Difference between map, applymap and apply methods in Pandas
Question:
Can you tell me when to use these vectorization methods with basic examples?
I see that map
is a Series
method whereas the rest are DataFrame
methods. I got confused about apply
and applymap
methods though. Why do we have two methods for applying a function to a DataFrame? Again, simple examples which illustrate the usage would be great!
Answers:
Straight from Wes McKinney’s Python for Data Analysis book, pg. 132 (I highly recommended this book):
Another frequent operation is applying a function on 1D arrays to each column or row. DataFrame’s apply method does exactly this:
In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [117]: frame
Out[117]:
b d e
Utah 0.029638 1.081563 1.280300
Ohio 0.647747 0.831136 1.549481
Texas 0.513416 0.884417 0.195343
Oregon 0.485454 0.477388 0.309548
In [118]: f = lambda x: x.max()  x.min()
In [119]: frame.apply(f)
Out[119]:
b 1.133201
d 1.965980
e 2.829781
dtype: float64
Many of the most common array statistics (like sum and mean) are DataFrame methods,
so using apply is not necessary.Elementwise Python functions can be used, too. Suppose you wanted to compute a formatted string from each floating point value in frame. You can do this with applymap:
In [120]: format = lambda x: '%.2f' % x
In [121]: frame.applymap(format)
Out[121]:
b d e
Utah 0.03 1.08 1.28
Ohio 0.65 0.83 1.55
Texas 0.51 0.88 0.20
Oregon 0.49 0.48 0.31
The reason for the name applymap is that Series has a map method for applying an elementwise function:
In [122]: frame['e'].map(format)
Out[122]:
Utah 1.28
Ohio 1.55
Texas 0.20
Oregon 0.31
Name: e, dtype: object
Summing up, apply
works on a row / column basis of a DataFrame, applymap
works elementwise on a DataFrame, and map
works elementwise on a Series.
@jeremiahbuddha mentioned that apply works on row/columns, while applymap works elementwise. But it seems you can still use apply for elementwise computation….
frame.apply(np.sqrt)
Out[102]:
b d e
Utah NaN 1.435159 NaN
Ohio 1.098164 0.510594 0.729748
Texas NaN 0.456436 0.697337
Oregon 0.359079 NaN NaN
frame.applymap(np.sqrt)
Out[103]:
b d e
Utah NaN 1.435159 NaN
Ohio 1.098164 0.510594 0.729748
Texas NaN 0.456436 0.697337
Oregon 0.359079 NaN NaN
Adding to the other answers, in a Series
there are also map and apply.
Apply can make a DataFrame out of a series; however, map will just put a series in every cell of another series, which is probably not what you want.
In [40]: p=pd.Series([1,2,3])
In [41]: p
Out[31]:
0 1
1 2
2 3
dtype: int64
In [42]: p.apply(lambda x: pd.Series([x, x]))
Out[42]:
0 1
0 1 1
1 2 2
2 3 3
In [43]: p.map(lambda x: pd.Series([x, x]))
Out[43]:
0 0 1
1 1
dtype: int64
1 0 2
1 2
dtype: int64
2 0 3
1 3
dtype: int64
dtype: object
Also if I had a function with side effects, such as “connect to a web server”, I’d probably use apply
just for the sake of clarity.
series.apply(download_file_for_every_element)
Map
can use not only a function, but also a dictionary or another series. Let’s say you want to manipulate permutations.
Take
1 2 3 4 5
2 1 4 5 3
The square of this permutation is
1 2 3 4 5
1 2 5 3 4
You can compute it using map
. Not sure if selfapplication is documented, but it works in 0.15.1
.
In [39]: p=pd.Series([1,0,3,4,2])
In [40]: p.map(p)
Out[40]:
0 0
1 1
2 4
3 2
4 3
dtype: int64
Just wanted to point out, as I struggled with this for a bit
def f(x):
if x < 0:
x = 0
elif x > 100000:
x = 100000
return x
df.applymap(f)
df.describe()
this does not modify the dataframe itself, has to be reassigned:
df = df.applymap(f)
df.describe()
Probably simplest explanation the difference between apply and applymap:
apply takes the whole column as a parameter and then assign the result to this column
applymap takes the separate cell value as a parameter and assign the result back to this cell.
