6 Powerful Pandas Tricks to Replace Categorical Codes with Text

Introduction

In response to a fan’s question about converting a column of numeric codes (1, 2, 3, 4) into corresponding Chinese words, this article presents six Pandas solutions.

Method 1 – Mapping with a Dictionary

Using map to replace values:

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

Method 2 – Custom Function with apply

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

Method 3 – Using replace

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

Method 4 – Dictionary Lookup with apply

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

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

Method 5 – Mapping with a Function and map

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

Method 6 – Bulk Replacement with replace

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

Conclusion

The six approaches demonstrate how to efficiently replace numeric codes with descriptive strings in a Pandas DataFrame, giving readers multiple options to choose from based on their coding style and performance needs.