You have an index with three index items 3. For this reason df.loc[3] will return a dataframe.

The reason is that you don’t specify the column. So df.loc[3] selects three items of all columns (which is column 0), while df.loc[3,0] will return a Series. E.g. df.loc[1:2] also returns a dataframe, because you slice the rows.

Selecting a single row (as df.loc[1]) returns a Series with the column names as the index.

If you want to be sure to always have a DataFrame, you can slice like df.loc[1:1]. Another option is boolean indexing (df.loc[df.index==1]) or the take method (df.take([0]), but this used location not labels!).

You wrote in a comment to joris’ answer:

“I don’t understand the design
decision for single rows to get converted into a series – why not a
data frame with one row?”

A single row isn’t converted in a Series.
It IS a Series: No, I don't think so, in fact; see the edit

The best way to think about the pandas data structures is as flexible
containers for lower dimensional data. For example, DataFrame is a
container for Series, and Panel is a container for DataFrame objects.
We would like to be able to insert and remove objects from these
containers in a dictionary-like fashion.

http://pandas.pydata.org/pandas-docs/stable/overview.html#why-more-than-1-data-structure

The data model of Pandas objects has been choosen like that. The reason certainly lies in the fact that it ensures some advantages I don’t know (I don’t fully understand the last sentence of the citation, maybe it’s the reason)

.

Edit : I don’t agree with me

A DataFrame can’t be composed of elements that would be Series, because the following code gives the same type “Series” as well for a row as for a column:

import pandas as pd

df = pd.DataFrame(data=[11,12,13], index=[2, 3, 3])

print '-------- df -------------'
print df

print 'n------- df.loc[2] --------'
print df.loc[2]
print 'type(df.loc[1]) : ',type(df.loc[2])

print 'n--------- df[0] ----------'
print df[0]
print 'type(df[0]) : ',type(df[0])

result

-------- df -------------
    0
2  11
3  12
3  13

------- df.loc[2] --------
0    11
Name: 2, dtype: int64
type(df.loc[1]) :  <class 'pandas.core.series.Series'>

--------- df[0] ----------
2    11
3    12
3    13
Name: 0, dtype: int64
type(df[0]) :  <class 'pandas.core.series.Series'>

So, there is no sense to pretend that a DataFrame is composed of Series because what would these said Series be supposed to be : columns or rows ? Stupid question and vision.

.

Then what is a DataFrame ?

In the previous version of this answer, I asked this question, trying to find the answer to the Why is that? part of the question of the OP and the similar interrogation single rows to get converted into a series - why not a data frame with one row? in one of his comment,
while the Is there a way to ensure I always get back a data frame? part has been answered by Dan Allan.

Then, as the Pandas’ docs cited above says that the pandas’ data structures are best seen as containers of lower dimensional data, it seemed to me that the understanding of the why would be found in the characteristcs of the nature of DataFrame structures.

However, I realized that this cited advice must not be taken as a precise description of the nature of Pandas’ data structures.
This advice doesn’t mean that a DataFrame is a container of Series.
It expresses that the mental representation of a DataFrame as a container of Series (either rows or columns according the option considered at one moment of a reasoning) is a good way to consider DataFrames, even if it isn’t strictly the case in reality. “Good” meaning that this vision enables to use DataFrames with efficiency. That’s all.

.

Then what is a DataFrame object ?

The DataFrame class produces instances that have a particular structure originated in the NDFrame base class, itself derived from the PandasContainer base class that is also a parent class of the Series class.
Note that this is correct for Pandas until version 0.12. In the upcoming version 0.13, Series will derive also from NDFrame class only.

# with pandas 0.12

from pandas import Series
print 'Series  :n',Series
print 'Series.__bases__  :n',Series.__bases__

from pandas import DataFrame
print 'nDataFrame  :n',DataFrame
print 'DataFrame.__bases__  :n',DataFrame.__bases__

print 'n-------------------'

from pandas.core.generic import NDFrame
print 'nNDFrame.__bases__  :n',NDFrame.__bases__

from pandas.core.generic import PandasContainer
print 'nPandasContainer.__bases__  :n',PandasContainer.__bases__

from pandas.core.base import PandasObject
print 'nPandasObject.__bases__  :n',PandasObject.__bases__

from pandas.core.base import StringMixin
print 'nStringMixin.__bases__  :n',StringMixin.__bases__

result

Series  :
<class 'pandas.core.series.Series'>
Series.__bases__  :
(<class 'pandas.core.generic.PandasContainer'>, <type 'numpy.ndarray'>)

DataFrame  :
<class 'pandas.core.frame.DataFrame'>
DataFrame.__bases__  :
(<class 'pandas.core.generic.NDFrame'>,)

-------------------

NDFrame.__bases__  :
(<class 'pandas.core.generic.PandasContainer'>,)

PandasContainer.__bases__  :
(<class 'pandas.core.base.PandasObject'>,)

PandasObject.__bases__  :
(<class 'pandas.core.base.StringMixin'>,)

StringMixin.__bases__  :
(<type 'object'>,)

So my understanding is now that a DataFrame instance has certain methods that have been crafted in order to control the way data are extracted from rows and columns.

The ways these extracting methods work are described in this page:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing
We find in it the method given by Dan Allan and other methods.

Why these extracting methods have been crafted as they were ?
That’s certainly because they have been appraised as the ones giving the better possibilities and ease in data analysis.
It’s precisely what is expressed in this sentence:

The best way to think about the pandas data structures is as flexible
containers for lower dimensional data.

The why of the extraction of data from a DataFRame instance doesn’t lies in its structure, it lies in the why of this structure. I guess that the structure and functionning of the Pandas’ data structure have been chiseled in order to be as much intellectually intuitive as possible, and that to understand the details, one must read the blog of Wes McKinney.

If the objective is to get a subset of the data set using the index, it is best to avoid using loc or iloc. Instead you should use syntax similar to this :

df = pd.DataFrame(data=range(5), index=[1, 2, 3, 3, 3])
result = df[df.index == 3] 
isinstance(result, pd.DataFrame) # True

result = df[df.index == 1]
isinstance(result, pd.DataFrame) # True

Use df['columnName'] to get a Series and df[['columnName']] to get a Dataframe.

If you also select on the index of the dataframe then the result can be either a DataFrame or a Series or it can be a Series or a scalar (single value).

This function ensures that you always get a list from your selection (if the df, index and column are valid):

def get_list_from_df_column(df, index, column):
    df_or_series = df.loc[index,[column]] 
    # df.loc[index,column] is also possible and returns a series or a scalar
    if isinstance(df_or_series, pd.Series):
        resulting_list = df_or_series.tolist() #get list from series
    else:
        resulting_list = df_or_series[column].tolist() 
        # use the column key to get a series from the dataframe
    return(resulting_list)

The TLDR

When using loc

df.loc[:] = Dataframe

df.loc[int] = Dataframe if you have more than one column and Series if you have only 1 column in the dataframe

df.loc[:, ["col_name"]] = Dataframe if you have more than one row and Series if you have only 1 row in the selection

df.loc[:, "col_name"] = Series

Not using loc

df["col_name"] = Series

df[["col_name"]] = Dataframe

every time we put [['column name']] it returns Pandas DataFrame object,
if we put ['column name'] we got Pandas Series object