Convert pandas Series to DataFrame

I have a Pandas series sf:

email
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="01646c60686d3041646c60686d2f626e6c">[email protected]</a>    [1.0, 0.0, 0.0]
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="d2b7bfb3bbbee092b7bfb3bbbefcb1bdbf">[email protected]</a>    [2.0, 0.0, 0.0]
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="4f2a222e26237c0f2a222e2623612c2022">[email protected]</a>    [1.0, 0.0, 0.0]
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="91f4fcf0f8fda5d1f4fcf0f8fdbff2fefc">[email protected]</a>    [4.0, 0.0, 0.0]
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="71141c10181d4431141c10181d5f121e1c">[email protected]</a>    [1.0, 0.0, 3.0]
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="6f0a020e0603592f0a020e0603410c0002">[email protected]</a>    [1.0, 5.0, 0.0]

And I would like to transform it to the following DataFrame:

index | email             | list
_____________________________________________
0     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="ed88808c8481dcad88808c8481c38e8280">[email protected]</a>  | [1.0, 0.0, 0.0]
1     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="4f2a222e26237d0f2a222e2623612c2022">[email protected]</a>  | [2.0, 0.0, 0.0]
2     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="67020a060e0b5427020a060e0b4904080a">[email protected]</a>  | [1.0, 0.0, 0.0]
3     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="a3c6cec2cacf97e3c6cec2cacf8dc0ccce">[email protected]</a>  | [4.0, 0.0, 0.0]
4     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="62070f030b0e5722070f030b0e4c010d0f">[email protected]</a>  | [1.0, 0.0, 3.0]
5     | <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="16737b777f7a2056737b777f7a3875797b">[email protected]</a>  | [1.0, 5.0, 0.0]

I found a way to do it, but I doubt it’s the more efficient one:

df1 = pd.DataFrame(data=sf.index, columns=['email'])
df2 = pd.DataFrame(data=sf.values, columns=['list'])
df = pd.merge(df1, df2, left_index=True, right_index=True)

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

Rather than create 2 temporary dfs you can just pass these as params within a dict using the DataFrame constructor:

pd.DataFrame({'email':sf.index, 'list':sf.values})

There are lots of ways to construct a df, see the docs

Method 2

to_frame():

Starting with the following Series, df:

email
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="fb9e969a9297cabb9e969a9297d5989496">[email protected]</a>    A
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="98fdf5f9f1f4aad8fdf5f9f1f4b6fbf7f5">[email protected]</a>    B
<a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="c9aca4a8a0a5fa89aca4a8a0a5e7aaa6a4">[email protected]</a>    C
dtype: int64

I use to_frame to convert the series to DataFrame:

df = df.to_frame().reset_index()

    email               0
0   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="90f5fdf1f9fca1d0f5fdf1f9fcbef3fffd">[email protected]</a>    A
1   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="adc8c0ccc4c19fedc8c0ccc4c183cec2c0">[email protected]</a>    B
2   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="056068646c6936456068646c692b666a68">[email protected]</a>    C
3   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="21444c40484d1561444c40484d0f424e4c">[email protected]</a>    D

Now all you need is to rename the column name and name the index column:

df = df.rename(columns= {0: 'list'})
df.index.name = 'index'

Your DataFrame is ready for further analysis.

Update: I just came across this link where the answers are surprisingly similar to mine here.

Method 3

One line answer would be

myseries.to_frame(name='my_column_name')

Or

myseries.reset_index(drop=True, inplace=True)  # As needed

Method 4

Series.reset_index with name argument

Often the use case comes up where a Series needs to be promoted to a DataFrame. But if the Series has no name, then reset_index will result in something like,

s = pd.Series([1, 2, 3], index=['a', 'b', 'c']).rename_axis('A')
s

A
a    1
b    2
c    3
dtype: int64
s.reset_index()

   A  0
0  a  1
1  b  2
2  c  3

Where you see the column name is “0”. We can fix this be specifying a name parameter.

s.reset_index(name='B')

   A  B
0  a  1
1  b  2
2  c  3
s.reset_index(name='list')

   A  list
0  a     1
1  b     2
2  c     3

Series.to_frame

If you want to create a DataFrame without promoting the index to a column, use Series.to_frame, as suggested in this answer. This also supports a name parameter.

s.to_frame(name='B')

   B
A   
a  1
b  2
c  3

pd.DataFrame Constructor

You can also do the same thing as Series.to_frame by specifying a columns param:

pd.DataFrame(s, columns=['B'])

   B
A   
a  1
b  2
c  3

Method 5

Super simple way is also

df = pd.DataFrame(series)

It will return a DF of 1 column (series values) + 1 index (0….n)

Method 6

Series.to_frame can be used to convert a Series to DataFrame.

# The provided name (columnName) will substitute the series name
df = series.to_frame('columnName')

For example,

s = pd.Series(["a", "b", "c"], name="vals")
df = s.to_frame('newCol')
print(df)

   newCol
0    a
1    b
2    c

Method 7

probably graded as a non-pythonic way to do this but this’ll give the result you want in a line:

new_df = pd.DataFrame(zip(email,list))

Result:

               email               list
0   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="741119151d1845341119151d185a171b19">[email protected]</a>    [1.0, 0.0, 0.0]
1   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="b9dcd4d8d0d58bf9dcd4d8d0d597dad6d4">[email protected]</a>    [2.0, 0.0, 0.0]
2   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="20454d41494c1360454d41494c0e434f4d">[email protected]</a>    [1.0, 0.0, 0.0]
3   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="e2878f838b8ed6a2878f838b8ecc818d8f">[email protected]</a>    [4.0, 0.0, 3.0]
4   <a href="https://getridbug.com/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="61040c00080d5421040c00080d4f020e0c">[email protected]</a>    [1.0, 5.0, 0.0]


All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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