166

I have a Pandas series sf:

email
email1@email.com    [1.0, 0.0, 0.0]
email2@email.com    [2.0, 0.0, 0.0]
email3@email.com    [1.0, 0.0, 0.0]
email4@email.com    [4.0, 0.0, 0.0]
email5@email.com    [1.0, 0.0, 3.0]
email6@email.com    [1.0, 5.0, 0.0]

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

index | email             | list
_____________________________________________
0     | email1@email.com  | [1.0, 0.0, 0.0]
1     | email2@email.com  | [2.0, 0.0, 0.0]
2     | email3@email.com  | [1.0, 0.0, 0.0]
3     | email4@email.com  | [4.0, 0.0, 0.0]
4     | email5@email.com  | [1.0, 0.0, 3.0]
5     | email6@email.com  | [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)
cs95
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woshitom
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    In more recent versions of pandas, [this can be achieved with a single `reset_index` call](https://stackoverflow.com/a/53686321/4909087). – cs95 Dec 08 '18 at 19:54

8 Answers8

219

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

EdChum
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    another great option is to concat if your series have the same axes `pd.concat([sf.index, sf.values], axis=1)` – Lauren Aug 24 '17 at 20:30
102

to_frame():

Starting with the following Series, df:

email
email1@email.com    A
email2@email.com    B
email3@email.com    C
dtype: int64

I use to_frame to convert the series to DataFrame:

df = df.to_frame().reset_index()

    email               0
0   email1@email.com    A
1   email2@email.com    B
2   email3@email.com    C
3   email4@email.com    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.

Community
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Shoresh
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36

One line answer would be

myseries.to_frame(name='my_column_name')

Or

myseries.reset_index(drop=True, inplace=True)  # As needed
cs95
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Mysterious
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  • I need to use the converted DataFrame obj in seaborn bar plot, so I need to reset index, say, mydf = myseries.to_frame(name='my_column_name').reset_index() – karl li Aug 05 '21 at 21:42
28

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
cs95
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  • I was wondering why one might use `to_frame` instead of `reset_index`, but is there ever a good reason to use both? [here](https://stackoverflow.com/q/56920021/575530) – dumbledad Jul 07 '19 at 07:25
  • @dumbledad mostly utility. If you want a single col dataframe with index, use to_frame(). If you need two columns (one from the series index and the other from series values itself), go with reset_index(). – cs95 Jul 07 '19 at 11:12
  • And what if I want to convert Series to DataFrame with Seires index used as DataFrame columns names (i.e. transposed)? `to_frame` doesn't seem to have an argument to do this. Thanks. – Confounded Apr 20 '20 at 10:01
  • @Confounded use to_frame().T to transpose it – cs95 Apr 20 '20 at 10:19
12

Super simple way is also

df = pd.DataFrame(series)

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

Lorenzo Bassetti
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5

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
Giorgos Myrianthous
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2

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   email1@email.com    [1.0, 0.0, 0.0]
1   email2@email.com    [2.0, 0.0, 0.0]
2   email3@email.com    [1.0, 0.0, 0.0]
3   email4@email.com    [4.0, 0.0, 3.0]
4   email5@email.com    [1.0, 5.0, 0.0]
Prathamesh Mistry
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0

The series.to_frame() method and pd.DataFrame() method is used to convert Pandas Series to a DataFrame.

import pandas
series = pandas.Series([40, 90, 80, 50, 70])
data_frame = series.to_frame()
print(data_frame)

or use the below:

data_frame = pandas.DataFrame(series)

The pd.concat() method is used to convert multiple Series to a single DataFrame in Python.

data_frame = pandas.concat([series1, series2], axis=1)
Adrian Mole
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pythonial
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