6 Powerful Pandas Tricks to Replace Numeric Codes with Text

Preface

Hello, I am a Python enthusiast. Recently a fan asked how to replace a column of numeric codes (1, 2, 3, 4) with corresponding text values in Pandas, so I am sharing several solutions.

Solution Process

The data frame is created with the following code:

df = pd.DataFrame({'col1': [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]})
df

Method 1: Mapping with a Dictionary

Use map to assign new values:

df['col2'] = df['col1'].map({1:"开心", 2:"悲伤", 3:"难过", 4:"泪目"})
df

Result:

Method 2: Custom Function with apply

Define a function that returns the appropriate string and apply it:

def getValue(s):
    if s == 1:
        return '开心'
    elif s == 2:
        return '悲伤'
    elif s == 3:
        return '难过'
    elif s == 4:
        return '泪目'

df['col3'] = df['col1'].apply(getValue)
df

Result:

Method 3: Using replace Directly

Chain replace calls for each value:

df['col4'] = df['col1'].replace(1, '开心').replace(2, '悲伤').replace(3, '难过').replace(4, '泪目')
df

Result:

Method 4: Mapping via apply with a Dictionary

def get_value(s):
    dict = {1:"开心", 2:"悲伤", 3:"难过", 4:"泪目"}
    return dict[s]

df['col5'] = df['col1'].apply(get_value)
df

Result:

Method 5: Mapping with map and a Helper Function

def get_value(s):
    dict = {1:"开心", 2:"悲伤", 3:"难过", 4:"泪目"}
    return dict[s]

df['col5'] = df['col1'].map(get_value)
df

Result:

Method 6: Vectorized replace with Lists

df['col7'] = df['col1'].replace([1, 2, 3, 4], ['开心', '悲伤', '难过', '泪目'])
df

Result:

Conclusion

This article collected six Pandas approaches—mapping, apply, replace, and map —to batch‑replace numeric codes with meaningful strings, providing code snippets and visual output to help readers solve the problem efficiently.