The index that I have in the dataframe (with 30 rows) is of the form:
Int64Index([171, 174, 173, 172, 199, …, 175, 200])
The index is not strictly increasing because the data frame is the output of a sort().
I want to add a column which is the series:
[1, 2, 3, 4, 5, …, 30]
How should I go about doing that?
How about:
df['new_col'] = range(1, len(df) + 1)
Alternatively if you want the index to be the ranks and store the original index as a column:
df = df.reset_index()
df = df.reset_index(drop=True)
np.arange instead of native range, like df['new_col'] = np.arange(1, df.shape[0] + 1) should speed up the runtime, especially when dealing with large datasets.I stumbled on this question while trying to do the same thing (I think). Here is how I did it:
df['index_col'] = df.index
You can then sort on the new index column, if you like.
df[df.index.name] = df.indexHow about this:
from pandas import *
idx = Int64Index([171, 174, 173])
df = DataFrame(index = idx, data =([1,2,3]))
print df
It gives me:
0
171 1
174 2
173 3
Is this what you are looking for?
The way to do that would be this:
Resetting the index:
df.reset_index(drop=True, inplace=True)
Sorting an index:
df.sort_index(inplace=True)
Setting a new index from a column:
df.set_index('column_name', inplace=True)
Setting a new index from a range:
df.index = range(1, 31, 1) #a range starting at one ending at 30 with a stepsize of 1.
Sorting a dataframe based on column value:
df.sort_values(by='column_name', inplace=True)
Reassigning variables works as-well:
df=df.reset_index(drop=True)
df=df.sort_index()
df=df.set_index('column_name')
df.index = range(1, 31, 1) #a range starting at one ending at 30 with a stepsize of 1.
df=df.sort_values(by='column_name')
I want to add a column which is the [sort order] series ie set a column to the index.