[英]How to change consecutive repeating values in pandas dataframe series to nan or 0?
[英]How to group consecutive NaN values from a Pandas Series in a set of slices?
我想將連續的NaN
值合並到切片中。 是否有一種簡單的方法可以用numpy或pandas做到這一點?
l = [
(996, np.nan), (997, np.nan), (998, np.nan),
(999, -47.3), (1000, -72.5), (1100, -97.7),
(1200, np.nan), (1201, np.nan), (1205, -97.8),
(1300, np.nan), (1302, np.nan), (1305, -97.9),
(1400, np.nan), (1405, -97.10), (1408, np.nan)
]
l = pd.Series(dict(l))
預期結果:
[
(slice(996, 999, None), array([nan, nan, nan])),
(999, -47.3),
(1000, -72.5),
(1100, -97.7),
(slice(1200, 1202, None), array([nan, nan])),
(1205, -97.8),
(slice(1300, 1301, None), array([nan])),
(slice(1302, 1303, None), array([nan])),
(1305, -97.9),
(slice(1400, 1401, None), array([nan])),
(1405, -97.1),
(slice(1408, 1409, None), array([nan]))
]
具有兩個維度的numpy數組也可以,而不是元組列表
更新2019/05/31 :我剛剛意識到,如果我只使用字典而不是Pandas系列,那么algorythm會更有效率
你想要的是完整或角落情況,nan相等,每對的第一個元素是一個切片或一個單獨的值,第二個是np.array或單個值。
對於如此復雜的需求,我只依賴於普通的Python非向量化方式:
def trans(ser):
def build(last, cur, val):
if cur == last + 1:
if np.isnan(val):
return (slice(last, cur), np.array([np.nan]))
else:
return (last, val)
else:
return (slice(last, cur), np.array([val] * (cur - last)))
last = ser.iloc[0]
old = last_index = ser.index[0]
resul = []
for i in ser.index[1:]:
val = ser[i]
if ((val != last) and not(np.isnan(val) and np.isnan(last))) \
or i != old + 1:
resul.append(build(last_index, old + 1, last))
last_index = i
last = val
old = i
resul.append(build(last_index, old+1, last))
return resul
它提供了接近預期結果的東西:
[(slice(996, 999, None), array([nan, nan, nan])),
(999, -47.3),
(1000, -72.5),
(1100, -97.7),
(slice(1200, 1202, None), array([nan, nan])),
(1205, -97.8),
(slice(1300, 1301, None), array([nan])),
(slice(1302, 1303, None), array([nan])),
(1305, -97.9),
(slice(1400, 1401, None), array([nan])),
(1405, -97.1),
(slice(1408, 1409, None), array([nan]))]
cumsum
的notnull
是一個好主意,但是我們需要過濾掉每個子系列中的第一個非空值,這樣我們就可以對該對進行(cumsum, notnull)
:
# convert series to frame,
# don't know why series only doesn't work
df = l.to_frame(name='val')
df['notnull'] = df['val'].notnull()
g = df.groupby([ df['notnull'].cumsum(), 'notnull']).val
[(v.index, v.values) for i, v in g]
日期:
[(Int64Index([996, 997, 998], dtype='int64'), array([nan, nan, nan])),
(Int64Index([1200, 1201], dtype='int64'), array([nan, nan])),
(Int64Index([1300, 1302, 1400, 1402], dtype='int64'),
array([nan, nan, nan, nan])),
(Int64Index([999], dtype='int64'), array([-47.3])),
(Int64Index([1000], dtype='int64'), array([-72.5])),
(Int64Index([1100], dtype='int64'), array([-97.7])),
(Int64Index([1202], dtype='int64'), array([-97.1]))]
編輯:考慮連續索引並更新切片:
# convert group to slices
def get_slice(x):
idx_min, idx_max = x.index.min(), x.index.max()
if len(x) >1:
return (slice(idx_min, idx_max+1), x.values)
elif x.isna().any():
return (slice(idx_min, idx_min+1), x.values)
else:
return (idx_min, x[idx_min])
df['notnull'] = df['val'].notnull()
# non-continuous indices
df['sep'] = (df.index != df.index.to_series().shift() + 1).cumsum()
g = df.groupby(['sep', df['notnull'].cumsum(), 'notnull']).val
g.apply(get_slice).values.tolist()
得到:
[(slice(996, 999, None), array([nan, nan, nan])),
(999, -47.3),
(1000, -72.5),
(1100, -97.7),
(slice(1200, 1202, None), array([nan, nan])),
(1205, -97.8),
(slice(1300, 1301, None), array([nan])),
(slice(1302, 1303, None), array([nan])),
(1305, -97.9),
(slice(1400, 1401, None), array([nan])),
(1405, -97.1),
(slice(1408, 1409, None), array([nan]))]
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