python - How to convert index of a pandas dataframe into a column

Question

This seems rather obvious, but I can't seem to figure out how to convert an index of data frame to a column?

For example:

df=
        gi       ptt_loc
 0  384444683      593  
 1  384444684      594 
 2  384444686      596  

To,

df=
    index1    gi       ptt_loc
 0  0     384444683      593  
 1  1     384444684      594 
 2  2     384444686      596  

Answer 1

either:

df['index1'] = df.index

or, .reset_index:

df.reset_index(level=0, inplace=True)

---

so, if you have a multi-index frame with 3 levels of index, like:

>>> df
                       val
tick       tag obs        
2016-02-26 C   2    0.0139
2016-02-27 A   2    0.5577
2016-02-28 C   6    0.0303

and you want to convert the 1st (tick) and 3rd (obs) levels in the index into columns, you would do:

>>> df.reset_index(level=['tick', 'obs'])
          tick  obs     val
tag                        
C   2016-02-26    2  0.0139
A   2016-02-27    2  0.5577
C   2016-02-28    6  0.0303

Answer 2

For MultiIndex you can extract its subindex using

df['si_name'] = R.index.get_level_values('si_name') 

where si_name is the name of the subindex.

Answer 3

To provide a bit more clarity, let's look at a DataFrame with two levels in its index (a MultiIndex).

index = pd.MultiIndex.from_product([['TX', 'FL', 'CA'], 
                                    ['North', 'South']], 
                                   names=['State', 'Direction'])

df = pd.DataFrame(index=index, 
                  data=np.random.randint(0, 10, (6,4)), 
                  columns=list('abcd'))

The reset_index method, called with the default parameters, converts all index levels to columns and uses a simple RangeIndex as new index.

df.reset_index()

Use the level parameter to control which index levels are converted into columns. If possible, use the level name, which is more explicit. If there are no level names, you can refer to each level by its integer location, which begin at 0 from the outside. You can use a scalar value here or a list of all the indexes you would like to reset.

df.reset_index(level='State') # same as df.reset_index(level=0)

In the rare event that you want to preserve the index and turn the index into a column, you can do the following:

# for a single level
df.assign(State=df.index.get_level_values('State'))

# for all levels
df.assign(**df.index.to_frame())

Answer 4

rename_axis + reset_index

You can first rename your index to a desired label, then elevate to a series:

df = df.rename_axis('index1').reset_index()

print(df)

   index1         gi  ptt_loc
0       0  384444683      593
1       1  384444684      594
2       2  384444686      596

This works also for MultiIndex dataframes:

print(df)
#                        val
# tick       tag obs        
# 2016-02-26 C   2    0.0139
# 2016-02-27 A   2    0.5577
# 2016-02-28 C   6    0.0303

df = df.rename_axis(['index1', 'index2', 'index3']).reset_index()

print(df)

       index1 index2  index3     val
0  2016-02-26      C       2  0.0139
1  2016-02-27      A       2  0.5577
2  2016-02-28      C       6  0.0303

Answer 5

If you want to use the reset_index method and also preserve your existing index you should use:

df.reset_index().set_index('index', drop=False)

or to change it in place:

df.reset_index(inplace=True)
df.set_index('index', drop=False, inplace=True)

For example:

print(df)
          gi  ptt_loc
0  384444683      593
4  384444684      594
9  384444686      596

print(df.reset_index())
   index         gi  ptt_loc
0      0  384444683      593
1      4  384444684      594
2      9  384444686      596

print(df.reset_index().set_index('index', drop=False))
       index         gi  ptt_loc
index
0          0  384444683      593
4          4  384444684      594
9          9  384444686      596

And if you want to get rid of the index label you can do:

df2 = df.reset_index().set_index('index', drop=False)
df2.index.name = None
print(df2)
   index         gi  ptt_loc
0      0  384444683      593
4      4  384444684      594
9      9  384444686      596

Answer 6

df1 = pd.DataFrame({"gi":[232,66,34,43],"ptt":[342,56,662,123]})
p = df1.index.values
df1.insert( 0, column="new",value = p)
df1

    new     gi     ptt
0    0      232    342
1    1      66     56 
2    2      34     662
3    3      43     123

Answer 7

This should do the trick (if not multilevel indexing) -

df.reset_index().rename({'index':'index1'}, axis = 'columns')

And of course, you can always set inplace = True, if you do not want to assign this to a new variable in the function parameter of rename.

Answer 8

A very simple way of doing this is to use reset_index() method.For a data frame df use the code below:

df.reset_index(inplace=True)

This way, the index will become a column, and by using inplace as True,this become permanent change.