Reputation: 17934
I have a dictionary which looks like this: di = {1: "A", 2: "B"}
I would like to apply it to the col1
column of a dataframe similar to:
col1 col2
0 w a
1 1 2
2 2 NaN
to get:
col1 col2
0 w a
1 A 2
2 B NaN
How can I best do this?
Upvotes: 608
Views: 812919
Reputation: 23361
map
+fillna
for large di
and use replace
for small di
np.select()
If the remapping dictionary is not too large, another option is numpy.select
. The syntax of np.select
requires separate arrays/lists of conditions and replacement values, so the keys and values of di
should be separated.
import numpy as np
df['col1'] = np.select((df[['col1']].values == list(di)).T, di.values(), df['col1'])
N.B. If the remapping dictionary di
is very large, this may run into memory issues because as you can see from the line of code above, a boolean array of shape (len(df), len(di))
is required to evaluate the conditions.
map
+fillna
vs replace
. Which is better?If we look at the source code, if a dictionary is passed to it, map
is an optimized method that calls a Cython-optimized take_nd()
function to make replacements and fillna()
calls where()
(another optimized method) to fill values. On the other hand, replace()
is implemented in Python and uses a loop over the dictionary. So if the dictionary is large, replace
can potentially be thousands of times slower than map
+fillna
. Let's illustrate the difference by the following example where a single value (0
) is replaced in the column (one using a dictionary of length 1000 (di1
) and another using a dictionary of length 1 (di2
)).
df = pd.DataFrame({'col1': range(1000)})
di1 = {k: k+1 for k in range(-1000, 1)}
di2 = {0: 1}
%timeit df['col1'].map(di1).fillna(df['col1'])
# 1.19 ms ± 6.77 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
%timeit df['col1'].replace(di1)
# 41.4 ms ± 400 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit df['col1'].map(di2).fillna(df['col1'])
# 691 µs ± 27.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
%timeit df['col1'].replace(di2)
# 157 µs ± 3.34 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
As you can see, if len(di)==1000
, replace
is 35 times slower, but if len(di)==1
, it's 4.5 times faster. This gap gets worse as the size of the remapping dictionary di
increases.
In fact, if we look at the performance plots, we can make the following observations. The plots were drawn with particular parameters fixed in each graph. You can use the code down below to change the size of the dataframe to see for different parameters but it will produce very similar plots.
map
+fillna
makes replacements in almost constant time regardless of the size of the remapping dictionary whereas replace
does worse as the size of the remapping dictionary increases (top-left plot).di
completely trumps whatever impact it has (top-right plot).map
+fillna
performs better than replace
as the size of the dataframe increases (bottom-left plot).di
is large, the size of the dataframe does not matter; map
+fillna
is much faster than replace
(bottom-right plot).Code used to produce the plots:
import numpy as np
import pandas as pd
from perfplot import plot
import matplotlib.pyplot as plt
kernels = [lambda df,di: df['col1'].replace(di),
lambda df,di: df['col1'].map(di).fillna(df['col1'])]
labels = ["replace", "map+fillna"]
# first plot
N, m = 100000, 20
plot(
setup=lambda n: (pd.DataFrame({'col1': np.resize(np.arange(m*n), N)}),
{k: (k+1)/2 for k in range(n)}),
kernels=kernels, labels=labels,
n_range=range(1, 21),
xlabel='Length of replacement dictionary',
title=f'Remapping values in a column (len(df)={N:,}, {100//m}% replaced)',
equality_check=pd.Series.equals)
_, xmax = plt.xlim()
plt.xlim((0.5, xmax+1))
plt.xticks(np.arange(1, xmax+1, 2));
# second plot
N, m = 100000, 1000
di = {k: (k+1)/2 for k in range(m)}
plot(
setup=lambda n: pd.DataFrame({'col1': np.resize(np.arange((n-100)*m//100, n*m//100), N)}),
kernels=kernels, labels=labels,
n_range=[1, 5, 10, 15, 25, 40, 55, 75, 100],
xlabel='Percentage of values replaced',
title=f'Remapping values in a column (len(df)={N:,}, len(di)={m})',
equality_check=pd.Series.equals);
# third plot
m, n = 10, 0.01
di = {k: (k+1)/2 for k in range(m)}
plot(
setup=lambda N: pd.DataFrame({'col1': np.resize(np.arange((n-1)*m, n*m), N)}),
kernels=kernels, labels=labels,
