5 Best Ways to Convert Python DataFrame Column Header to Row

In data manipulation scenarios, you might need to transpose column headers of a DataFrame into a row. This is common especially when reshaping data for analysis. Consider a DataFrame with column headers ‘A’, ‘B’, and ‘C’. The goal is to transform these headers into a single row within the DataFrame, changing its structure for better compatibility with certain visualization or analysis tools.

Method 1: Using transpose() and reset_index()

The transpose() method flips the DataFrame’s axes, and reset_index() can then convert the transposed column headers into a row. This approach is commonly used for its simplicity and readability.

Here’s an example:

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
transposed_df = df.T.reset_index().rename(columns={'index': 'Header'})
print(transposed_df)

Output:

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

This snippet transposes the DataFrame and then resets the index to turn the previous column headers into a row, adding them under the new column ‘Header’.

Method 2: Using melt() Function

The melt() function is useful for unpivoting a DataFrame from wide format to long format, which effectively can also convert headers to a row when combined with additional operations.

Here’s an example:

import pandas as pd

df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]})
melted_df = pd.melt(df).T
header_row = melted_df.iloc[[1]]
print(header_row)

Output:

   0  1  2
    value  A  B  C
    

The melt() function unpivots the DataFrame, after which transposing places the original headers into a row. The second row of the transposed DataFrame now contains the headers.

Method 3: List Comprehension and DataFrame Constructor

This method leverages the simplicity of list comprehension to create a list from the column headers, which is then passed to the DataFrame constructor, creating a DataFrame with a single row containing the headers.

Here’s an example:

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
headers_row_df = pd.DataFrame([list(df.columns)], columns=df.columns)
print(headers_row_df)

Output:

    A  B  C
    0  A  B  C
    

The list comprehension creates a list of the column headers, and the DataFrame constructor creates a new DataFrame with the original column names as both the header and the first row data.

Method 4: Using np.array() and DataFrame Constructor

NumPy’s array() function can be used to create an array of the DataFrame’s column headers, which can then be passed into the DataFrame constructor. This is a fast and efficient way to achieve our goal, especially useful with large datasets.

Here’s an example:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
headers_row_df = pd.DataFrame(np.array([df.columns]), columns=df.columns)
print(headers_row_df)

Output:

    A  B  C
    0  A  B  C
    

This code constructs a NumPy array from the DataFrame’s column headers and initializes a new DataFrame, where the first row is composed of the headers.

Bonus One-Liner Method 5: Using iloc and to_frame() with a One-Liner

This one-liner uses iloc and to_frame() to create a new DataFrame where the header is the first row. It’s a concise, though less explicit method.

Here’s an example:

import pandas as pd

df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]})
header_row_df = df.columns.to_frame().T
print(header_row_df)

Output:

    0  1  2
    A  B  C
    

The one-liner takes the column headers using columns, converts them to a DataFrame using to_frame(), then transposes to convert the column header into a row.

Summary/Discussion

• Method 1: Transpose and Reset Index. Simple and readable, suitable for beginners. Can be less efficient with very large datasets.
• Method 2: Melt Function. Offers flexibility and is part of a wider set of DataFrame manipulation methods. It might be less intuitive for those not familiar with data reshaping concepts.
• Method 3: List Comprehension and DataFrame Constructor. Straightforward and Pythonic. Efficiency drops with large datasets, as list comprehensions can be slower than vectorized operations.
• Method 4: NumPy Array and DataFrame Constructor. Efficient and fast, the best choice when working with large data. Requires additional import of NumPy, which is not a default dataframe library.
• Method 5: One-liner with iloc and to_frame(). Concise and elegant, but may compromise readability for the sake of terseness.