0
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I'm trying to display all the columns in my dataset that have NaN values:

print(train.isnull().sum())

The output that I get is this:

Id                0
MSSubClass        0
MSZoning          0
LotFrontage       0
LotArea           0
Street            0
Alley             0
LotShape          0
LandContour       0
Utilities         0
LotConfig         0
LandSlope         0
Neighborhood      0
Condition1        0
Condition2        0
BldgType          0
HouseStyle        0
OverallQual       0
OverallCond       0
YearBuilt         0
YearRemodAdd      0
RoofStyle         0
RoofMatl          0
Exterior1st       0
Exterior2nd       0
MasVnrType        8
MasVnrArea        0
ExterQual         0
ExterCond         0
Foundation        0
                 ..
BedroomAbvGr      0
KitchenAbvGr      0
KitchenQual       0
TotRmsAbvGrd      0
Functional        0
Fireplaces        0
FireplaceQu       0
GarageType        0
GarageYrBlt      81
GarageFinish      0
GarageCars        0
GarageArea        0
GarageQual        0
GarageCond        0
PavedDrive        0
WoodDeckSF        0
OpenPorchSF       0
EnclosedPorch     0
3SsnPorch         0
ScreenPorch       0
PoolArea          0
PoolQC            0
Fence             0
MiscFeature       0
MiscVal           0
MoSold            0
YrSold            0
SaleType          0
SaleCondition     0
SalePrice         0
Length: 81, dtype: int64

I actually want to see the entire output and not a shortened version with ..

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2 Answers 2

2
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Alternatively to Marce's answer, you can display all the rows without finding N by using:

with pd.option_context("display.max_rows", -1):
    display(train.isnull().sum())
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0
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I am assuming you are using a Pandas dataframe. In that case, something like

pd.options.display.max_rows = N

should work. Here, N is a number bigger than the number of rows you want to print and pd comes from import pandas as pd.

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2
  • $\begingroup$ Actually its Boston. $\endgroup$ Jul 5, 2019 at 8:40
  • $\begingroup$ what is Boston? what type of object is your train variable? $\endgroup$
    – Marce
    Jul 5, 2019 at 12:59

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