Pandas Replace Multiple Values in Python

Replacing multiple values in a Pandas DataFrame or Series is a common operation in data manipulation tasks. Pandas provides several versatile methods for achieving this, allowing you to seamlessly replace specific values with desired alternatives. In this context, we will explore various approaches to replace multiple values in Python using Pandas.

Replacing Multiple Values in Pandas

There are times when working with data in a Pandas DataFrame or Series that call for replacing particular values in order to improve data analysis or consistency. Pandas provides a number of methods for changing values in a column of a dataframe, offering versatility and effectiveness in managing a range of data replacement requirements.

Ways to Replace Multiple Values in Python Using Pandas are:

1. Using the replace() Method
2. Using map() method for single column
3. Using apply() method

Using the Replace Method

One of Pandas most useful tools is the replace() method, which allows to substitute desired values with specified ones. It is a flexible option for a range of situations because it may be applied to a single column or the full DataFrame.

In the code, we will replaces specific values (2 with 200 and 4 with 400) in column ‘A’ using the replace method.

Python

import pandas as pd

data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
print("Before Replacing:\n", df)

df.replace({2: 200, 4: 400}, inplace=True)
print("After Replacing:\n", df)

Output:

Before Replacing:
    A   B
0  1  10
1  2  20
2  3  30
3  4  40
4  5  50
After Replacing:
      A   B
0    1  10
1  200  20
2    3  30
3  400  40
4    5  50

We can observe that the value ‘2’ in column “A” is replaced by ‘200’ and the value ‘4’ in column “A” is replaced by ‘400’. Since, ‘inplace=True’, the changes are made directly in the DataFrame ‘df’. The other values and the structure of the DataFrame remain unchanged.

Using the Map Method for Single Column

Values in a particular column can be changed using the map() method in conjunction with a dictionary. For targeted replacements inside a single series, this method works well.

Using the map method for a dictionary (replacement_dict) to replace values in column ‘A’ (1 with ‘one’, 2 with ‘two’, and 3 with ‘three’

Python

import pandas as pd

data = {'A': [1, 2, 3]}
df = pd.DataFrame(data)
print("Before Replacing:\n", df)

df['A'] = df['A'].map({1: 'one', 2: 'two', 3: 'three'}) 
print("After Replacing:\n", df)

Output:

Before Replacing:
    A
0  1
1  2
2  3
After Replacing:
        A
0    one
1    two
2  three

Using apply Method

In Pandas, you can use the apply() method along with a custom function to replace multiple values in a DataFrame or Series.

In this example, the replace_values function is applied to each element in the DataFrame using the apply() method. The function checks for specific values (‘apple’ and ‘banana’ in this case) and replaces them accordingly.

Python

import pandas as pd

data = {'A': ['apple', 'potato', 'orange']}
df = pd.DataFrame(data)
print("Before Replacing:\n", df)

def replace_values(x):
    if x == 'apple' or x == 'orange':
        return 'fruit'
    elif x == 'potato':
        return 'vegetable'
    else:
        return x
df['A'] = df['A'].apply(replace_values)
print("After Replacing:\n", df)

Output:

Before Replacing
         A
0   apple
1  potato
2  orange
After Replacing
            A
0      fruit
1  vegetable
2      fruit

Conclusion

In conclusion, Pandas offers a robust set of methods for replacing multiple values in Python, catering to various data manipulation scenarios. The replace method stands out as a versatile and straightforward choice, allowing for global or column-specific substitutions effortlessly.