CODEWITHSUNDEEP
pythonpandasdataframeindexing
A. Martin
A. Martin

Reputation: 51

pandas dataframe index match

I'm wondering if there is a more efficient way to do an "index & match" type function that is popular in excel. For example - given two pandas DataFrames, update the df_1 with information found in df_2:

import pandas as pd

df_1 = pd.DataFrame({'num_a':[1, 2, 3, 4, 5],
                     'num_b':[2, 4, 1, 2, 3]})    
df_2 = pd.DataFrame({'num':[1, 2, 3, 4, 5],
                     'name':['a', 'b', 'c', 'd', 'e']})

I'm working with data sets that have ~80,000 rows in both df_1 and df_2 and my goal is to create two new columns in df_1, "name_a" and "name_b".

Below is the most efficient method that I could come up with. There has to be a better way!

name_a = []
name_b = []
for i in range(len(df_1)):

    name_a.append(df_2.name.iloc[df_2[
                  df_2.num == df_1.num_a.iloc[i]].index[0]])
    name_b.append(df_2.name.iloc[df_2[
                  df_2.num == df_1.num_b.iloc[i]].index[0]])

df_1['name_a'] = name_a
df_1['name_b'] = name_b

Resulting in:

>>> df_1.head()
   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      e      c

Upvotes: 5

Views: 19094

Answers (3)

T. Ray
T. Ray

Reputation: 641

I think there's a more straightforward solution than those already offered. Since you mentioned Excel, this is a basic vlookup. You can simulate this in pandas by using Series.map.

name_map = dict(df_2.set_index('num').name)

df_1['name_a'] = df_1.num_a.map(name_map)
df_1['name_b'] = df_1.num_b.map(name_map)

df_1

   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      e      c

All we do is convert df_2 to a dict with 'num' as the keys. The map function looks up each value from a df_1 column in the dict and returns the corresponding letter. No complicated indexing required.

Upvotes: 3

piRSquared
piRSquared

Reputation: 294488

High Level

  • Create a dictionary to use in a replace
  • replace, rename columns, and join
m = dict(zip(
    df_2.num.values.tolist(),
    df_2.name.values.tolist()
))

df_1.join(
    df_1.replace(m).rename(
        columns=lambda x: x.replace('num', 'name')
    )
)

   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      5      c

Breakdown

replace with a dictionary should be pretty quick. There are bunch of ways to build a dictionary form df_2. As a matter of fact we could have used a pd.Series. I chose to build with dict and zip because I find that it's faster.

Building m

Option 1 

m = df_2.set_index('num').name

Option 2 

m = df_2.set_index('num').name.to_dict()

Option 3 

m = dict(zip(df_2.num, df_2.name))

Option 4 (My Choice) 

m = dict(zip(df_2.num.values.tolist(), df_2.name.values.tolist()))

m build times 

1000 loops, best of 3: 325 µs per loop
1000 loops, best of 3: 376 µs per loop
10000 loops, best of 3: 32.9 µs per loop
100000 loops, best of 3: 10.4 µs per loop

%timeit df_2.set_index('num').name
%timeit df_2.set_index('num').name.to_dict()
%timeit dict(zip(df_2.num, df_2.name))
%timeit dict(zip(df_2.num.values.tolist(), df_2.name.values.tolist()))

Replacing num

Again, we have choices, here are a few and their times.

%timeit df_1.replace(m)
%timeit df_1.applymap(lambda x: m.get(x, x))
%timeit df_1.stack().map(lambda x: m.get(x, x)).unstack()

1000 loops, best of 3: 792 µs per loop
1000 loops, best of 3: 959 µs per loop
1000 loops, best of 3: 925 µs per loop

I choose...

df_1.replace(m)

  num_a num_b
0     a     b
1     b     d
2     c     a
3     d     b
4     5     c

Rename columns 

df_1.replace(m).rename(columns=lambda x: x.replace('num', 'name'))

  name_a name_b   <-- note the column name change
0      a      b
1      b      d
2      c      a
3      d      b
4      5      c

Join 

df_1.join(df_1.replace(m).rename(columns=lambda x: x.replace('num', 'name')))

   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      5      c

Upvotes: 5

David Jaimes
David Jaimes

Reputation: 341

Just try a conditional statement:

import pandas as pd
import numpy as np
df_1 = pd.DataFrame({'num_a':[1, 2, 3, 4, 5],
                     'num_b':[2, 4, 1, 2, 3]})    
df_2 = pd.DataFrame({'num':[1, 2, 3, 4, 5],
                     'name':['a', 'b', 'c', 'd', 'e']})
df_1["name_a"] = df_2["num_b"]
df_1["name_b"] = np.array(df_1["name_a"][df_1["num_b"]-1]) 
print(df_1)

   num_a  num_b name_a name_b
0      1      2      a      b
1      2      4      b      d
2      3      1      c      a
3      4      2      d      b
4      5      3      e      c

Upvotes: 0

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