I have a very large knowledge graph in pandas dataframe format as follows.
This dataframe KG has more than 100 million rows:
pred subj obj
0 nationality BART USA
1 placeOfBirth BART NEWYORK
2 locatedIn NEWYORK USA
... ... ... ...
116390740 hasFather BART HOMMER
116390741 nationality HOMMER USA
116390743 placeOfBirth HOMMER NEWYORK
I tried to get a row from this KG with a specific value for subj and obj.
a) I tried indexing into KG by generating a boolean series using isin() function:
KG[KG['subj'].isin(['BART', 'NEWYORK']) & KG['obj'].isin(['USA', 'HOMMER'])]
b) I also tried indexing the KG using query() function:
KG = KG.set_index(['subj','obj'], drop=True)
KG = KG.sort_index()
subj_substitution = ['BART', 'NEWYORK']
obj_substitution= ['USA', 'HOMMER']
KG.query(f"subj in {subj_substitution} & obj in {obj_substitution}
c) And I also tried to join two DataFrames using a merge() as shown below.
subj_df
subj
0 BART
1 NEWYORK
obj_df
obj
0 USA
1 HOMMER
merge_result = pd.merge(KG, subj_df, on = ['subj']).drop_duplicates()
merge_result = pd.merge(merge_result, obj_df, on = ['obj']).drop_duplicates()
These methods result in the following:
pred subj obj
0 nationality BART USA
2 locatedIn NEWYORK USA
116390740 hasFather BART HOMMER
I used the timeit function to check the time for each as shown below.
timeit.timeit(lambda: KG[(KG['subj'].isin(['BART', 'NEWYORK']) & (KG['obj'].isin(['USA', 'HOMMER'])))] , number=10)
The runtimes were:
function
runtime
isin()
35.6s
query()
155.2s
merge()
288.9s
I think isin() is the fastest way to index a very large Dataframe.
I would appreciate it if you could tell me a faster way than this.
I would personally go with isin or query with in.
Pandas doc says:
Performance of query()
DataFrame.query() using numexpr is slightly faster than Python for large frames.
Note: You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200,000 rows.
Details about query can be found here
In your example when I tested KG dataframe with shape (50331648, 3) - 50M+ rows and 3 column using query and isin the performance results were almost same.
isin
%timeit KG[KG['subj'].isin(['BART', 'NEWYORK']) & KG['obj'].isin(['USA', 'HOMMER'])]
4.14 s ± 83.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
query with in operator
%timeit KG.query("(subj in ['BART', 'NEWYORK']) and (obj in ['USA', 'HOMMER'])")
4.08 s ± 82.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
query with isin
%timeit KG.query("(subj.isin(['BART', 'NEWYORK']))& (obj.isin(['USA', 'HOMMER']))")
4.99 s ± 210 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Test Data
d="""pred,subj,obj
nationality,BART,USA
placeOfBirth,BART,NEWYORK
locatedIn,NEWYORK,USA
hasFather,BART,HOMMER
nationality,HOMMER,USA
placeOfBirth,HOMMER,NEWYORK"""
KG = pd.read_csv(StringIO(d))
for i in range(23):
KG = pd.concat([KG,KG])
KG.shape # (50331648, 3)
If performance + code readability(maintenance) is concerned, then atleast for complex queries I would go with query function.
I am just trying to calculate the percentage of one column against another's total, but I am unsure how to do this in Pandas so the calculation gets added into a new column.
Let's say, for argument's sake, my data frame has two attributes:
Number of Green Marbles
Total Number of Marbles
Now, how would I calculate the percentage of the Number of Green Marbles out of the Total Number of Marbles in Pandas?
Obviously, I know that the calculation will be something like this:
(Number of Green Marbles / Total Number of Marbles) * 100
Thanks - any help is much appreciated!
