How to select one or multiple rows in a pandas DataFrame in python ?

Examples of how to select one or multiple rows in a pandas DataFrame in python:

Create a DataFrame

Lets consider the following dataset train.csv (that can be downloaded on kaggle). To read the file a solution is to use read_csv():

>>> import pandas as pd
>>> df = pd.read_csv('train.csv')
>>> df.shape
(1460, 81)

Get a dataset preview:

>>> df.head(10)
   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \
0   1          60       RL         65.0     8450   Pave   NaN      Reg   
1   2          20       RL         80.0     9600   Pave   NaN      Reg   
2   3          60       RL         68.0    11250   Pave   NaN      IR1   
3   4          70       RL         60.0     9550   Pave   NaN      IR1   
4   5          60       RL         84.0    14260   Pave   NaN      IR1   
5   6          50       RL         85.0    14115   Pave   NaN      IR1   
6   7          20       RL         75.0    10084   Pave   NaN      Reg   
7   8          60       RL          NaN    10382   Pave   NaN      IR1   
8   9          50       RM         51.0     6120   Pave   NaN      Reg   
9  10         190       RL         50.0     7420   Pave   NaN      Reg

  LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature MiscVal  \
0         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
1         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
2         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
3         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
4         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
5         Lvl    AllPub    ...            0    NaN  MnPrv        Shed     700   
6         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
7         Lvl    AllPub    ...            0    NaN    NaN        Shed     350   
8         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
9         Lvl    AllPub    ...            0    NaN    NaN         NaN       0

  MoSold YrSold  SaleType  SaleCondition  SalePrice  
0      2   2008        WD         Normal     208500  
1      5   2007        WD         Normal     181500  
2      9   2008        WD         Normal     223500  
3      2   2006        WD        Abnorml     140000  
4     12   2008        WD         Normal     250000  
5     10   2009        WD         Normal     143000  
6      8   2007        WD         Normal     307000  
7     11   2009        WD         Normal     200000  
8      4   2008        WD        Abnorml     129900  
9      1   2008        WD         Normal     118000

[10 rows x 81 columns]

Select a given row

>>> df.iloc[4,:]
Id                     5
MSSubClass            60
MSZoning              RL
LotFrontage           84
LotArea            14260
Street              Pave
Alley                NaN
LotShape             IR1
LandContour          Lvl
Utilities         AllPub
LotConfig            FR2
LandSlope            Gtl
Neighborhood     NoRidge
Condition1          Norm
Condition2          Norm
BldgType            1Fam
HouseStyle        2Story
OverallQual            8
OverallCond            5
YearBuilt           2000
YearRemodAdd        2000
RoofStyle          Gable
RoofMatl         CompShg
Exterior1st      VinylSd
Exterior2nd      VinylSd
MasVnrType       BrkFace
MasVnrArea           350
ExterQual             Gd
ExterCond             TA
Foundation         PConc
                  ...   
BedroomAbvGr           4
KitchenAbvGr           1
KitchenQual           Gd
TotRmsAbvGrd           9
Functional           Typ
Fireplaces             1
FireplaceQu           TA
GarageType        Attchd
GarageYrBlt         2000
GarageFinish         RFn
GarageCars             3
GarageArea           836
GarageQual            TA
GarageCond            TA
PavedDrive             Y
WoodDeckSF           192
OpenPorchSF           84
EnclosedPorch          0
3SsnPorch              0
ScreenPorch            0
PoolArea               0
PoolQC               NaN
Fence                NaN
MiscFeature          NaN
MiscVal                0
MoSold                12
YrSold              2008
SaleType              WD
SaleCondition     Normal
SalePrice         250000
Name: 4, dtype: object

Select a list of rows

>>> df.iloc[[3,5,7],:]
   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \
3   4          70       RL         60.0     9550   Pave   NaN      IR1   
5   6          50       RL         85.0    14115   Pave   NaN      IR1   
7   8          60       RL          NaN    10382   Pave   NaN      IR1

  LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature MiscVal  \
3         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   
5         Lvl    AllPub    ...            0    NaN  MnPrv        Shed     700   
7         Lvl    AllPub    ...            0    NaN    NaN        Shed     350

  MoSold YrSold  SaleType  SaleCondition  SalePrice  
3      2   2006        WD        Abnorml     140000  
5     10   2009        WD         Normal     143000  
7     11   2009        WD         Normal     200000

[3 rows x 81 columns]

Select multiple consecutive rows

>>> df.iloc[2:5,:]
   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \
2   3          60       RL         68.0    11250   Pave   NaN      IR1   
3   4          70       RL         60.0     9550   Pave   NaN      IR1   
4   5          60       RL         84.0    14260   Pave   NaN      IR1

  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \
2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   
3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   
4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0

  MoSold YrSold  SaleType  SaleCondition  SalePrice  
2      9   2008        WD         Normal     223500  
3      2   2006        WD        Abnorml     140000  
4     12   2008        WD         Normal     250000

[3 rows x 81 columns]

References