Why use to_frame before reset_index? - pandas

Question

Using a data set like this one
df = pd.DataFrame(np.random.randint(0,5,size=(20, 3)), columns=['user_id','module_id','week'])
we often see this pattern:
df.groupby(['user_id'])['module_id'].count().to_frame().reset_index().rename({'module_id':'count'}, axis='columns')
But we get exactly the same result from
df.groupby(['user_id'])['module_id'].count().reset_index(name='count')
(N.B. we need the additional rename in the former because reset_index on Series (here) includes a name parameter and returns a data frame, while reset_index on DataFrame (here) does not include the name parameter.)
Is there any advantage in using to_frame first?
(I wondered if it might be an artefact of earlier versions of pandas, but that looks unlikely:
Series.reset_index was added in this commit on the 27th of January 2012.
Series.to_frame was added in this commit on the 13th of October 2013.
So Series.reset_index was available over a year before Series.to_frame.)

Answer 1

There is no noticeable advantage of using to_frame(). Both approaches can be used to achieve the same result. It is common in pandas to use multiple approaches for solving a problem. The only advantage I can think of is that for larger sets of data, it maybe more convenient to have a dataframe view first before resetting the index. If we take your dataframe as an example, you will find that to_frame() displays a dataframe view that maybe useful to understand the data in terms of a neat dataframe table v/s a count series. Also, the usage of to_frame() makes the intent more clear to a new user who looks at your code for the first time.
The example dataframe:
In [7]: df = pd.DataFrame(np.random.randint(0,5,size=(20, 3)), columns=['user_i
...: d','module_id','week'])
In [8]: df.head()
Out[8]:
user_id module_id week
0 3 4 4
1 1 3 4
2 1 2 2
3 1 3 4
4 1 2 2
The count() function returns a Series:
In [18]: test1 = df.groupby(['user_id'])['module_id'].count()
In [19]: type(test1)
Out[19]: pandas.core.series.Series
In [20]: test1
Out[20]:
user_id
0 2
1 7
2 4
3 6
4 1
Name: module_id, dtype: int64
In [21]: test1.index
Out[21]: Int64Index([0, 1, 2, 3, 4], dtype='int64', name='user_id')
Using to_frame makes it explicit that you intend to convert the Series to a Dataframe. The index here is user_id:
In [22]: test1.to_frame()
Out[22]:
module_id
user_id
0 2
1 7
2 4
3 6
4 1
And now we reset the index and rename the column using Dataframe.rename. As you rightly pointed, Dataframe.reset_index() does not have a name parameter and therefore, we will have to rename the column explicitly.
In [24]: testdf1 = test1.to_frame().reset_index().rename({'module_id':'count'}, axis='columns')
In [25]: testdf1
Out[25]:
user_id count
0 0 2
1 1 7
2 2 4
3 3 6
4 4 1
Now lets look at the other case. We will use the same count() series test1 but rename it as test2 to differentiate between the two approaches. In other words, test1 is equal to test2.
In [26]: test2 = df.groupby(['user_id'])['module_id'].count()
In [27]: test2
Out[27]:
user_id
0 2
1 7
2 4
3 6
4 1
Name: module_id, dtype: int64
In [28]: test2.reset_index()
Out[28]:
user_id module_id
0 0 2
1 1 7
2 2 4
3 3 6
4 4 1
In [30]: testdf2 = test2.reset_index(name='count')
In [31]: testdf1 == testdf2
Out[31]:
user_id count
0 True True
1 True True
2 True True
3 True True
4 True True
As you can see both dataframes are equivalent, and in the second approach we just had to use reset_index(name='count') to both reset the index and rename the column name because Series.reset_index() does have a name parameter.
The second case has lesser code but is less readable for new eyes and I'd prefer the first approach of using to_frame() because it makes the intent clear: "Convert this count series to a dataframe and rename the column 'module_id' to 'count'".