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I have a DataFrame where the columns are a PeriodIndex by Month as follows:
df = pd.DataFrame(np.random.randn(3,4), index = np.arange(3), columns = pd.period_range('2015-01', freq = 'M', periods = 4))
2015-01 2015-02 2015-03 2015-04
0 -1.459943 -1.572013 2.977714 -0.067696
1 -1.545259 -0.570757 0.133756 -1.231192
2 0.498197 -0.555625 0.174545 0.371475
I can select a subset of columns as follows:
testdf[[pd.Period('2015-01'),pd.Period('2015-03')]]
2015-01 2015-03
0 -1.459943 2.977714
1 -1.545259 0.133756
2 0.498197 0.174545
However when it comes to slicing to get for example all months from '2015-01' to '2015-03' I am stumped as to the syntax required. I have tried all kinds of iterations without luck.
For example:
df[pd.Period('2015-01'):pd.Period('2015-03')]
df['2015-01':'2015-03']
All of which do not work.
How can I slice this PeriodIndex?
793
In Pandas, data is typically arranged in rows and columns. A DataFrame is an indexed and typed two-dimensional data structure. In Pandas, you can use a technique called DataFrame slicing to extract just the data you need from large or small datasets.
Use DataFrame. loc[] and DataFrame. iloc[] to select a single column or multiple columns from pandas DataFrame by column names/label or index position respectively. where loc[] is used with column labels/names and iloc[] is used with column index/position.
If you'd like to select columns based on integer indexing, you can use the . iloc function. If you'd like to select columns based on label indexing, you can use the . loc function.
Use .ix to pass a slice for the column selection arg:
In [9]:
df.ix[:,pd.Period('2015-01'):pd.Period('2015-03')]
Out[9]:
2015-01 2015-02 2015-03
0 0.046028 -0.298445 0.908185
1 -0.955049 -1.420290 1.632564
2 0.848906 0.089553 0.551265
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