Renaming & Dropping

renaming

# Rename specific columns
df.rename(columns={"old_name": "new_name"})

# Rename multiple columns
df.rename(columns={
    "firstName": "first_name",
    "lastName": "last_name"
})

# Rename all columns at once
df.columns = ["col1", "col2", "col3"]

# Apply function to all column names
df.columns = df.columns.str.lower()
df.columns = df.columns.str.replace(" ", "_")

dropping columns

# Drop single column
df.drop(columns=["unwanted"])

# Drop multiple columns
df.drop(columns=["col1", "col2"])

# Drop by position
df.drop(df.columns[0], axis=1)  # First column

dropping rows

# Drop by index
df.drop(index=[0, 1, 2])

# Drop by condition (opposite of filtering)
df = df[df["age"] >= 18]  # Keep only adults

# Drop using drop()
df.drop(df[df["score"] < 50].index)

reordering

# Specify exact order
df = df[["name", "age", "city", "score"]]

# Move specific column to front
cols = ["important_col"] + [c for c in df.columns if c != "important_col"]
df = df[cols]

in-place

By default, these return a new DataFrame. Use inplace=True to modify the original:

df.rename(columns={"old": "new"}, inplace=True)
df.drop(columns=["unwanted"], inplace=True)

Or just reassign: df = df.drop(...)