Add Empty Column(s) to Pandas DataFrame [6 Methods]


Deepak Prasad

Python Pandas

In the realm of data analysis and manipulation with Python, the library that often stands front and center is pandas. The primary data structure of pandas, the DataFrame, is a two-dimensional, size-mutable, and heterogeneous tabular data container, akin to a spreadsheet. As users navigate through the myriad tasks involved in data wrangling, a common operation they might encounter is the need to "add empty column to pandas DataFrame". This action can serve various purposes – from acting as a placeholder for subsequent data insertion, reformatting tasks, to facilitating easier data merges.

 

Understanding 'Empty' in Pandas DataFrames

In the context of a pandas DataFrame, "empty" generally refers to cells or columns that do not have data. Such emptiness in a DataFrame can be represented in a few ways:

  • NaN (Not a Number): This is the standard representation of missing or null values in pandas for float, integer, and complex numbers. It's a special floating-point value from the numpy library.
  • NaT (Not a Timestamp): Specifically for datetime-like data types, pandas uses NaT to denote missing values.
  • None: In Python, None represents the absence of a value or a null value. When you put None into a pandas DataFrame or Series, pandas often converts it to NaN or NaT depending on the context and the data type of the column.
  • Empty string ("" or ''): While this is technically a value (i.e., not missing in the same way as NaN or None), in some contexts, an empty string might be treated or considered as "empty."

An "empty" DataFrame can also refer to a DataFrame that has no rows (and potentially no columns). You can check if a DataFrame is empty by using the .empty attribute:

import pandas as pd

df = pd.DataFrame()
print(df.empty)  # This will return True since df has no rows or columns.

 

Different methods to add empty column(s) to pandas DataFrame

Here are the different methods to add an empty column to a pandas DataFrame:

  1. Using bracket notation.
  2. Using the assign() method.
  3. Using the insert() method.
  4. Directly setting with DataFrame's attribute-like access.
  5. Using the reindex() method with columns.
  6. Using the concat() function.

 

1. Using bracket notation

When you want to add a new column to a DataFrame, you can use the name of the new column in square brackets and assign values to it. If the column name already exists, this method will overwrite the existing column.

Example 1: Adding an Empty Column

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Add an empty column named 'C'
df['C'] = ""

print(df)

Output:

   A  B C
0  1  4  
1  2  5  
2  3  6  

 

Example 2: Adding a Column with NaN values

If you want to add a column filled with NaN (Not a Number) values, you can use the numpy library:

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Add a column named 'C' with NaN values
df['C'] = np.nan

print(df)

Output:

   A  B   C
0  1  4 NaN
1  2  5 NaN
2  3  6 NaN

 

Example 3: Adding a Column with Default Values

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Add a column named 'C' with default value 0
df['C'] = 0

print(df)

Output:

   A  B  C
0  1  4  0
1  2  5  0
2  3  6  0

 

2. Using the assign() method

The assign() method returns a new DataFrame with the added columns, rather than modifying the original DataFrame in-place. This is in line with the functional programming paradigm which promotes immutability. To add empty columns using the assign() method, you can assign NaN values or another placeholder value to the new columns.

Example 1: Adding a Single Empty Column

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add an empty column 'B'
df_new = df.assign(B=np.nan)

print(df_new)

Output:

   A   B
0  1 NaN
1  2 NaN
2  3 NaN

 

Example 2: Adding Multiple Empty Columns

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add empty columns 'B' and 'C'
df_new = df.assign(B=np.nan, C=np.nan)

print(df_new)

Output:

   A   B   C
0  1 NaN NaN
1  2 NaN NaN
2  3 NaN NaN

 

Example 3: Using a Different Placeholder Value for Empty Columns

If you want the new columns to have a default value other than NaN, you can specify that value in the assign() method.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add an empty column 'B' with a default value of 0
df_new = df.assign(B=0)

print(df_new)

Output:

   A  B
0  1  0
1  2  0
2  3  0

 

3. Using the insert() method

The insert() method in pandas provides a way to insert a column into a DataFrame at a specific column index. This method is unique in that it lets you specify the position where the new column should be added.

To add an empty column using the insert() method, you can insert a column filled with NaN values or another placeholder value.

