I am having a real strange behaviour when trying to reindex a dataframe in pandas. My version of Pandas is 0.10.0 and I use Python 2.7. Basically, when I load a dataframe:
eurusd = pd.DataFrame.load('EUR_USD_30Min.df').drop_duplicates().dropna()
eurusd
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 119710 entries, 2003-02-02 17:30:00 to 2012-12-28 17:00:00
Data columns:
open 119710 non-null values
high 119710 non-null values
low 119710 non-null values
close 119710 non-null values
dtypes: float64(4)
and then I try to reindex inside a larger date range:
newindex = pd.DateRange(datetime.datetime(2002,1,1), datetime.datetime(2012,12,31), offset=pd.datetools.Minute(30))
newindex
<class 'pandas.tseries.index.DatetimeIndex'>
[2002-01-01 00:00:00, ..., 2012-12-31 00:00:00]
Length: 192817, Freq: 30T, Timezone: None
I get strange behaviour when trying to reindex the dataframe. If I reindex one larger part of the dataset I get this error:
eurusd[29558:29560].reindex(index=newindex)
Exception: Reindexing only valid with uniquely valued Index objects
But, if I do the same for two subsets of the data above, I don't get the error:
Here's the first subset, with no problems,
eurusd[29558:29559].reindex(index=newindex)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 192817 entries, 2002-01-01 00:00:00 to 2012-12-31 00:00:00
Freq: 30T
Data columns:
open 1 non-null values
high 1 non-null values
low 1 non-null values
close 1 non-null values
dtypes: float64(4)
and here's the second subset, still no problems,
eurusd[29559:29560].reindex(index=newindex)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 192817 entries, 2002-01-01 00:00:00 to 2012-12-31 00:00:00
Freq: 30T
Data columns:
open 1 non-null values
high 1 non-null values
low 1 non-null values
close 1 non-null values
dtypes: float64(4)
I am really going crazy about this, and cannot understand the reasons of this. It seems like the dataframe is 'clean' from duplicates, and duplicated indexes.... I can provide the pickle file for the dataframe if you want.
You could groupby the index and take the first entry (see docs):
df.groupby(level=0).first()
Example:
In [1]: df = pd.DataFrame([[1], [2]], index=[1, 1])
In [2]: df
Out[2]:
0
1 1
1 2
In [3]: df.groupby(level=0).first()
Out[3]:
0
1 1