Leer Renaming Columns | Data Cleaning

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import pandas as pd

df = pd.read_csv("passengers.csv")

df = df.rename(columns={"Fare": "TicketPrice"})
df.columns = df.columns.str.lower()

Renaming Specific Columns

When you need to rename one or several columns, use the .rename() method. Just pass a dictionary with old names as keys and the new ones as values:


              123456789
            
import pandas as pd

df = pd.read_csv("https://staging-content-media-cdn.codefinity.com/courses/64641555-cae4-4cd0-8d29-807aeb6bc0c4/datasets/passengers.csv")
print(df.columns)

# Rename Fare into TicketPrice and Embarked into Port
df = df.rename(columns={"Fare": "TicketPrice", "Embarked": "Port"})

print(df.columns)

Renaming All Columns

The .rename() method works well when you only need to change a few column names. But if you want to rename every column, it's easier to assign a full list of new names directly:


              12345678910111213
            
import pandas as pd

df = pd.read_csv("https://staging-content-media-cdn.codefinity.com/courses/64641555-cae4-4cd0-8d29-807aeb6bc0c4/datasets/passengers.csv")
print(df.columns)

# Rename all columns
df.columns = [
    "id", "class", "name", "sex", "age",
    "siblings_spouses", "parents_children",
    "ticket", "fare", "cabin", "port", "ticket_date"
]

print(df.columns)

The list must contain the same number of names as there are columns. If the lengths don't match, pandas will raise an error.

Applying Text Transformations

Pandas allows you to apply string operations to column names to improve consistency and readability:


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import pandas as pd

df = pd.DataFrame({
    "Passenger Name": ["John Smith", "Anna Brown", "Mike Johnson"],
    "Ticket Number ": ["A/5 21171", "PC 17599", "STON/O2. 3101282"],
    " Port of Embarkation": ["S", "C", "Q"]
})

print(df.columns)

# Convert all column names to lowercase
df.columns = df.columns.str.lower()
print(df.columns)

# Remove leading or trailing spaces
df.columns = df.columns.str.strip()
print(df.columns)

# Replace spaces with underscores
df.columns = df.columns.str.replace(" ", "_")
print(df.columns)

You can also modify column names by adding prefixes or suffixes. This is especially useful when combining multiple datasets, as it helps avoid name conflicts and makes column origins clearer:


              123456789101112
            
import pandas as pd

df = pd.read_csv("https://staging-content-media-cdn.codefinity.com/courses/64641555-cae4-4cd0-8d29-807aeb6bc0c4/datasets/passengers.csv")
print(df.columns)

# Add a prefix to all column names
df = df.add_prefix("passenger_")

# Add a suffix to all column names
df = df.add_suffix("_info")

print(df.columns)

Note

All these operations affect only the column names, not the data stored in the columns.

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