While working with data in Pandas, we often need to modify or transform values in specific columns. One common transformation is remapping values using a dictionary. This technique is useful when we need to replace categorical values with labels, abbreviations or numerical representations. In this article, we’ll explore different ways to remap values in a Pandas DataFrame using dictionary mapping.

Creating a sample pandas dataframe

Let’s start by creating a sample DataFrame that contains event details.

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'],
                   'Cost': [10000, 5000, 15000, 2000]})

print(df)

        Date    Event   Cost
0  10/2/2011    Music  10000
1  11/2/2011   Poetry   5000
2  12/2/2011  Theatre  15000
3  13/2/2011   Comedy   2000

Using replace() function

The replace() function in Pandas allows us to remap values using a dictionary. It works directly on a DataFrame column and modifies the values based on the provided mapping.

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'],
                   'Cost': [10000, 5000, 15000, 2000]})


# Define a dictionary for remapping values
d = {'Music': 'M', 'Poetry': 'P', 'Theatre': 'T', 'Comedy': 'C'}

# Remap the values using replace()
df['Event'] = df['Event'].replace(d)

print(df)

        Date Event   Cost
0  10/2/2011     M  10000
1  11/2/2011     P   5000
2  12/2/2011     T  15000
3  13/2/2011     C   2000

Explanation:

• replace() searches for values in the column and replaces them based on the dictionary.
• This method works on DataFrame columns and can handle multiple column replacements.

Using map() function 

Another way to remap values in a Pandas column is by using the map() function.

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'],
                   'Cost': [10000, 5000, 15000, 2000]})

# Define a dictionary for remapping values
d = {'Music': 'M', 'Poetry': 'P', 'Theatre': 'T', 'Comedy': 'C'}

# Remap the values using map()
df['Event'] = df['Event'].map(d)
print(df)

        Date Event   Cost
0  10/2/2011     M  10000
1  11/2/2011     P   5000
2  12/2/2011     T  15000
3  13/2/2011     C   2000

Explanation:

• map() applies the dictionary mapping to each element in the column.
• Unlike replace(), map() only works on Series (single column).

Differences between replace() and map()

Handling missing values during mapping

If the dictionary used in map() does not contain a key for some values in the column, those values will be replaced with NaN. To handle this, we can use the fillna() function.

import pandas as pd

df = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                   'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'],
                   'Cost': [10000, 5000, 15000, 2000]})

# Define an incomplete dictionary
d = {'Music': 'M', 'Poetry': 'P'}

# Apply map() and handle missing values
df['Event'] = df['Event'].map(d).fillna('Unknown')

print(df)

        Date    Event   Cost
0  10/2/2011        M  10000
1  11/2/2011        P   5000
2  12/2/2011  Unknown  15000
3  13/2/2011  Unknown   2000

Explanation:

• map() function replaces only the values present in the dictionary.
• Missing values (i.e., Theatre and Comedy) become NaN.
• fillna(‘Unknown’) ensures that missing values are replaced with “Unknown” instead of NaN.