Example 1: Adding a Single Empty Column at a Specific Position

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'C': [4, 5, 6]
})

# Insert an empty column 'B' between 'A' and 'C'
df.insert(1, 'B', np.nan)

print(df)

Output:

   A   B  C
0  1 NaN  4
1  2 NaN  5
2  3 NaN  6

 

Example 2: Adding an Empty Column at the End

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Insert an empty column 'C' at the end
df.insert(len(df.columns), 'C', np.nan)

print(df)

Output:

   A  B   C
0  1  4 NaN
1  2  5 NaN
2  3  6 NaN

 

Example 3: Using a Different Placeholder Value for the Empty Column

If you want the new column to have a default value other than NaN, you can specify that value in the insert() method.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'C': [4, 5, 6]
})

# Insert a column 'B' with a default value of 0 between 'A' and 'C'
df.insert(1, 'B', 0)

print(df)

Output:

   A  B  C
0  1  0  4
1  2  0  5
2  3  0  6

 

4. Directly setting with DataFrame's attribute-like access

DataFrame columns can be added using attribute-like access, although this method has some limitations. Primarily, it's important to note that the column name should be a valid Python identifier (i.e., it should start with an underscore or a letter and should not contain spaces or special characters). This is because attribute-like access in pandas essentially treats column names as attributes of the DataFrame object.

Example 1: Adding a Single Empty Column

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add an empty column 'B' using attribute-like access
df.B = np.nan

print(df)

Output:

   A   B
0  1 NaN
1  2 NaN
2  3 NaN

 

Example 2: Adding Multiple Empty Columns

This method becomes a bit more tedious when adding multiple columns, as you would need to assign each one separately.

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add empty columns 'B' and 'C' using attribute-like access
df.B = np.nan
df.C = np.nan

print(df)

Output:

   A   B   C
0  1 NaN NaN
1  2 NaN NaN
2  3 NaN NaN

 

Example 3: Using a Different Placeholder Value for the Empty Column

Instead of NaN, you can set the column values to any other default value.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add a column 'B' with a default value of 0 using attribute-like access
df.B = 0

print(df)

Output:

   A  B
0  1  0
1  2  0
2  3  0

 

5. Using the reindex() method with columns

The reindex() method is used to change the row and column labels of a DataFrame. When using it for columns, it allows you to add new columns by specifying a new set of column labels. Any new labels that didn't exist in the original DataFrame will be added as empty columns filled with NaN values.

Example 1: Adding a Single Empty Column

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add an empty column 'B'
df_new = df.reindex(columns=['A', 'B'])

print(df_new)

Output

   A   B
0  1 NaN
1  2 NaN
2  3 NaN

 

Example 2: Adding Multiple Empty Columns

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3]
})

# Add empty columns 'B' and 'C'
df_new = df.reindex(columns=['A', 'B', 'C'])

print(df_new)

Output:

   A   B   C
0  1 NaN NaN
1  2 NaN NaN
2  3 NaN NaN

 

Example 3: Reordering and Adding New Columns Simultaneously

You can also use reindex() to reorder existing columns and add new ones at the same time.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'C': [4, 5, 6]
})

# Reorder columns and add a new empty column 'B' in the middle
df_new = df.reindex(columns=['A', 'B', 'C'])

print(df_new)

Output:

   A   B  C
0  1 NaN  4
1  2 NaN  5
2  3 NaN  6

 

6. Using the concat() function to add columns

The concat() function in pandas is primarily used for concatenating two or more pandas objects along a particular axis. While it's often used for concatenating rows (i.e., along axis 0), you can also use it to concatenate columns (i.e., along axis 1). By exploiting this behavior, you can also add new columns to a DataFrame.

Example 1: Adding a Single Empty Column

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# DataFrame with an empty column 'C'
empty_col = pd.DataFrame({'C': np.nan}, index=df.index)

# Concatenate the original DataFrame with the empty column
df_new = pd.concat([df, empty_col], axis=1)

print(df_new)

Output:

   A  B   C
0  1  4 NaN
1  2  5 NaN
2  3  6 NaN

 

Example 2: Adding Multiple Empty Columns

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# DataFrame with empty columns 'C' and 'D'
empty_cols = pd.DataFrame({'C': np.nan, 'D': np.nan}, index=df.index)

# Concatenate the original DataFrame with the empty columns
df_new = pd.concat([df, empty_cols], axis=1)

print(df_new)

Output:

   A  B   C   D
0  1  4 NaN NaN
1  2  5 NaN NaN
2  3  6 NaN NaN

 

Choosing the Data Type for the Empty Column

The data type of a column plays a critical role in pandas. When you add an empty column filled with NaN values, it defaults to a floating-point data type (float64). However, there are situations where you might want to specify a different data type or be aware of the default choice, especially when considering memory efficiency.

