Pandas: how to merge two dataframes on a column by keeping the information of the first one?

Sample:

df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'], 
                    'Age': [34, 18, 44, 27, 30]})

#print (df1)
df3 = df1.copy()

df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Jack', 'Michelle'], 
                    'Sex': ['M', 'M', 'F', 'M', 'F']})
#print (df2)

Use map by Series created by set_index:

df1['Sex'] = df1['Name'].map(df2.set_index('Name')['Sex'])
print (df1)
    Name  Age  Sex
0    Tom   34    M
1   Sara   18  NaN
2    Eva   44    F
3   Jack   27    M
4  Laura   30  NaN

Alternative solution with merge with left join:

df = df3.merge(df2[['Name','Sex']], on='Name', how='left')
print (df)
    Name  Age  Sex
0    Tom   34    M
1   Sara   18  NaN
2    Eva   44    F
3   Jack   27    M
4  Laura   30  NaN

If need map by multiple columns (e.g. Year and Code) need merge with left join:

df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'], 
                    'Year':[2000,2003,2003,2004,2007],
                    'Code':[1,2,3,4,4],
                    'Age': [34, 18, 44, 27, 30]})

print (df1)
    Name  Year  Code  Age
0    Tom  2000     1   34
1   Sara  2003     2   18
2    Eva  2003     3   44
3   Jack  2004     4   27
4  Laura  2007     4   30

df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Jack', 'Michelle'], 
                    'Sex': ['M', 'M', 'F', 'M', 'F'],
                    'Year':[2001,2003,2003,2004,2007],
                    'Code':[1,2,3,5,3],
                    'Val':[21,34,23,44,67]})
print (df2)
       Name Sex  Year  Code  Val
0       Tom   M  2001     1   21
1      Paul   M  2003     2   34
2       Eva   F  2003     3   23
3      Jack   M  2004     5   44
4  Michelle   F  2007     3   67

#merge by all columns
df = df1.merge(df2, on=['Year','Code'], how='left')
print (df)
  Name_x  Year  Code  Age Name_y  Sex   Val
0    Tom  2000     1   34    NaN  NaN   NaN
1   Sara  2003     2   18   Paul    M  34.0
2    Eva  2003     3   44    Eva    F  23.0
3   Jack  2004     4   27    NaN  NaN   NaN
4  Laura  2007     4   30    NaN  NaN   NaN

#specified columns - columns for join (Year, Code) need always + appended columns (Val)
df = df1.merge(df2[['Year','Code', 'Val']], on=['Year','Code'], how='left')
print (df)
    Name  Year  Code  Age   Val
0    Tom  2000     1   34   NaN
1   Sara  2003     2   18  34.0
2    Eva  2003     3   44  23.0
3   Jack  2004     4   27   NaN
4  Laura  2007     4   30   NaN

If get error with map it means duplicates by columns of join, here Name:

df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'], 
                    'Age': [34, 18, 44, 27, 30]})

print (df1)
    Name  Age
0    Tom   34
1   Sara   18
2    Eva   44
3   Jack   27
4  Laura   30

df3, df4 = df1.copy(), df1.copy()

df2 = pd.DataFrame({'Name': ['Tom', 'Tom', 'Eva', 'Jack', 'Michelle'], 
                    'Val': [1,2,3,4,5]})
print (df2)
       Name  Val
0       Tom    1 <-duplicated name Tom
1       Tom    2 <-duplicated name Tom
2       Eva    3
3      Jack    4
4  Michelle    5

s = df2.set_index('Name')['Val']
df1['New'] = df1['Name'].map(s)
print (df1)

InvalidIndexError: Reindexing only valid with uniquely valued Index objects

Solutions are removed duplicates by DataFrame.drop_duplicates, or use map by dict for last dupe match:

#default keep first value
s = df2.drop_duplicates('Name').set_index('Name')['Val']
print (s)
Name
Tom         1
Eva         3
Jack        4
Michelle    5
Name: Val, dtype: int64

df1['New'] = df1['Name'].map(s)
print (df1)
    Name  Age  New
0    Tom   34  1.0
1   Sara   18  NaN
2    Eva   44  3.0
3   Jack   27  4.0
4  Laura   30  NaN

#add parameter for keep last value 
s = df2.drop_duplicates('Name', keep='last').set_index('Name')['Val']
print (s)
Name
Tom         2
Eva         3
Jack        4
Michelle    5
Name: Val, dtype: int64

df3['New'] = df3['Name'].map(s)
print (df3)
    Name  Age  New
0    Tom   34  2.0
1   Sara   18  NaN
2    Eva   44  3.0
3   Jack   27  4.0
4  Laura   30  NaN

#map by dictionary
d = dict(zip(df2['Name'], df2['Val']))
print (d)
{'Tom': 2, 'Eva': 3, 'Jack': 4, 'Michelle': 5}

df4['New'] = df4['Name'].map(d)
print (df4)
    Name  Age  New
0    Tom   34  2.0
1   Sara   18  NaN
2    Eva   44  3.0
3   Jack   27  4.0
4  Laura   30  NaN

You can also use the join method:

df1.set_index("Name").join(df2.set_index("Name"), how="left")

edit: added set_index("Name")

Simple addition to @jezrael answer for creating dictionary from dataframe.