NB If apply returns the single value you will have this value instead of the column after assigning and eventually will have just a row instead of matrix.
Quick Summary

DataFrame.apply
operates on entire rows or columns at a time. 
DataFrame.applymap
,Series.apply
, andSeries.map
operate on one
element at time.
Series.apply
and Series.map
are similar and often interchangeable. Some of their slight differences are discussed in osa’s answer below.
My understanding:
From the function point of view:
If the function has variables that need to compare within a column/ row, use
apply
.
e.g.: lambda x: x.max()x.mean()
.
If the function is to be applied to each element:
1> If a column/row is located, use apply
2> If apply to entire dataframe, use applymap
majority = lambda x : x > 17
df2['legal_drinker'] = df2['age'].apply(majority)
def times10(x):
if type(x) is int:
x *= 10
return x
df2.applymap(times10)
FOMO:
The following example shows apply
and applymap
applied to a DataFrame
.
map
function is something you do apply on Series only. You cannot apply map
on DataFrame.
The thing to remember is that apply
can do anything applymap
can, but apply
has eXtra options.
The X factor options are: axis
and result_type
where result_type
only works when axis=1
(for columns).
df = DataFrame(1, columns=list('abc'),
index=list('1234'))
print(df)
f = lambda x: np.log(x)
print(df.applymap(f)) # apply to the whole dataframe
print(np.log(df)) # applied to the whole dataframe
print(df.applymap(np.sum)) # reducing can be applied for rows only
# apply can take different options (vs. applymap cannot)
print(df.apply(f)) # same as applymap
print(df.apply(sum, axis=1)) # reducing example
print(df.apply(np.log, axis=1)) # cannot reduce
print(df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')) # expand result
As a sidenote, Series map
function, should not be confused with the Python map
function.
The first one is applied on Series, to map the values, and the second one to every item of an iterable.
Lastly don’t confuse the dataframe apply
method with groupby apply
method.
Comparing map
, applymap
and apply
: Context Matters
First major difference: DEFINITION
map
is defined on Series ONLYapplymap
is defined on DataFrames ONLYapply
is defined on BOTH
Second major difference: INPUT ARGUMENT
map
acceptsdict
s,Series
, or callableapplymap
andapply
accept callables only
Third major difference: BEHAVIOR
map
is elementwise for Seriesapplymap
is elementwise for DataFramesapply
also works elementwise but is suited to more complex operations and aggregation. The behaviour and return value depends on the function.
Fourth major difference (the most important one): USE CASE
map
is meant for mapping values from one domain to another, so is optimised for performance (e.g.,df['A'].map({1:'a', 2:'b', 3:'c'})
)applymap
is good for elementwise transformations across multiple rows/columns (e.g.,df[['A', 'B', 'C']].applymap(str.strip)
)apply
is for applying any function that cannot be vectorised (e.g.,df['sentences'].apply(nltk.sent_tokenize)
).
Also see When should I (not) want to use pandas apply() in my code? for a writeup I made a while back on the most appropriate scenarios for using apply
(note that there aren’t many, but there are a few— apply is generally slow).
Summarising
Footnotes
map
when passed a dictionary/Series will map elements based on the keys in that dictionary/Series. Missing values will be recorded as
NaN in the output.
applymap
in more recent versions has been optimised for some operations. You will findapplymap
slightly faster thanapply
in
some cases. My suggestion is to test them both and use whatever works
better.
map
is optimised for elementwise mappings and transformation. Operations that involve dictionaries or Series will enable pandas to
use faster code paths for better performance.
Series.apply
returns a scalar for aggregating operations, Series otherwise. Similarly forDataFrame.apply
. Note thatapply
also has
fastpaths when called with certain NumPy functions such asmean
,
sum
, etc.
