Data Manipulation

Pandas Basics

1 min read

Published Sep 29 2025, updated Oct 24 2025

PandasPython

Renaming Columns

You can rename all columns at once or specific ones.

# Rename all columns by assigning a new list

df.columns = ["Name", "Age", "Salary"]

# Rename selected columns

df.rename(columns={"old_name": "new_name"}, inplace=True)

Use .rename() when you only want to change a few columns.

Selecting Columns by Data Type

.select_dtypes() helps filter numeric, categorical, or boolean columns.

# Select only numeric columns

df_numeric = df.select_dtypes(include=["number"])

# Select only object (string) columns

df_object = df.select_dtypes(include=["object"])

# Exclude float columns

df_no_float = df.select_dtypes(exclude=["float"])

Converting to NumPy

Access the underlying NumPy representation of the DataFrame.

# returns a 2D NumPy array

df_array = df.values

This strips column and index labels (just raw data).

Replacing Data

.replace() can substitute values in the whole DataFrame or specific columns.

# Replace a single value

df.replace(0, pd.NA, inplace=True)

# Replace multiple values

df.replace([1,2,3], [10,20,30], inplace=True)

# Replace in a single column

df["col"].replace("?", "Unknown", inplace=True)

Changing Data Types

.astype() is used to convert a column to a new data type.

# Convert a column to int

df["Age"] = df["Age"].astype(int)

# Convert multiple columns

df = df.astype({"Age": "int32", "Salary": "float"})

Mapping Data

.map() can apply a function, dictionary mapping, or Series to each element in that column.

df["col"].map({"M": "Male", "F": "Female"})

This replaces "M" with "Male" and "F" with "Female" in that column.

You can also pass a function:

df["col"].map(str.lower)

applymap() can apply a function elementwise to every single cell in the entire DataFrame.

df.applymap(str.upper)

Converts every value in the DataFrame to uppercase strings.

Data Manipulation

Pandas Basics

1 min read

Published Sep 29 2025, updated Oct 24 2025

Guide Sections

Guide Comments

Renaming Columns

Selecting Columns by Data Type

Converting to NumPy

Replacing Data

Changing Data Types

Mapping Data

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