 

Default data type (float64 for empty columns in pandas):

By default, when you add an empty column with NaN values, pandas assigns the float64 data type because NaN (which stands for "Not a Number") is inherently a floating-point representation in Python.

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]})
df['B'] = None
print(df['B'].dtype)  # Outputs: float64

 

Specifying a different data type using dtype:

  • If you anticipate the type of data the empty column will hold in the future, you can proactively specify the data type.
  • This is particularly useful if you want to initialize an empty column that will later be populated with integers, strings, or other data types.
df['C'] = pd.Series(dtype='int64')
df['D'] = pd.Series(dtype='object')

 

Importance of choosing the correct data type for memory efficiency:

  • Memory usage is an important consideration when working with large DataFrames. The more precise you can be with data types, the more memory you can save.
  • For example, using a float32 instead of float64 or int32 instead of int64 can reduce memory consumption, especially for large datasets.
  • It's also essential when considering categorical data. If a column will hold a limited set of string values, converting it to a categorical data type can save a significant amount of memory.
  • Using the memory_usage() method on a DataFrame can provide insights into how much memory each column uses.

 

Handling Errors and Edge Cases

What happens if the column name already exists?

  • If you attempt to add a column that already exists in the DataFrame using methods like bracket notation or assign(), the original column will be overwritten without any warning.
  • If you want to avoid this, you can first check if a column with the desired name already exists:
if 'ColumnName' not in df.columns:
    df['ColumnName'] = None
else:
    print("Column already exists!")

 

Ensuring that the new column aligns with the DataFrame's index.

  • When adding a new column, pandas will automatically align the new column's data with the DataFrame's index.
  • However, if you're adding a column from another DataFrame or Series, there could be alignment issues if the indices don't match. It's a good practice to ensure that indices align or to be prepared for NaN values in locations where they don't.
  • You can use the align() function to align two DataFrames or Series based on their indices. The function will introduce NaN values in places where indices don't overlap.

 

Handling situations where you need to add multiple empty columns.

  • Adding multiple empty columns is straightforward, but you need to be cautious about overwriting existing columns.
  • If you're using bracket notation, you can simply iterate over a list of new column names:
new_columns = ['B', 'C', 'D']
for col in new_columns:
    if col not in df.columns:
        df[col] = None

With methods like assign(), you can add multiple columns in one call. For reindex(), you can specify an extended list of columns.

 

Summary

To add an empty column to a pandas DataFrame, several methods can be employed, each catering to specific requirements and use cases.

Brackets Notation: The most straightforward way to add an empty column to a pandas DataFrame is by using bracket notation. It's as simple as assigning None or NaN values to a new column name. This method is quick and intuitive, but if the column already exists, it will overwrite it without warning.

Using assign() Method: The assign() method offers a more functional approach. With it, you can add one or more empty columns to a DataFrame, ensuring the columns align with the DataFrame's index. It returns a new DataFrame, preserving the original.

Direct Attribute Access: Although convenient for scripting, adding columns using attribute-like access has its pitfalls. The column name must be a valid Python identifier, and it's less explicit, which can lead to potential naming conflicts or confusion.

The reindex() Method: If you need to add new columns while potentially reshuffling the existing ones, the reindex() method is invaluable. However, care must be taken to ensure that indices align, especially when sourcing columns from other DataFrames.

 

References

 

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Deepak Prasad

He is the founder of GoLinuxCloud and brings over a decade of expertise in Linux, Python, Go, Laravel, DevOps, Kubernetes, Git, Shell scripting, OpenShift, AWS, Networking, and Security. With extensive experience, he excels in various domains, from development to DevOps, Networking, and Security, ensuring robust and efficient solutions for diverse projects. You can reach out to him on his LinkedIn profile or join on Facebook page.

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