Based on the answer of cs95
map
is defined on Series ONLYapplymap
is defined on DataFrames ONLYapply
is defined on BOTH
give some examples
In [3]: frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [4]: frame
Out[4]:
b d e
Utah 0.129885 0.475957 0.207679
Ohio 2.978331 1.015918 0.784675
Texas 0.256689 0.226366 2.262588
Oregon 2.605526 1.139105 0.927518
In [5]: myformat=lambda x: f'{x:.2f}'
In [6]: frame.d.map(myformat)
Out[6]:
Utah 0.48
Ohio 1.02
Texas 0.23
Oregon 1.14
Name: d, dtype: object
In [7]: frame.d.apply(myformat)
Out[7]:
Utah 0.48
Ohio 1.02
Texas 0.23
Oregon 1.14
Name: d, dtype: object
In [8]: frame.applymap(myformat)
Out[8]:
b d e
Utah 0.13 0.48 0.21
Ohio 2.98 1.02 0.78
Texas 0.26 0.23 2.26
Oregon 2.61 1.14 0.93
In [9]: frame.apply(lambda x: x.apply(myformat))
Out[9]:
b d e
Utah 0.13 0.48 0.21
Ohio 2.98 1.02 0.78
Texas 0.26 0.23 2.26
Oregon 2.61 1.14 0.93
In [10]: myfunc=lambda x: x**2
In [11]: frame.applymap(myfunc)
Out[11]:
b d e
Utah 0.016870 0.226535 0.043131
Ohio 8.870453 1.032089 0.615714
Texas 0.065889 0.051242 5.119305
Oregon 6.788766 1.297560 0.860289
In [12]: frame.apply(myfunc)
Out[12]:
b d e
Utah 0.016870 0.226535 0.043131
Ohio 8.870453 1.032089 0.615714
Texas 0.065889 0.051242 5.119305
Oregon 6.788766 1.297560 0.860289
Just for additional context and intuition, here’s an explicit and concrete example of the differences.
Assume you have the following function seen below. (
This label function, will arbitrarily split the values into ‘High’ and ‘Low’, based upon the threshold you provide as the parameter (x). )
def label(element, x):
if element > x:
return 'High'
else:
return 'Low'
In this example, lets assume our dataframe has one column with random numbers.
If you tried mapping the label function with map:
df['ColumnName'].map(label, x = 0.8)
You will result with the following error:
TypeError: map() got an unexpected keyword argument 'x'
Now take the same function and use apply, and you’ll see that it works:
df['ColumnName'].apply(label, x=0.8)
Series.apply() can take additional arguments elementwise, while the Series.map() method will return an error.
Now, if you’re trying to apply the same function to several columns in your dataframe simultaneously, DataFrame.applymap() is used.
df[['ColumnName','ColumnName2','ColumnName3','ColumnName4']].applymap(label)
Lastly, you can also use the apply() method on a dataframe, but the DataFrame.apply() method has different capabilities. Instead of applying functions elementwise, the df.apply() method applies functions along an axis, either columnwise or rowwise. When we create a function to use with df.apply(), we set it up to accept a series, most commonly a column.
Here is an example:
df.apply(pd.value_counts)
When we applied the pd.value_counts function to the dataframe, it calculated the value counts for all the columns.
Notice, and this is very important, when we used the df.apply() method to transform multiple columns. This is only possible because the pd.value_counts function operates on a series. If we tried to use the df.apply() method to apply a function that works elementwise to multiple columns, we’d get an error:
For example:
def label(element):
if element > 1:
return 'High'
else:
return 'Low'
df[['ColumnName','ColumnName2','ColumnName3','ColumnName4']].apply(label)
This will result with the following error:
ValueError: ('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', u'occurred at index Economy')
In general, we should only use the apply() method when a vectorized function does not exist. Recall that pandas uses vectorization, the process of applying operations to whole series at once, to optimize performance. When we use the apply() method, we’re actually looping through rows, so a vectorized method can perform an equivalent task faster than the apply() method.
Here are some examples of vectorized functions that already exist that you do NOT want to recreate using any type of apply/map methods:
 Series.str.split() Splits each element in the Series
 Series.str.strip() Strips whitespace from each string in the Series.
 Series.str.lower() Converts strings in the Series to lowercase.
 Series.str.upper() Converts strings in the Series to uppercase.
 Series.str.get() Retrieves the ith element of each element in the Series.
 Series.str.replace() Replaces a regex or string in the Series with another string
 Series.str.cat() Concatenates strings in a Series.
 Series.str.extract() Extracts substrings from the Series matching a regex pattern.