By default, arithmetic operations on pandas dataframes are element-wise, so this is as simple as it can be:
import pandas as pd
>>> d = pd.DataFrame()
>>> d['green'] = [3,5,10,12]
>>> d['total'] = [8,8,20,20]
>>> d
green total
0 3 8
1 5 8
2 10 20
3 12 20
>>> d['percent_green'] = d['green'] / d['total'] * 100
>>> d
green total percent_green
0 3 8 37.5
1 5 8 62.5
2 10 20 50.0
3 12 20 60.0
References:
pandas.DataFrame.div documentation;
Adding new column to existing dataframe in python pandas?
df['percentage columns'] = (df['Number of Green Marbles']) / (df['Total Number of Marbles'] ) * 100
Here is my comparison of regular vs vectorized approach:
%timeit us_consum['Commercial_%ofUS'] = us_consum['Commercial_MWhrs']*100/us_consum['Total US consumption (MWhr)']
351 µs ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit us_consum['Commercial_%ofUS'] = (us_consum['Commercial_MWhrs'].div(us_consum['Total US consumption (MWhr)']))*100
337 µs ± 60.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
I am running a groupby rolling count, sum & mean using Pandas v1.1.0 and I notice that the rolling count is considerably slower than the rolling mean & sum. This seems counter intuitive as we can derive the count from the mean and sum and save time. Is this a bug or am I missing something? Grateful for advice.
import pandas as pd
# Generate sample df
df = pd.DataFrame({'column1': range(600), 'group': 5*['l'+str(i) for i in range(120)]})
# sort by group for easy/efficient joining of new columns to df
df=df.sort_values('group',kind='mergesort').reset_index(drop=True)
# timing of groupby rolling count, sum and mean
%timeit df['mean']=df.groupby('group').rolling(3,min_periods=1)['column1'].mean().values
%timeit df['sum']=df.groupby('group').rolling(3,min_periods=1)['column1'].sum().values
%timeit df['count']=df.groupby('group').rolling(3,min_periods=1)['column1'].count().values
### Output
6.14 ms ± 812 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
5.61 ms ± 179 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
76.1 ms ± 4.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
### df Output for illustration
print(df.head(10))
column1 group mean sum count
0 0 l0 0.0 0.0 1.0
1 120 l0 60.0 120.0 2.0
2 240 l0 120.0 360.0 3.0
3 360 l0 240.0 720.0 3.0
4 480 l0 360.0 1080.0 3.0
5 1 l1 1.0 1.0 1.0
6 121 l1 61.0 122.0 2.0
7 241 l1 121.0 363.0 3.0
8 361 l1 241.0 723.0 3.0
9 481 l1 361.0 1083.0 3.0
Did you really mean count (number of non-NaN values)? That can not be inferred from just sum and mean.
I suspect that what you are looking for would be a size operator (just the length of the group, irrespective of whether or not there are any NaNs). While size exists in regular groupby, it seems that it is absent in RollingGroupBy (at least as of pandas 1.1.4). One can calculate the size of the rolling groups with:
# DRY:
rgb = df.groupby('group').rolling(3, min_periods=1)['column1']
# size is either:
rgb.apply(len)
# or
rgb.apply(lambda g: g.shape[0])
Neither of those two is as fast as it could, of course, because there needs to be a call to the function for each group, rather than being all vectorized and working just off of the rolling window indices start and end. On my system, either of the above is 2x slower than rgb.sum() or rgb.mean().
Thinking about how one would implement size: it is obvious (just end - start for each window).
Now, in the case one really wanted to speed up count (count of non-NaN values): one could establish a "cumulative count" at first:
cumcnt = (1 - df['column1'].isnull()).cumsum()
(this is very fast, about 200x faster than rgb.mean() on my system).
Then the rolling function could simply take cumcnt[end] - cumcnt[start].
I don't know enough about the internals of RollingGroupBy (and their use of various mixins) to assess feasibility, but at least functionally it seems pretty straightforward.
Update:
It seems that the issue is already fixed with these commits. That was fast and simple --I am impressed with the internal architecture of pandas and all the tools they already have on their Swiss army knife!
