Better way to replace values in DataFrame from large dictionary

Question

I have written some code that replaces values in a DataFrame with values from another frame using a dictionary, and it is working, but i am using this on some large files, where the dictionary can get very long. A few thousand pairs. When I then uses this code it runs very slow, and it have also been going out of memory on a few ocations.

I am somewhat convinced that my method of doing this is far from optimal, and that there must be some faster ways to do this. I have created a simple example that does what I want, but that is slow for large amounts of data. Hope someone have a simpler way to do this.

import pandas as pd

#Frame with data where I want to replace the 'id' with the name from df2
df1 = pd.DataFrame({'id' : [1, 2, 3, 4, 5, 3, 5, 9], 'values' : [12, 32, 42,    51, 23, 14, 111, 134]})

#Frame containing names linked to ids
df2 = pd.DataFrame({'id' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'name' : ['id1',   'id2', 'id3', 'id4', 'id5', 'id6', 'id7', 'id8', 'id9', 'id10']})

#My current "slow" way of doing this.

#Starts by creating a dictionary from df2
#Need to create dictionaries from the domain and banners tables to link ids
df2_dict = dict(zip(df2['id'], df2['name']))

#and then uses the dict to replace the ids with name in df1
df1.replace({'id' : df2_dict}, inplace=True)

Answer 1

I think you can use map with Series converted to_dict - get NaN if not exist value in df2:

df1['id'] = df1.id.map(df2.set_index('id')['name'].to_dict())
print (df1)
    id  values
0  id1      12
1  id2      32
2  id3      42
3  id4      51
4  id5      23
5  id3      14
6  id5     111
7  id9     134

Or replace, if dont exist value in df2 let original values from df1:

df1['id'] = df1.id.replace(df2.set_index('id')['name'])
print (df1)
    id  values
0  id1      12
1  id2      32
2  id3      42
3  id4      51
4  id5      23
5  id3      14
6  id5     111
7  id9     134

Sample:

#Frame with data where I want to replace the 'id' with the name from df2
df1 = pd.DataFrame({'id' : [1, 2, 3, 4, 5, 3, 5, 9], 'values' : [12, 32, 42,    51, 23, 14, 111, 134]})
print (df1)
#Frame containing names linked to ids
df2 = pd.DataFrame({'id' : [1, 2, 3, 4, 6, 7, 8, 9, 10], 'name' : ['id1',   'id2', 'id3', 'id4', 'id6', 'id7', 'id8', 'id9', 'id10']})
print (df2)

df1['new_map'] = df1.id.map(df2.set_index('id')['name'].to_dict())
df1['new_replace'] = df1.id.replace(df2.set_index('id')['name'])
print (df1)
   id  values new_map new_replace
0   1      12     id1         id1
1   2      32     id2         id2
2   3      42     id3         id3
3   4      51     id4         id4
4   5      23     NaN           5
5   3      14     id3         id3
6   5     111     NaN           5
7   9     134     id9         id9