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I'm using IPython Notebook and would like to be able to control which type of output is returned from submitting a simple DataFrame name. For example:

df = DataFrame({"A": [1,2,3], "B": [4,5,6]})
df

will always return a grid representation because it such a small DataFrame. However larger DataFrames will show in HTML representation (at least with my settings in the notebook). That is, as long as there isn't more than about 15 columns, in which case the representation is something like this:

<class 'pandas.core.frame.DataFrame'>
Index: 35 entries, 6215.0 to 6028.0
Data columns:
District Name               35  non-null values
Total Percent Pass          35  non-null values
GE Percent Pass             35  non-null values
SE Percent Pass             3  non-null values
White Percent Pass          11  non-null values
Black Percent Pass          23  non-null values
Hispanic Percent Pass       21  non-null values
Asian Percent Pass          4  non-null values
PI Percent Pass             0  non-null values
AI Percent Pass             0  non-null values
Other Percent Pass          0  non-null values
EcDis Percent Pass          29  non-null values
Non EcDis Percent Pass      26  non-null values
Total Percent Pass_D        35  non-null values
GE Percent Pass_D           35  non-null values
SE Percent Pass_D           31  non-null values
White Percent Pass_D        27  non-null values
Black Percent Pass_D        35  non-null values
Hispanic Percent Pass_D     35  non-null values
Asian Percent Pass_D        19  non-null values
PI Percent Pass_D           0  non-null values
AI Percent Pass_D           0  non-null values
Other Percent Pass_D        0  non-null values
EcDis Percent Pass_D        35  non-null values
Non EcDis Percent Pass_D    35  non-null values
Comparative                 35  non-null values
dtypes: float64(25), object(1)

I would like to be able to force this type of representation (the last one) at will without having to turn options on an off in the notebook. Is there a way to do this?

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1 回答 1

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Extending your example, you can just use df.info

In [21]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns:
A    3  non-null values
B    3  non-null values
dtypes: int64(2)
于 2013-03-27T16:22:57.323 回答