Pandas Append Column-Pandas Dataframe

Pandas Append Column

Pandas is a powerful data manipulation library in Python that offers various functionalities for data analysis and manipulation. One common task when working with data is appending a new column to an existing DataFrame. This article will explore different methods to append columns to a DataFrame using the Pandas library. We will cover a range of techniques, from simple additions of single columns to more complex operations involving conditions and computations.

1. Adding a Single Column

The simplest way to append a column to a DataFrame is by assigning a list or array of values to a new column name. Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Append a new column
df['Website'] = ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com']

print(df)

Output:

Pandas Append Column

2. Using `assign()` Method

The assign() method allows you to append one or more new columns to a DataFrame. This method returns a new DataFrame, leaving the original DataFrame unchanged.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Append a new column using assign
df_new = df.assign(Website=['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'])

print(df_new)

Output:

Pandas Append Column

3. Appending a Column Based on Conditions

You can append a column to a DataFrame based on certain conditions applied to the existing data. Here’s how you can do it:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Append a new column based on condition
df['Status'] = ['Senior' if age >= 30 else 'Junior' for age in df['Age']]

print(df)

Output:

Pandas Append Column

4. Using `concat()` to Append Multiple Columns

If you have multiple columns stored in another DataFrame or a dictionary, you can use concat() to append them to the original DataFrame.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Another DataFrame
additional_data = pd.DataFrame({
    'Website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'Status': ['Junior', 'Senior', 'Senior']
})

# Concatenate columns
df = pd.concat([df, additional_data], axis=1)

print(df)

Output:

Pandas Append Column

5. Appending a Column Using `map()`

The map() function can be used to create a new column based on the mapping of values from another column.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Employee ID': [1, 2, 3]
})

# Mapping dictionary
employee_website = {1: 'pandasdataframe.com', 2: 'pandasdataframe.com', 3: 'pandasdataframe.com'}

# Append a new column using map
df['Website'] = df['Employee ID'].map(employee_website)

print(df)

Output:

Pandas Append Column

6. Using `apply()` to Append a Column

The apply() function allows you to apply a function along an axis of the DataFrame and append the result as a new column.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Function to categorize age
def categorize_age(age):
    return 'Senior' if age >= 30 else 'Junior'

# Append a new column using apply
df['Category'] = df['Age'].apply(categorize_age)

print(df)

Output:

Pandas Append Column

7. Appending a Column with a Default Value

Sometimes, you might want to append a column that contains a default value for all rows.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Append a new column with a default value
df['Website'] = 'pandasdataframe.com'

print(df)

Output:

Pandas Append Column

8. Using `merge()` to Append Columns from Another DataFrame

If you have a separate DataFrame and want to merge it with your original DataFrame based on a key, you can use the merge() function.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Employee ID': [1, 2, 3]
})

# Another DataFrame with additional information
additional_info = pd.DataFrame({
    'Employee ID': [1, 2, 3],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com']
})

# Merge DataFrames
df = pd.merge(df, additional_info, on='Employee ID')

print(df)

Output:

Pandas Append Column

9. Appending a Computed Column

You can append a column that is computed based on other columns in the DataFrame.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Hours Worked': [40, 50, 45],
    'Hourly Rate': [15, 20, 18]
})

# Append a computed column for total salary
df['Total Salary'] = df['Hours Worked'] * df['Hourly Rate']

print(df)

Output:

Pandas Append Column

10. Using `eval()` to Append a Computed Column

The eval() function in Pandas allows for efficient operations on DataFrame columns. You can use it to append a new computed column.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Hours Worked': [40, 50, 45],
    'Hourly Rate': [15, 20, 18]
})

# Append a computed column using eval
df.eval('Total Salary = Hours Worked * Hourly Rate', inplace=True)

print(df)

In conclusion, appending columns to a DataFrame in Pandas can be achieved through various methods depending on the specific requirements of your data manipulation task. Whether you’re adding a simple static column, merging data from another DataFrame, or computing values based on existing columns, Pandas provides a robust toolkit to efficiently handle these operations.

Pandas Append Column