DOC: pandas cheat sheet

[jreback]

https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

I think we could pretty much rip this off exactly as is and just substitute pandas functions directly.

Further could update comparison with R a bit.

anyone up for this?

[jorisvandenbossche]

+1 I have wanted to do this already for a long time! (but not sure I will be able to :-))

[sinhrks]

+1. shall we close #1618?

[MrMauricioLeite]

+1

[Dr-Irv]

I don't know R, but know that having a cheatsheet like this would be helpful. I'm going to give it a shot in Powerpoint, because I know it well and can make it pretty. I'm not sure what other tool to use that would let other people edit it and provide the good formatting.

[jreback]

@Dr-Irv we will just check in the pdf and whatever format it's in

[Dr-Irv]

Here is what I did today. Plagiarizing is blatant! Will be flying a lot next week, so this will be good to do on the plane.
Pandas Cheat Sheet.pdf

[TomAugspurger]

Nice start! That original from RStudio is CC 4.0, so AFAIK it's fine to copy / transform it as long as you acknowledge RStudio for the original.

[Dr-Irv]

Here is the first page of the cheat sheet. I'll be working on page 2 next. In most places, I found the pandas way of doing things. I used @jreback initial comment as a guideline, i.e., "I think we could pretty much rip this off exactly as is and just substitute pandas functions directly." There were places where the R example didn't make sense, so I made my own arbitrary choices.

Comments and criticism are welcome. I think it is better to get feedback here rather than anywhere else.
Pandas Cheat Sheet.pdf

[jreback]

for the subset section where you have:

df[['width','length','species']]
  Select multiple columns with specific names.
df['width'] or df.width
  Select single column with specific name.
df[[i for i in df.columns if '.' in i]]
  Select columns whose name contains a character string.
df[[i for i in df.columns if i.endswith("Length")]]
  Select columns whose name ends with a character string.
df[[i for i in df.columns if re.match('.t.',i)]]
  Select columns whose name matches a regular expression.
df[["x"+str(i) for i in range(1,6)]
  Select columns named x1, x2, x3, x4, x5.
df[[i for i in df.columns if i.startswith("Length")]]
  Select columns whose name starts with a character string.
df.loc[:,"x2":"x4"]
  Select all columns between x2 and x4 (inclusive).
df[[i for i in df.columns if i != "Species"]]
  Select all columns except Species.
df.iloc[:,[1,2,5]]
   Select columns in positions 1, 2 and 5

more idiomatic to do:

df[['width','length','species']]
  Select multiple columns with specific names.
df['width'] or df.width
  Select single column with specific name.
df.filter(regex='\.')
  Select columns whose name contains a character string.
df.filter(regex='Length$')
  Select columns whose name ends with a character string.
df.fitler(regex='.t.')
  Select columns whose name matches a regular expression.
df.filter(regex='x\d')
  Select columns named x1, x2, x3, x4, x5.
df.filter(regex='^Length')
  Select columns whose name starts with a character string.
df.loc[:,"x2":"x4"]
  Select all columns between x2 and x4 (inclusive).
df.filter(regex="^(?!Species|\\.).*")
  Select all columns except Species.
df.iloc[:,[1,2,5]]
   Select columns in positions 1, 2 and 5

though maybe elminate some of these column selections and show usage of .loc instead

[Dr-Irv]

@jreback Thanks for the suggestions. I've made changes in my current working copy.

Separate question - R has a cume_dist() method that computes the cumulative distribution of a vector. Similar to rank(pct=True), but different. Is there a pandas equivalent?

[Dr-Irv]

Here's my latest version. Only thing left to do is to do groupby() examples in the open space on the second page. Some notes: (1) Don't have correspondence to R's cume_dist(), so left it out, and added clip() instead. (2) Setdiff between DataFrames is not there (discussion in #4480) so the example at bottom right of page 2 isn't so pretty. Maybe someone knows a better way.
Pandas Cheat Sheet.pdf