I have a df which contains of categorical and numerical data
df = {'Name':['Tom', 'nick', 'krish', 'jack'],
'Address':['Oxford', 'Cambridge', 'Xianjiang', 'Wuhan'],
'Age':[20, 21, 19, 18],
'Weight':[50, 61, 69, 78]}
df = pd.DataFrame(df)
I need to replace 50 % in each column to NaN randomly, so the result might look like this picture
how to do that with the most efficient techique because I have large number of rows and columns, and I'll do many repetitions.
Use apply with sample
df_final = df.apply(lambda x: x.sample(frac=0.5)).reindex(df.index)
Out[175]:
Name Address Age Weight
0 Tom NaN NaN 50.0
1 NaN NaN NaN 61.0
2 krish Xianjiang 19.0 NaN
3 NaN Wuhan 18.0 NaN
Improving three times the performance of previous answers, mostly inspired on #jezrael , I suggest using argpartition instead of argsort, since the sorting performed is rather useless:
df1 = df.mask(np.random.rand(*df.shape).argpartition(0, axis=0) >= df.shape[0] // 2)
print(df1)
Name Address Age Weight
0 NaN Oxford NaN 50.0
1 nick Cambridge 21.0 61.0
2 NaN NaN NaN NaN
3 jack NaN 18.0 NaN
Performance comparison
# Reusing the same comparison dataset
df = pd.concat([df] * 50000, ignore_index=True)
df = pd.concat([df] * 50, ignore_index=True, axis=1)
# #Andy's answer, using apply and sample
%timeit df.apply(lambda x: x.sample(frac=0.5)).reindex(df.index)
9.72 s ± 532 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# #jezrael's answer, based on mask, np random and argsort
%timeit df.mask(np.random.rand(*df.shape).argsort(axis=0) >= df.shape[0] // 2)
8.23 s ± 732 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# This answer, based on mask, np random and argpartition
%timeit df.mask(np.random.rand(*df.shape).argpartition(0, axis=0) >= df.shape[0] // 2)
2.54 s ± 98.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
It can be done by taking random numbers in the range of your tuples and run a loop over them and consider that as index to replace with NaaN
example:
if you have 10 tuples
from random number generator set range to 0 to 9 and
and take result of above operation as index to replace with NaN
I have seen many answers posted to questions on Stack Overflow involving the use of the Pandas method apply. I have also seen users commenting under them saying that "apply is slow, and should be avoided".
I have read many articles on the topic of performance that explain apply is slow. I have also seen a disclaimer in the docs about how apply is simply a convenience function for passing UDFs (can't seem to find that now). So, the general consensus is that apply should be avoided if possible. However, this raises the following questions:
If apply is so bad, then why is it in the API?
How and when should I make my code apply-free?
Are there ever any situations where apply is good (better than other possible solutions)?
apply, the Convenience Function you Never Needed
We start by addressing the questions in the OP, one by one.
"If apply is so bad, then why is it in the API?"
DataFrame.apply and Series.apply are convenience functions defined on DataFrame and Series object respectively. apply accepts any user defined function that applies a transformation/aggregation on a DataFrame. apply is effectively a silver bullet that does whatever any existing pandas function cannot do.
Some of the things apply can do:
Run any user-defined function on a DataFrame or Series
Apply a function either row-wise (axis=1) or column-wise (axis=0) on a DataFrame
Perform index alignment while applying the function
Perform aggregation with user-defined functions (however, we usually prefer agg or transform in these cases)
Perform element-wise transformations
Broadcast aggregated results to original rows (see the result_type argument).
Accept positional/keyword arguments to pass to the user-defined functions.
...Among others. For more information, see Row or Column-wise Function Application in the documentation.
So, with all these features, why is apply bad? It is because apply is slow. Pandas makes no assumptions about the nature of your function, and so iteratively applies your function to each row/column as necessary. Additionally, handling all of the situations above means apply incurs some major overhead at each iteration. Further, apply consumes a lot more memory, which is a challenge for memory bounded applications.
