For example:
0 1
0 87.0 NaN
1 NaN 99.0
2 NaN NaN
3 NaN NaN
4 NaN 66.0
5 NaN NaN
6 NaN 77.0
7 NaN NaN
8 NaN NaN
9 88.0 NaN
My expected output is: [False, True]
since 87 is the first !NaN value but not the maximum in column 0
. 99
however is the first !NaN value and is indeed the max in that column.
Option a): Just do groupby
with first
(May not be 100% reliable )
df.groupby([1]*len(df)).first()==df.max()
Out[89]:
0 1
1 False True
Option b): bfill
Or using bfill
(Fill any NaN value by the backward value in the column , then the first row after bfill
is the first not NaN
value )
df.bfill().iloc[0]==df.max()
Out[94]:
0 False
1 True
dtype: bool
Option c): stack
df.stack().reset_index(level=1).drop_duplicates('level_1').set_index('level_1')[0]==df.max()
Out[102]:
level_1
0 False
1 True
dtype: bool
Option d): idxmax
with first_valid_index
df.idxmax()==df.apply(pd.Series.first_valid_index)
Out[105]:
0 False
1 True
dtype: bool
Option e)(From Pir): idxmax
with isna
df.notna().idxmax() == df.idxmax()
Out[107]:
0 False
1 True
dtype: bool
Using pure numpy
(I think this is very fast)
>>> np.isnan(df.values).argmin(axis=0) == df.fillna(-np.inf).values.argmax(axis=0)
array([False, True])
The idea is to compare if the index of the first non-nan is also the index of the argmax
.
Timings
df = pd.concat([df]*1000).reset_index(drop=True) # setup
%timeit np.isnan(df.values).argmin(axis=0) == df.fillna(-np.inf).values.argmax(axis=0)
207 µs ± 8.83 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df.groupby([1]*len(df)).first()==df.max()
9.78 ms ± 339 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit df.bfill().iloc[0]==df.max()
824 µs ± 47.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df.stack().reset_index(level=1).drop_duplicates('level_1').set_index('level_1')[0]==df.max()
3.55 ms ± 249 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit df.idxmax()==df.apply(pd.Series.first_valid_index)
1.5 ms ± 25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df.values[df.notnull().idxmax(), np.arange(df.shape[1])] == df.max(axis=0)
1.13 ms ± 14.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df.values[(~np.isnan(df.values)).argmax(axis=0), np.arange(df.shape[1])] == df.max(axis=0).values
450 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
We can use numpy
's nanmax
here for an efficient solution:
a = df.values
np.nanmax(a, 0) == a[np.isnan(a).argmin(0), np.arange(a.shape[1])]
array([False, True])
Timings (Whole lot of options presented here):
Functions
def chris(df):
a = df.values
return np.nanmax(a, 0) == a[np.isnan(a).argmin(0), np.arange(a.shape[1])]
def bradsolomon(df):
df.values[df.notnull().idxmax(), np.arange(df.shape[1])] == df.max(axis=0).values
def wen1(df):
return df.groupby([1]*len(df)).first()==df.max()
def wen2(df):
return df.bfill().iloc[0]==df.max()
def wen3(df):
return df.idxmax()==df.apply(pd.Series.first_valid_index)
def rafaelc(df):
return np.isnan(df.values).argmin(axis=0) == df.fillna(-np.inf).values.argmax(axis=0)
def pir(df):
return df.notna().idxmax() == df.idxmax()
Setup
res = pd.DataFrame(
index=['chris', 'bradsolomon', 'wen1', 'wen2', 'wen3', 'rafaelc', 'pir'],
columns=[10, 20, 30, 100, 500, 1000],
dtype=float
)
for f in res.index:
for c in res.columns:
a = np.random.rand(c, c)
a[a > 0.4] = np.nan
df = pd.DataFrame(a)
stmt = '{}(df)'.format(f)
setp = 'from __main__ import df, {}'.format(f)
res.at[f, c] = timeit(stmt, setp, number=50)
ax = res.div(res.min()).T.plot(loglog=True)
ax.set_xlabel("N");
ax.set_ylabel("time (relative)");
plt.show()
Results
You can do something similar to Wens' answer with the underlying Numpy arrays:
>>> df.values[df.notnull().idxmax(), np.arange(df.shape[1])] == df.max(axis=0).values
array([False, True])
df.max(axis=0)
gives the column-wise max.
The left hand side indexes df.values
, which is a 2d array, to make it a 1d array and compare it element-wise to the maxes per column.
If you exclude .values
from the right-hand side, the result will just be a Pandas Series:
>>> df.values[df.notnull().idxmax(), np.arange(df.shape[1])] == df.max(axis=0)
0 False
1 True
dtype: bool
After posting the question I came up with this:
def nice_method_name_here(sr):
return sr[sr > 0][0] == np.max(sr)
print(df.apply(nice_method_name_here))
which seems to work, but not sure yet!