How can I transform a series object (generated with iloc) into a single-row dataframe?

Question

Suppose I have a pd.DataFrame object. Suppose further that I've used the iloc method to extract a single row from it as a pd.Series object. How can I transform that pd.Series object into a single-row pd.DataFrame object?

Here's a small reproducible example of what I'm talking about:

import pandas as pd

# Generating a DataFrame
df = pd.DataFrame({'id':[1,2,3,4,5],
                   'colA':['a','b','c','d','e'],
                   'colB':[11,22,33,44,55]})

# Extracting a single row from the DataFrame as
# a pd.Series object
row_as_series = df.iloc[0]

print(row_as_series)
# id      1
# colA    a
# colB    11
# Name: 0, dtype: object

print(type(row_as_series))
# <class 'pandas.core.series.Series'>

print(row_as_series.shape)
# (3,)

How can I reshape the row_as_series object above into a single-row DataFrame? Here is what I'd like the output to look like

   id colA  colB
0   1    a    11

Note about iloc[[0]]

I know that I can use iloc and double square brackets to generate a DataFrame-like output instead of a Series-like output, as seen below:

row_as_df = df.iloc[[0]]

print(row_as_df)
#    id colA  colB
# 0   1    a    11

print(type(row_as_series))
# <class 'pandas.core.frame.DataFrame'>

print(row_as_series.shape)
# (1, 3)

However, suppose I can't change the part of the code with the iloc command. Suppose that I'm only allowed to edit the part of the code that is "downstream" from the iloc[] method. I'm still curious about how to go from a Series object to a single-row DataFrame.

Answer 1

You can just put the Series object inside a list and feed it into pd.DataFrame(), as seen below:

single_row_df = pd.DataFrame([row_as_series])

print(single_row_df)
#    id colA  colB
# 0   1    a    11

print(type(single_row_df))
# <class 'pandas.core.frame.DataFrame'>

print(single_row_df.shape)
# (1, 3)

Extra info: building pd.DataFrames with multiple rows

This wasn't part of the original question, but it's worth mentioning: If you add multiple pd.Series objects into the list, they will be read as multiple rows. Here's a quick example:

temp_df = pd.DataFrame([df.iloc[0],
                        df.iloc[2],
                        df.iloc[4]])

print(temp_df)
#    id colA  colB
# 0   1    a    11
# 2   3    c    33
# 4   5    e    55

print(type(temp_df))
# <class 'pandas.core.frame.DataFrame'>

print(temp_df.shape)
# (3, 3)