There are very few situations where apply is appropriate to use (more on that below). If you're not sure whether you should be using apply, you probably shouldn't.
Let's address the next question.
"How and when should I make my code apply-free?"
To rephrase, here are some common situations where you will want to get rid of any calls to apply.
Numeric Data
If you're working with numeric data, there is likely already a vectorized cython function that does exactly what you're trying to do (if not, please either ask a question on Stack Overflow or open a feature request on GitHub).
Contrast the performance of apply for a simple addition operation.
df = pd.DataFrame({"A": [9, 4, 2, 1], "B": [12, 7, 5, 4]})
df
A B
0 9 12
1 4 7
2 2 5
3 1 4
<!- ->
df.apply(np.sum)
A 16
B 28
dtype: int64
df.sum()
A 16
B 28
dtype: int64
Performance wise, there's no comparison, the cythonized equivalent is much faster. There's no need for a graph, because the difference is obvious even for toy data.
%timeit df.apply(np.sum)
%timeit df.sum()
2.22 ms ± 41.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
471 µs ± 8.16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Even if you enable passing raw arrays with the raw argument, it's still twice as slow.
%timeit df.apply(np.sum, raw=True)
840 µs ± 691 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Another example:
df.apply(lambda x: x.max() - x.min())
A 8
B 8
dtype: int64
df.max() - df.min()
A 8
B 8
dtype: int64
%timeit df.apply(lambda x: x.max() - x.min())
%timeit df.max() - df.min()
2.43 ms ± 450 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
1.23 ms ± 14.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In general, seek out vectorized alternatives if possible.
String/Regex
Pandas provides "vectorized" string functions in most situations, but there are rare cases where those functions do not... "apply", so to speak.
A common problem is to check whether a value in a column is present in another column of the same row.
df = pd.DataFrame({
'Name': ['mickey', 'donald', 'minnie'],
'Title': ['wonderland', "welcome to donald's castle", 'Minnie mouse clubhouse'],
'Value': [20, 10, 86]})
df
Name Value Title
0 mickey 20 wonderland
1 donald 10 welcome to donald's castle
2 minnie 86 Minnie mouse clubhouse
This should return the row second and third row, since "donald" and "minnie" are present in their respective "Title" columns.
Using apply, this would be done using
df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)
0 False
1 True
2 True
dtype: bool
df[df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)]
Name Title Value
1 donald welcome to donald's castle 10
2 minnie Minnie mouse clubhouse 86
However, a better solution exists using list comprehensions.
df[[y.lower() in x.lower() for x, y in zip(df['Title'], df['Name'])]]
Name Title Value
1 donald welcome to donald's castle 10
2 minnie Minnie mouse clubhouse 86
<!- ->
%timeit df[df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)]
%timeit df[[y.lower() in x.lower() for x, y in zip(df['Title'], df['Name'])]]
2.85 ms ± 38.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
788 µs ± 16.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
The thing to note here is that iterative routines happen to be faster than apply, because of the lower overhead. If you need to handle NaNs and invalid dtypes, you can build on this using a custom function you can then call with arguments inside the list comprehension.
For more information on when list comprehensions should be considered a good option, see my writeup: Are for-loops in pandas really bad? When should I care?.
Note
Date and datetime operations also have vectorized versions. So, for example, you should prefer pd.to_datetime(df['date']), over,
say, df['date'].apply(pd.to_datetime).
Read more at the
docs.
A Common Pitfall: Exploding Columns of Lists
s = pd.Series([[1, 2]] * 3)
s
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
People are tempted to use apply(pd.Series). This is horrible in terms of performance.
s.apply(pd.Series)
0 1
0 1 2
1 1 2
2 1 2
A better option is to listify the column and pass it to pd.DataFrame.
pd.DataFrame(s.tolist())
0 1
0 1 2
1 1 2
2 1 2
<!- ->
%timeit s.apply(pd.Series)
%timeit pd.DataFrame(s.tolist())
2.65 ms ± 294 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
816 µs ± 40.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Lastly,
"Are there any situations where apply is good?"
