New answers tagged pandas
3
There are a few small niceties that you can add.
long = pd.DataFrame(columns={'bene_id', 'day','date'})
cols_to_order = ['bene_id', 'day','date']
should reuse the list:
cols_to_order = ['bene_id', 'day','date']
long = pd.DataFrame(columns=set(cols_to_order))
This:
cols_to_order + (long.columns.drop(cols_to_order).tolist())
can drop the outer parens, since ...
3
Your rawData (which should be ideally named raw_data, python suggests a style guide to name variables and functions in lower_snake_case) is already in a list structure. You can manipulate this in place, without having to process the whole dataset manually.
for row in raw_data:
row.update(row.pop("prices"))
0
Here's code using .apply(), that makes a single pass through the dataframe.
df = pd.DataFrame({'category': [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1],
'value': range(12),
'other': list('abcdefghijkl')})
def pick(group, n):
"""Return n random rows from groups that have at least n rows"""
...
1
This is not too bad. It's a good thing you use keyword arguments for the replace method
I always try to keep my original data in its original state, and continue with the cleaned dataframe.
fluent style
This lends itself very well to a kind of fluent style as in this example. I use it too, and use a lot of df.assign, df.pipe, df.query...
In this example I ...
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