I'm trying to create a new pandas.DataFrame from another pandas.DataFrame based on a unique multiple column index. I'm able to create a pandas.core.index.MultiIndex using df.index.drop_duplicates() with the correct results, but I can't figure out how to convert it to a pandas.DataFrame.
The following script creates the original DataFrame using a SQL Query.
import sqlite3 as db
import pandas as pd
conn = db.connect('C:/data.db')
query = """SELECT TimeStamp, UnderlyingSymbol, Expiry, Strike, CP, BisectIV, OTMperc FROM ActiveOptions
WHERE TimeStamp = '2015-11-09 16:00:00' AND UnderlyingSymbol = 'INTC' AND
Expiry < '2015-11-27 16:00:00' AND OTMperc < .02 AND OTMperc > -.02
ORDER BY UnderlyingSymbol, Expiry, ABS(OTMperc)"""
df = pd.read_sql_query(sql=query, con=conn,index_col=['TimeStamp', 'UnderlyingSymbol', 'Expiry'],
parse_dates=['TimeStamp', 'Expiry'])
The script creates the following DataFrame:
In[6]: df
Out[6]:
Strike CP BisectIV OTMperc
TimeStamp UnderlyingSymbol Expiry
2015-11-09 16:00:00 INTC 2015-11-13 16:00:00 33.5 -1 0.2302 -0.0045
2015-11-13 16:00:00 33.5 1 0.2257 0.0045
2015-11-13 16:00:00 33.0 -1 0.2442 0.0105
2015-11-13 16:00:00 33.0 1 0.2426 -0.0106
2015-11-13 16:00:00 34.0 1 0.2240 0.0191
2015-11-13 16:00:00 34.0 -1 0.2295 -0.0195
2015-11-20 16:00:00 33.5 1 0.2817 0.0045
2015-11-20 16:00:00 33.5 -1 0.2840 -0.0045
2015-11-20 16:00:00 33.0 -1 0.2935 0.0105
2015-11-20 16:00:00 33.0 1 0.2914 -0.0106
2015-11-20 16:00:00 34.0 1 0.2718 0.0191
2015-11-20 16:00:00 34.0 -1 0.2784 -0.0195
Creating a new DataFrame with a unique multiple column index generates the following output:
In[10]: new_df = df.index.drop_duplicates()
In[11]: new_df
Out[11]:
MultiIndex(levels=[[2015-11-09 16:00:00], [u'INTC'], [2015-11-13 16:00:00, 2015-11-20 16:00:00]],
labels=[[0, 0], [0, 0], [0, 1]],
names=[u'TimeStamp', u'UnderlyingSymbol', u'Expiry'])
In[12]: type(new_df)
Out[12]: pandas.core.index.MultiIndex
Any ideas?
The problem is that you set new_df to the index list with the duplicates removed:
new_df = df.index.drop_duplicates()
What you want is to select only the rows which do not have duplicate indices. You can use the duplicated function to filter your old data frame:
new_df = df[~df.index.duplicated()]
A small example, based on this:
#create data sample with multi index
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'one', 'one', 'two', 'one', 'two', 'one', 'one']]
#(the first and last are duplicates)
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
s = pd.Series(np.random.randn(8), index=index)
The original data:
>>> s
first second
bar one -0.932521
one 1.969771
baz one 1.574908
two 0.125159
foo one -0.075174
two 0.777039
qux one -0.992862
one -1.099260
dtype: float64
And filtered for duplicates:
>>> s[~s.index.duplicated()]
first second
bar one -0.932521
baz one 1.574908
two 0.125159
foo one -0.075174
two 0.777039
qux one -0.992862
dtype: float64