Apply is a convenience function, so there are situations where the overhead is negligible enough to forgive. It really depends on how many times the function is called.
Functions that are Vectorized for Series, but not DataFrames
What if you want to apply a string operation on multiple columns? What if you want to convert multiple columns to datetime? These functions are vectorized for Series only, so they must be applied over each column that you want to convert/operate on.
df = pd.DataFrame(
pd.date_range('2018-12-31','2019-01-31', freq='2D').date.astype(str).reshape(-1, 2),
columns=['date1', 'date2'])
df
date1 date2
0 2018-12-31 2019-01-02
1 2019-01-04 2019-01-06
2 2019-01-08 2019-01-10
3 2019-01-12 2019-01-14
4 2019-01-16 2019-01-18
5 2019-01-20 2019-01-22
6 2019-01-24 2019-01-26
7 2019-01-28 2019-01-30
df.dtypes
date1 object
date2 object
dtype: object
This is an admissible case for apply:
df.apply(pd.to_datetime, errors='coerce').dtypes
date1 datetime64[ns]
date2 datetime64[ns]
dtype: object
Note that it would also make sense to stack, or just use an explicit loop. All these options are slightly faster than using apply, but the difference is small enough to forgive.
%timeit df.apply(pd.to_datetime, errors='coerce')
%timeit pd.to_datetime(df.stack(), errors='coerce').unstack()
%timeit pd.concat([pd.to_datetime(df[c], errors='coerce') for c in df], axis=1)
%timeit for c in df.columns: df[c] = pd.to_datetime(df[c], errors='coerce')
5.49 ms ± 247 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.94 ms ± 48.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.16 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.41 ms ± 1.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
You can make a similar case for other operations such as string operations, or conversion to category.
u = df.apply(lambda x: x.str.contains(...))
v = df.apply(lambda x: x.astype(category))
v/s
u = pd.concat([df[c].str.contains(...) for c in df], axis=1)
v = df.copy()
for c in df:
v[c] = df[c].astype(category)
And so on...
Converting Series to str: astype versus apply
This seems like an idiosyncrasy of the API. Using apply to convert integers in a Series to string is comparable (and sometimes faster) than using astype.
The graph was plotted using the perfplot library.
import perfplot
perfplot.show(
setup=lambda n: pd.Series(np.random.randint(0, n, n)),
kernels=[
lambda s: s.astype(str),
lambda s: s.apply(str)
],
labels=['astype', 'apply'],
n_range=[2**k for k in range(1, 20)],
xlabel='N',
logx=True,
logy=True,
equality_check=lambda x, y: (x == y).all())
With floats, I see the astype is consistently as fast as, or slightly faster than apply. So this has to do with the fact that the data in the test is integer type.
GroupBy operations with chained transformations
GroupBy.apply has not been discussed until now, but GroupBy.apply is also an iterative convenience function to handle anything that the existing GroupBy functions do not.
One common requirement is to perform a GroupBy and then two prime operations such as a "lagged cumsum":
df = pd.DataFrame({"A": list('aabcccddee'), "B": [12, 7, 5, 4, 5, 4, 3, 2, 1, 10]})
df
A B
0 a 12
1 a 7
2 b 5
3 c 4
4 c 5
5 c 4
6 d 3
7 d 2
8 e 1
9 e 10
<!- ->
You'd need two successive groupby calls here:
df.groupby('A').B.cumsum().groupby(df.A).shift()
0 NaN
1 12.0
2 NaN
3 NaN
4 4.0
5 9.0
6 NaN
7 3.0
8 NaN
9 1.0
Name: B, dtype: float64
Using apply, you can shorten this to a a single call.
df.groupby('A').B.apply(lambda x: x.cumsum().shift())
0 NaN
1 12.0
2 NaN
3 NaN
4 4.0
5 9.0
6 NaN
7 3.0
8 NaN
9 1.0
Name: B, dtype: float64
It is very hard to quantify the performance because it depends on the data. But in general, apply is an acceptable solution if the goal is to reduce a groupby call (because groupby is also quite expensive).
