Pandas: Copy Two Columns to a New DataFrame

Table of Contents

1. Core Concepts
2. Typical Usage Method
3. Common Practice
4. Best Practices
5. Code Examples
6. Conclusion
7. FAQ
8. References

Core Concepts

DataFrame

A pandas DataFrame is a two - dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or a SQL table. Each column in a DataFrame can be thought of as a pandas Series, which is a one - dimensional labeled array.

Column Selection

Selecting columns from a DataFrame is a fundamental operation. When we copy two columns to a new DataFrame, we are essentially creating a subset of the original data based on column labels.

Copying

There are two types of copying in pandas: shallow copy and deep copy. A shallow copy creates a new DataFrame object but still references the original data in memory. A deep copy creates a completely independent copy of the data, so changes to the new DataFrame do not affect the original one.

Typical Usage Method

The most straightforward way to copy two columns to a new DataFrame is by using the column labels. We can pass a list of column names to the original DataFrame indexing operator.

import pandas as pd

# Create a sample DataFrame
data = {
    'col1': [1, 2, 3],
    'col2': [4, 5, 6],
    'col3': [7, 8, 9]
}
df = pd.DataFrame(data)

# Copy two columns to a new DataFrame
new_df = df[['col1', 'col2']]

In this example, we first create a sample DataFrame with three columns. Then we use the list ['col1', 'col2'] to select these two columns and assign the result to a new DataFrame new_df.

Common Practice

Error Handling

When selecting columns, it’s important to handle cases where the column names might not exist in the DataFrame. We can use the try - except block to catch KeyError exceptions.

try:
    new_df = df[['col1', 'col4']]
except KeyError as e:
    print(f"Column {e} not found in the DataFrame.")

Checking Data Integrity

After creating the new DataFrame, we can check its shape and data types to ensure that the operation was successful.

print(new_df.shape)
print(new_df.dtypes)

Best Practices

Deep Copy

By default, the above method creates a view of the original DataFrame. If we want to ensure that the new DataFrame is completely independent of the original one, we can use the copy() method.

new_df = df[['col1', 'col2']].copy(deep=True)

Use Descriptive Column Names

When creating the new DataFrame, it’s a good practice to use descriptive column names. If needed, we can rename the columns in the new DataFrame.

new_df = df[['col1', 'col2']].copy(deep=True)
new_df.columns = ['Column_One', 'Column_Two']

Code Examples

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)

# Copy 'Name' and 'Age' columns to a new DataFrame with deep copy
new_df = df[['Name', 'Age']].copy(deep=True)

# Rename columns
new_df.columns = ['Full_Name', 'Years_Old']

# Print the new DataFrame
print(new_df)

In this example, we create a DataFrame with information about people. We then copy the ‘Name’ and ‘Age’ columns to a new DataFrame, make a deep copy, rename the columns, and finally print the new DataFrame.

Conclusion

Copying two columns from a pandas DataFrame to a new one is a simple yet important operation in data analysis. By understanding the core concepts, using the typical usage methods, following common practices, and applying best practices, we can ensure that our code is robust and the new DataFrame meets our requirements.

FAQ

Q1: What is the difference between a shallow copy and a deep copy?

A: A shallow copy creates a new DataFrame object but still references the original data in memory. Changes to the new DataFrame may affect the original one. A deep copy creates a completely independent copy of the data, so changes to the new DataFrame do not affect the original one.

Q2: What if I want to copy more than two columns?

A: You can simply add more column names to the list passed to the indexing operator. For example, df[['col1', 'col2', 'col3']] will copy three columns to a new DataFrame.

Q3: Can I copy columns based on their positions instead of names?

A: Yes, you can use the iloc indexer. For example, df.iloc[:, [0, 1]] will select the first and second columns of the DataFrame.

References

• pandas official documentation: https://pandas.pydata.org/docs/
• Python official documentation: https://docs.python.org/3/

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