It may be helpful..

Python:

df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'],
                    'Age': [34, 18, 44, 27, 30]})


df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Paul', 'Jack', 'Michelle', 'Tom'],
                    'Something': ['M', 'M', 'F', 'M', 'A', 'F', 'B']})


df1_dict = pd.Series(df1.Age.values, index=df1.Name).to_dict()

df2['Age'] = df2['Name'].map(df1_dict)

print(df2)

Output:

      Name Something   Age
0       Tom         M  34.0
1      Paul         M   NaN
2       Eva         F  44.0
3      Paul         M   NaN
4      Jack         A  27.0
5  Michelle         F   NaN
6       Tom         B  34.0

Reindexing hasn't been mentioned yet, but it's very fast and can auto-fill missing values if desired.

DataFrame.reindex

Use the common key (Name) as the index of the mapping dataframe (df2):

• If df2's index is already Name, just reindex directly:

  df2['Sex'].reindex(df1['Name'])
  

• Otherwise set_index beforehand:

  df2.set_index('Name')['Sex'].reindex(df1['Name'])
  

Note that when assigning into an existing dataframe, the reindexed index will be misaligned, so assign only the array values:

df1['Sex'] = df2.set_index('Name')['Sex'].reindex(df1['Name']).array

#     Name  Age  Sex
# 0    Tom   34    M
# 1   Sara   18  NaN
# 2    Eva   44    F
# 3   Jack   27    M
# 4  Laura   30  NaN

Also I've noticed a common assumption that reindexing is slow, but it's actually fast(est):

To fill missing values

reindex supports auto-filling missing values:

• fill_value: static replacement
• method: algorithmic replacement (ffill, bfill, or nearest) given monotonic index

For example, to fill empty Sex values with Prefer not to say (PNS):

df2.set_index('Name')['Sex'].reindex(df1['Name'], fill_value='PNS')

#     Name  Age  Sex
# 0    Tom   34    M
# 1   Sara   18  PNS
# 2    Eva   44    F
# 3   Jack   27    M
# 4  Laura   30  PNS

Reindexing with fill_value is faster than chaining fillna:

To handle duplicates

The mapping dataframe (df2) cannot have duplicate keys, so drop_duplicates if applicable:

df2.drop_duplicates('Name').set_index('Name')['Sex'].reindex(df1['Name'])

_{Timing data:}

'''
Note: This is python code in a js snippet, so "run code snippet" will not work.
The snippet is just to avoid cluttering the main post with supplemental code.
'''

df1 = pd.DataFrame({'Name': np.arange(n), 'Age': np.random.randint(100, size=n)}).sample(frac=1).reset_index(drop=True)
df2 = pd.DataFrame({'Name': np.arange(n) + int(n * 0.5), 'Sex': np.random.choice(list('MF'), size=n)}).sample(frac=1).reset_index(drop=True)

def reindex_(df1, df2):
    df1['Sex'] = df2.set_index('Name')['Sex'].reindex(df1['Name']).array
    return df1

def map_(df1, df2):
    df1['Sex'] = df1['Name'].map(df2.set_index('Name')['Sex'])
    return df1

def dict_(df1, df2):
    df1['Sex'] = df1['Name'].map(dict(zip(df2['Name'], df2['Sex'])))
    return df1

def merge_(df1, df2):
    return df1.merge(df2[['Name', 'Sex']], left_on='Name', right_on='Name', how='left')

def join_(df1, df2):
    return df1.set_index('Name').join(df2.set_index('Name'), how='left').reset_index()

reindex_fill_value_ = lambda df1, df2: df2.set_index('Name')['Sex'].reindex(df1['Name'], fill_value='PNTS')
reindex_fillna_ = lambda df1, df2: df2.set_index('Name')['Sex'].reindex(df1['Name']).fillna('PNTS')
map_fillna_ = lambda df1, df2: df1['Name'].map(df2.set_index('Name')['Sex']).fillna('PNTS')