n_range=[2**k for k in range(6, 21)],
xlabel='Length of dataframe',
logy=False,
title=f'Remapping values in a column (len(di)={m}, {int(n*100)}% replaced)',
equality_check=pd.Series.equals);
# fourth plot
m, n = 100, 0.01
di = {k: (k+1)/2 for k in range(m)}
plot(
setup=lambda N: pd.DataFrame({'col1': np.resize(np.arange((n-1)*m, n*m), N)}),
kernels=kernels, labels=labels,
n_range=[2**k for k in range(6, 21)],
xlabel='Length of dataframe',
title=f'Remapping values in a column (len(di)={m}, {int(n*100)}% replaced)',
equality_check=pd.Series.equals);
Upvotes: 8
Reputation: 17902
You can update your mapping dictionary with missing pairs from the dataframe. For example:
df = pd.DataFrame({'col1': ['a', 'b', 'c', 'd', np.nan]})
map_ = {'a': 'A', 'b': 'B', 'd': np.nan}
# Get mapping from df
uniques = df['col1'].unique()
map_new = dict(zip(uniques, uniques))
# {'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', nan: nan}
# Update mapping
map_new.update(map_)
# {'a': 'A', 'b': 'B', 'c': 'c', 'd': nan, nan: nan}
df['col2'] = df['col1'].map(dct_map_new)
Result:
col1 col2
0 a A
1 b B
2 c c
3 d NaN
4 NaN NaN
Upvotes: 4
Reputation: 59579
Given map
is faster than replace (@JohnE's solution) you need to be careful with Non-Exhaustive mappings where you intend to map specific values to NaN
. The proper method in this case requires that you mask
the Series when you .fillna
, else you undo the mapping to NaN
.
import pandas as pd
import numpy as np
d = {'m': 'Male', 'f': 'Female', 'missing': np.NaN}
df = pd.DataFrame({'gender': ['m', 'f', 'missing', 'Male', 'U']})
keep_nan = [k for k,v in d.items() if pd.isnull(v)]
s = df['gender']
df['mapped'] = s.map(d).fillna(s.mask(s.isin(keep_nan)))
gender mapped
0 m Male
1 f Female
2 missing NaN
3 Male Male
4 U U
Upvotes: 9
Reputation: 175
As an extension to what have been proposed by Nico Coallier (apply to multiple columns) and U10-Forward(using apply style of methods), and summarising it into a one-liner I propose:
df.loc[:,['col1','col2']].transform(lambda x: x.map(lambda x: {1: "A", 2: "B"}.get(x,x))
The .transform()
processes each column as a series. Contrary to .apply()
which passes the columns aggregated in a DataFrame.
Consequently you can apply the Series method map()
.
Finally, and I discovered this behaviour thanks to U10, you can use the whole Series in the .get() expression. Unless I have misunderstood its behaviour and it processes sequentially the series instead of bitwisely.
The .get(x,x)
accounts for the values you did not mention in your mapping dictionary which would be considered as Nan otherwise by the .map()
method
Upvotes: 1
Reputation: 5407
A nice complete solution that keeps a map of your class labels:
labels = features['col1'].unique()
labels_dict = dict(zip(labels, range(len(labels))))
features = features.replace({"col1": labels_dict})
This way, you can at any point refer to the original class label from labels_dict.
Upvotes: 2
Reputation: 30444
map
can be much faster than replace
If your dictionary has more than a couple of keys, using map
can be much faster than replace
. There are two versions of this approach, depending on whether your dictionary exhaustively maps all possible values (and also whether you want non-matches to keep their values or be converted to NaNs):
In this case, the form is very simple:
df['col1'].map(di) # note: if the dictionary does not exhaustively map all
# entries then non-matched entries are changed to NaNs
Although map
most commonly takes a function as its argument, it can alternatively take a dictionary or series: Documentation for Pandas.series.map
If you have a non-exhaustive mapping and wish to retain the existing variables for non-matches, you can add fillna
:
df['col1'].map(di).fillna(df['col1'])
as in @jpp's answer here: Replace values in a pandas series via dictionary efficiently
Using the following data with pandas version 0.23.1:
di = {1: "A", 2: "B", 3: "C", 4: "D", 5: "E", 6: "F", 7: "G", 8: "H" }
df = pd.DataFrame({ 'col1': np.random.choice( range(1,9), 100000 ) })
and testing with %timeit
, it appears that map
is approximately 10x faster than replace
.
Note that your speedup with map
will vary with your data. The largest speedup appears to be with large dictionaries and exhaustive replaces. See @jpp answer (linked above) for more extensive benchmarks and discussion.
