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
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.
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())
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
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
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.
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
Source: Stackoverflow.com