Other Caveats
Aside from the caveats mentioned above, it is also worth mentioning that apply operates on the first row (or column) twice. This is done to determine whether the function has any side effects. If not, apply may be able to use a fast-path for evaluating the result, else it falls back to a slow implementation.
df = pd.DataFrame({
'A': [1, 2],
'B': ['x', 'y']
})
def func(x):
print(x['A'])
return x
df.apply(func, axis=1)
# 1
# 1
# 2
A B
0 1 x
1 2 y
This behaviour is also seen in GroupBy.apply on pandas versions <0.25 (it was fixed for 0.25, see here for more information.)
Not all applys are alike
The below chart suggests when to consider apply1. Green means possibly efficient; red avoid.
Some of this is intuitive: pd.Series.apply is a Python-level row-wise loop, ditto pd.DataFrame.apply row-wise (axis=1). The misuses of these are many and wide-ranging. The other post deals with them in more depth. Popular solutions are to use vectorised methods, list comprehensions (assumes clean data), or efficient tools such as the pd.DataFrame constructor (e.g. to avoid apply(pd.Series)).
If you are using pd.DataFrame.apply row-wise, specifying raw=True (where possible) is often beneficial. At this stage, numba is usually a better choice.
GroupBy.apply: generally favoured
Repeating groupby operations to avoid apply will hurt performance. GroupBy.apply is usually fine here, provided the methods you use in your custom function are themselves vectorised. Sometimes there is no native Pandas method for a groupwise aggregation you wish to apply. In this case, for a small number of groups apply with a custom function may still offer reasonable performance.
pd.DataFrame.apply column-wise: a mixed bag
pd.DataFrame.apply column-wise (axis=0) is an interesting case. For a small number of rows versus a large number of columns, it's almost always expensive. For a large number of rows relative to columns, the more common case, you may sometimes see significant performance improvements using apply:
# Python 3.7, Pandas 0.23.4
np.random.seed(0)
df = pd.DataFrame(np.random.random((10**7, 3))) # Scenario_1, many rows
df = pd.DataFrame(np.random.random((10**4, 10**3))) # Scenario_2, many columns
# Scenario_1 | Scenario_2
%timeit df.sum() # 800 ms | 109 ms
%timeit df.apply(pd.Series.sum) # 568 ms | 325 ms
%timeit df.max() - df.min() # 1.63 s | 314 ms
%timeit df.apply(lambda x: x.max() - x.min()) # 838 ms | 473 ms
%timeit df.mean() # 108 ms | 94.4 ms
%timeit df.apply(pd.Series.mean) # 276 ms | 233 ms
1 There are exceptions, but these are usually marginal or uncommon. A couple of examples:
df['col'].apply(str) may slightly outperform df['col'].astype(str).
df.apply(pd.to_datetime) working on strings doesn't scale well with rows versus a regular for loop.
For axis=1 (i.e. row-wise functions) then you can just use the following function in lieu of apply. I wonder why this isn't the pandas behavior. (Untested with compound indexes, but it does appear to be much faster than apply)
def faster_df_apply(df, func):
cols = list(df.columns)
data, index = [], []
for row in df.itertuples(index=True):
row_dict = {f:v for f,v in zip(cols, row[1:])}
data.append(func(row_dict))
index.append(row[0])
return pd.Series(data, index=index)
Are there ever any situations where apply is good?
Yes, sometimes.
Task: decode Unicode strings.
import numpy as np
import pandas as pd
import unidecode
s = pd.Series(['mañana','Ceñía'])
s.head()
0 mañana
1 Ceñía
s.apply(unidecode.unidecode)
0 manana
1 Cenia
Update
I was by no means advocating for the use of apply, just thinking since the NumPy cannot deal with the above situation, it could have been a good candidate for pandas apply. But I was forgetting the plain ol list comprehension thanks to the reminder by #jpp.