Upvotes: 590
Reputation: 353459
You can use .replace
. For example:
>>> df = pd.DataFrame({'col2': {0: 'a', 1: 2, 2: np.nan}, 'col1': {0: 'w', 1: 1, 2: 2}})
>>> di = {1: "A", 2: "B"}
>>> df
col1 col2
0 w a
1 1 2
2 2 NaN
>>> df.replace({"col1": di})
col1 col2
0 w a
1 A 2
2 B NaN
or directly on the Series
, i.e. df["col1"].replace(di, inplace=True)
.
Upvotes: 622
Reputation: 71610
Or do apply
:
df['col1'].apply(lambda x: {1: "A", 2: "B"}.get(x,x))
Demo:
>>> df['col1']=df['col1'].apply(lambda x: {1: "A", 2: "B"}.get(x,x))
>>> df
col1 col2
0 w a
1 1 2
2 2 NaN
>>>
Upvotes: 3
Reputation: 15857
DSM has the accepted answer, but the coding doesn't seem to work for everyone. Here is one that works with the current version of pandas (0.23.4 as of 8/2018):
import pandas as pd
df = pd.DataFrame({'col1': [1, 2, 2, 3, 1],
'col2': ['negative', 'positive', 'neutral', 'neutral', 'positive']})
conversion_dict = {'negative': -1, 'neutral': 0, 'positive': 1}
df['converted_column'] = df['col2'].replace(conversion_dict)
print(df.head())
You'll see it looks like:
col1 col2 converted_column
0 1 negative -1
1 2 positive 1
2 2 neutral 0
3 3 neutral 0
4 1 positive 1
The docs for pandas.DataFrame.replace are here.
Upvotes: 14
Reputation: 3235
A more native pandas approach is to apply a replace function as below:
def multiple_replace(dict, text):
# Create a regular expression from the dictionary keys
regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys())))
# For each match, look-up corresponding value in dictionary
return regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], text)
Once you defined the function, you can apply it to your dataframe.
di = {1: "A", 2: "B"}
df['col1'] = df.apply(lambda row: multiple_replace(di, row['col1']), axis=1)
Upvotes: -1
Reputation: 686
Adding to this question if you ever have more than one columns to remap in a data dataframe:
def remap(data,dict_labels):
"""
This function take in a dictionnary of labels : dict_labels
and replace the values (previously labelencode) into the string.
ex: dict_labels = {{'col1':{1:'A',2:'B'}}
"""
for field,values in dict_labels.items():
print("I am remapping %s"%field)
data.replace({field:values},inplace=True)
print("DONE")
return data
Hope it can be useful to someone.
Cheers
Upvotes: 3
Reputation: 880667
There is a bit of ambiguity in your question. There are at least three two interpretations:
di
refer to index valuesdi
refer to df['col1']
valuesdi
refer to index locations (not the OP's question, but thrown in for fun.)Below is a solution for each case.
Case 1:
If the keys of di
are meant to refer to index values, then you could use the update
method:
df['col1'].update(pd.Series(di))
For example,
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1':['w', 10, 20],
'col2': ['a', 30, np.nan]},
index=[1,2,0])
# col1 col2
# 1 w a
# 2 10 30
# 0 20 NaN
di = {0: "A", 2: "B"}
# The value at the 0-index is mapped to 'A', the value at the 2-index is mapped to 'B'
df['col1'].update(pd.Series(di))
print(df)
yields
col1 col2
1 w a
2 B 30
0 A NaN
I've modified the values from your original post so it is clearer what update
is doing.
Note how the keys in di
are associated with index values. The order of the index values -- that is, the index locations -- does not matter.
Case 2:
If the keys in di
refer to df['col1']
values, then @DanAllan and @DSM show how to achieve this with replace
:
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1':['w', 10, 20],
'col2': ['a', 30, np.nan]},
index=[1,2,0])
print(df)
# col1 col2
# 1 w a
# 2 10 30
# 0 20 NaN
di = {10: "A", 20: "B"}
# The values 10 and 20 are replaced by 'A' and 'B'
df['col1'].replace(di, inplace=True)
print(df)
yields
col1 col2
1 w a
2 A 30
0 B NaN
Note how in this case the keys in di
were changed to match values in df['col1']
.
Case 3:
If the keys in di
refer to index locations, then you could use
df['col1'].put(di.keys(), di.values())
since
df = pd.DataFrame({'col1':['w', 10, 20],
'col2': ['a', 30, np.nan]},
index=[1,2,0])
di = {0: "A", 2: "B"}
# The values at the 0 and 2 index locations are replaced by 'A' and 'B'
df['col1'].put(di.keys(), di.values())
print(df)
yields
col1 col2
1 A a
2 10 30
0 B NaN
Here, the first and third rows were altered, because the keys in di
are 0
and 2
, which with Python's 0-based indexing refer to the first and third locations.
Upvotes: 92