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
Try the following:
df = df.T.reset_index(drop=True).T
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
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]