Reset column index in Pandas to 0,1,2,3...?

Question

How do I go about resetting the index of my dataframe columns to 0,1,2,3,4?

(How come doing df.reset_index() doesn't reset the column index?)

>>> data = data.drop(data.columns[[1,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]], axis=1)
>>> data = data.drop(data.index[[0,1]],axis = 0)
>>> print(data.head())
             0         2    3    4    20
2  500292014600       .00  .00  .00  NaN
3  500292014600    100.00  .00  .00  NaN
4  500292014600  11202.00  .00  .00  NaN
>>> data = data.reset_index(drop = True)
>>> print(data.head())
              0         2    3    4    20
 0  500292014600       .00  .00  .00  NaN
 1  500292014600    100.00  .00  .00  NaN
 2  500292014600  11202.00  .00  .00  NaN

score 44 · Answer 1 · edited May 31 '17 at 19:33

44

Try the following:

df = df.T.reset_index(drop=True).T

edited May 31 '17 at 19:33

Martin Evans

43,220
16
78
90

answered May 31 '17 at 19:05

Pablo Fonseca

667
6
6

9

this sounds like a hack; the `reset_index` method should be possibly applied to column indexes as well – ClementWalter Mar 08 '19 at 22:18
2

That's a nice way around the problem but is there a direct way to reset both row and column index? – Prashant Kumar Jan 13 '21 at 13:10

score 35 · Accepted Answer · edited Mar 21 '18 at 05:54

Try replacing the column names:

>>> import numpy as np
>>> import pandas as pd

>>> my_data = [[500292014600, .00, .00, .00, np.nan],
              [500292014600, 100.00, .00, .00, np.nan], 
              [500292014600, 11202.00, .00, .00, np.nan]]
>>> df = pd.DataFrame(my_data, columns=[0,2,3,4,20])
>>> df
              0        2    3    4  20
0  500292014600      0.0  0.0  0.0 NaN
1  500292014600    100.0  0.0  0.0 NaN
2  500292014600  11202.0  0.0  0.0 NaN

>>> df.columns = range(df.shape[1])
>>> df
              0        1    2    3   4
0  500292014600      0.0  0.0  0.0 NaN
1  500292014600    100.0  0.0  0.0 NaN
2  500292014600  11202.0  0.0  0.0 NaN

score 4 · Answer 3 · answered Feb 16 '17 at 21:10

4

In pandas, by index you essentially mean row index. As you can see in your data, the row index is reset after drop and reset_index().

For columns, you need to rename them, you can do something like

data.columns = [ 0,1,2,3,4]

answered Feb 16 '17 at 21:10

Vaishali

35,413
4
48
78

score 1 · Answer 4 · answered Oct 08 '17 at 07:56

1

If you have numpy imported with import numpy as np

simply set the columns to zero based indexes with data.columns = [np.arange(0,data.shape[1])]

answered Oct 08 '17 at 07:56

ashish patel

21
1

score 1 · Answer 5 · answered Sep 16 '21 at 06:46

How come when i use df.reset_index the index of my columns is not reset?

The column "index" is really more of a column title. It's possible that someone may using the "index" as a meaningful title. For example, perhaps they represent "Trial 1", "Trial 2", etc., so you wouldn't want to re-index it automatically and lose the significance.

How do I go about resetting this index to 0,1,2,3,4?

To reset the column indexes:

df.columns = range(df.columns.size)

score 0 · Answer 6 · answered Aug 23 '18 at 17:10

Pure Python Implementation

We enumerate the columns of the dataframe to create an array of items. Then we map the function reversed to each item in the array. Lastly, we create and input the dictionary as the parameter columns in the data frame object method rename.

columns = dict(map(reversed, enumerate(df.columns)))
df = df.rename(columns=columns)
df.head()

Results:

              0        1    2    3   4
0  500292014600      0.0  0.0  0.0 NaN
1  500292014600    100.0  0.0  0.0 NaN
2  500292014600  11202.0  0.0  0.0 NaN

Reset column index in Pandas to 0,1,2,3...?

6 Answers6

Pure Python Implementation

Linked

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