使用pandas后，为什么我的数据框中出现空行？

Question

I'm fairly new to Python and Pandas and trying to figure out how to do a simple split-join-apply. The problem I am having is that I am getting an blank row at the top of all the dataframes I'm getting back from Pandas' apply function and I'm not sure why. Can anyone explain?

The following is a minimal example that demonstrates the problem, not my actual code:

sorbet = pd.DataFrame({
  'flavour': ['orange', 'orange', 'lemon', 'lemon'],
  'niceosity' : [4, 5, 7, 8]})

def calc_vals(df, target) :
    return pd.Series({'total' : df[target].count(), 'mean' : df[target].mean()})

sorbet_grouped = sorbet.groupby('flavour')
sorbet_vals = sorbet_grouped.apply(calc_vals, target='niceosity')

if I then do print(sorted_vals) I get this output:

         mean  total
flavour                 <--- Why are there spaces here?
lemon     7.5      2
orange    4.5      2

[2 rows x 2 columns]

Compare this with print(sorbet) :

  flavour  niceosity     <--- Note how column names line up
0  orange          4
1  orange          5
2   lemon          7
3   lemon          8

[4 rows x 2 columns]

What is causing this discrepancy and how can I fix it?

Answer 1

The groupby/apply operation returns is a new DataFrame, with a named index. The name corresponds to the column name by which the original DataFrame was grouped.

The name shows up above the index. If you reset it to None , then that row disappears:

In [155]: sorbet_vals.index.name = None

In [156]: sorbet_vals
Out[156]: 
        mean  total
lemon    7.5      2
orange   4.5      2

[2 rows x 2 columns]

Note that the name is useful -- I don't really recommend removing it. The name allows you to refer to that index by name rather than merely by number.

---

If you wish the index to be a column, use reset_index :

In [209]: sorbet_vals.reset_index(inplace=True); sorbet_vals
Out[209]: 
  flavour  mean  total
0   lemon   7.5      2
1  orange   4.5      2

[2 rows x 3 columns]