinking about traversing the DOM and building up an array was already giving me a headache. Thankfully pandas has the “read_html” function. Getting a list of dataframes for each table on the page was as easy as:

  dfs = pd.read_html(url)

1. pandas can do date ranges date_range =

pd.date_range(date_from, date_to, freq="D")

2. merge with indicator

if you set `indicator` parameter of merge() to True pandas adds a column that tells you which dataset the row came from

3. merge with approximate match - the tolerance parameter of merge_asof()

pd.merge_asof(trades, quotes, on="timestamp", by='ticker', tolerance=pd.Timedelta('10ms'), direction='backward')

4. Create an Excel report and add some charts

5. Save the dataframe in gzipped form

edit: formatting

edit2: I managed to get an outline link

https://outline.com/fY26Aw

This is basically a summary of the link posted by OP.

Edit: not that it's a bad thing, I just mention it so people who read this know for sure they can avoid the link if they already know how to use the tips listed here. The article has code examples and more details.

On the flipside, now you need to validate your input in one more way if you don't want to accidentally be connecting to somewhere on the internet.

EDIT: for instance, it looks like on a suitably configured environment, passing in `s3://` and `gs://` URLs works fine too...

That is a useful one, yes.

Medium wants me to upgrade my account to read this article, please people share your posts in somewhere else.

You are not missing much, the article isn't groundbreaking.

Just block all cookies for medium

use incognito

Merge with indicator is also useful for doing anti-joins:

    left.merge(right, how="left", indicator=True, ...)
    [lambda df: df._merge == "left_only"]

My favorite, most elegant SO answer I've ever gotten was to a question about Pandas.

The question was "How do I create a column where each row's value is the mean of another column's values starting at that row?" The answer was:

  df.loc[::-1, 'col_1'].expanding().mean()[::-1]

I guess elegance is in the eye of the beholder.

Separately, I find it upsetting there are at least 6 ways of reversing a dataframe. It suggests some API smell.

I get tripped up by the Pandas API no matter how often I use it, and when I haven't used it in a while I practically have to re-learn all the basics. But whenever it does exactly what I need in a readable one-liner, I forgive it for all its transgressions. It's an abusive relationship.

I dunno about elegant... seems like pandas is missing a reverse parameter in expanding. Surely this would be preferable:

df[col].expanding(reverse=True).mean()

Note that there is a handy PeriodIndex version of pd.date_range:

  pd.period_range(date_from, date_to, freq = "D")

AFAICT, a PeriodIndex and DateTimeIndex function mostly the same, and have many of the same methods, except...

  * DateTimeIndex can't hold dates far in the future
  * PeriodIndex can't easily round to the end of a period (e.g. date + 0*MonthEnd() errors)
  * PeriodIndex doesn't handle timezones?

TLDR;

5 lesser-known pandas tricks:

1. Date Ranges

2. Merge with indicator

3. Nearest merge by timestamp

4. Create an Excel report from pandas

5. Use gzip with when saving to csv

Does anyone want to do a TLDR? I don't especially want to sign into Medium.

you have it above

Ok, that's been posted since I posted this question.