31

I have a long string of pandas chained commands, for example:

df.groupby[['x','y']].apply(lambda x: (np.max(x['z'])-np.min(x['z']))).sort_values(ascending=False)

And I would like to be able to present it across multiple lines but still as a one liner (without saving results to a temporary object, or defining the lambda as a function)

an example of how I would like it to look:

df.groupby[['x','y']]
.apply(lambda x: (np.max(x['z'])-np.min(x['z'])))
.sort_values(ascending=False)

Is it possible to do so? (I know '_' has this functionality in python, but it doesn't seem to work with chained commands)

Felipe Alarcon
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user2808117
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3 Answers3

36

In python you can continue to the next line by ending your line with a reverse slash or by enclosing the expression in parenthesis. 

df.groupby[['x','y']] \
.apply(lambda x: (np.max(x['z'])-np.min(x['z']))) \
.sort_values(ascending=False)

or

(df.groupby[['x','y']]
.apply(lambda x: (np.max(x['z'])-np.min(x['z'])))
.sort_values(ascending=False))
Dan
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GaryBishop
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    while this is correct syntax, it is rather unpythonic, as it makes code very hard to read. – cel Nov 26 '15 at 17:56
  • I agree, I don't like either option. – GaryBishop Nov 26 '15 at 18:06
  • I understand and agree that for code reading this is not the best option, however when presenting the code in a slide I think this would be more clear than breaking it into additional lines. Do you think otherwise? – user2808117 Nov 27 '15 at 08:56
  • In a slide it seems perfect. I code like that in Javascript all the time. In Python, I generally avoid the reverse slash form. Parenthesis used with indentation can be fine, IMO. – GaryBishop Nov 27 '15 at 12:28
  • For assigning to a new DataFrame: `df_cities = (df.groupby(['place_name'])['agent_count'] .sum() .reset_index() .sort_values(by='agent_count', ascending=False))` – Jesse Feb 24 '19 at 14:08
17

The preferred way of wrapping long lines is by using Python's implied line continuation inside parentheses, brackets and braces. Long lines can be broken over multiple lines by wrapping expressions in parentheses. These should be used in preference to using a backslash for line continuation

from https://www.python.org/dev/peps/pep-0008/#id19

So may be better:

df.groupby[['x', 'y']].apply(
    lambda x: (np.max(x['z'])-np.min(x['z']))
).sort_values(ascending=False)

The last printed expression variable "_" is known only in the Python console, so without explicit attribution cannot be used for that purpose in a script/module.

Zoli
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    I think this should be accepted answer since it's based on a widely accepted standard rather than on an opinion on what looks nicer – Liz Apr 10 '19 at 17:55
5

Since this has the nature of a command, I would probably format it close to your example, like this:

df.groupby[['x','y']] \
    .apply(lambda x: np.max(x['z'])-np.min(x['z'])) \
    .sort_values(ascending=False)

It took me a long time to realize I could break these expressions before the dots, which is often more readable than breaking inside the parentheses (same goes for "some long string".format()).

If this were more like an expression evaluation, I'd wrap the whole thing in parentheses, which is considered more "Pythonic" than line continuation markers:

var = (
    df.groupby[['x','y']]
        .apply(
            lambda x: np.max(x['z'])-np.min(x['z'])
        ) 
        .sort_values(ascending=False)
)

Update Since writing this, I've moved away from backslashes for line continuation whenever possible, including here, where it's not meaningful to chain the operations without assigning it to a variable or passing it to a function. I've also switched to using one level of indentation for each level of nesting inside parentheses or brackets, to avoid going to deep and/or getting a wiggly effect. So I would now write your expression like this:

 var = (
    df
    .groupby[['x','y']]
    .apply(
        lambda x: np.max(x['z']) - np.min(x['z'])
    ) 
    .sort_values(ascending=False)
)
Matthias Fripp
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