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

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

Remove one row

Lets create a simple dataframe with pandas

>>> data = np.random.randint(100, size=(10,10))
>>> df = pd.DataFrame(data=data)
>>> df
    0   1   2   3   4   5   6   7   8   9
0  13  44   3  55  13  98  44  87  20  52
1  72  72  96  18  68  84  81  41   8  75
2  53  71  97  27  21  44  61   9  59  87
3  40  94  49   7  56  93  17  62  71  14
4   4  20  56  45  89  78   9  27  58  77
5  71  90  73  79  31  49  14  73  58   1
6  28  77  73  66  90  71  26  51  18  87
7  96  15  18  23  74  82  54  62  24   1
8  32  34  50  57  90  68  20  56  26  78
9  93   0  90  16  24  88  16  90  82  45

To remove for example the row 7 a solution is to use drop():

>>> df.drop(7,0,inplace=True)

returns

>>> df
    0   1   2   3   4   5   6   7   8   9
0  13  44   3  55  13  98  44  87  20  52
1  72  72  96  18  68  84  81  41   8  75
2  53  71  97  27  21  44  61   9  59  87
3  40  94  49   7  56  93  17  62  71  14
4   4  20  56  45  89  78   9  27  58  77
5  71  90  73  79  31  49  14  73  58   1
6  28  77  73  66  90  71  26  51  18  87
8  32  34  50  57  90  68  20  56  26  78
9  93   0  90  16  24  88  16  90  82  45

Remove a list of rows

>>> df = pd.DataFrame(data=data)
>>> df.drop([1,5,7,9],0,inplace=True)
>>> df
    0   1   2   3   4   5   6   7   8   9
0  13  44   3  55  13  98  44  87  20  52
2  53  71  97  27  21  44  61   9  59  87
3  40  94  49   7  56  93  17  62  71  14
4   4  20  56  45  89  78   9  27  58  77
6  28  77  73  66  90  71  26  51  18  87
8  32  34  50  57  90  68  20  56  26  78

Remove multiple consecutive rows

>>> data = np.random.randint(100, size=(10,10))
>>> df.drop(df.index[3:7],0,inplace=True)
>>> df
    0   1   2   3   4   5   6   7   8   9
0  13  44   3  55  13  98  44  87  20  52
1  72  72  96  18  68  84  81  41   8  75
2  53  71  97  27  21  44  61   9  59  87
7  96  15  18  23  74  82  54  62  24   1
8  32  34  50  57  90  68  20  56  26  78
9  93   0  90  16  24  88  16  90  82  45

Remove rows with missing data

Lets create a dataset with missing data

>>> data = np.random.randn(10,7)
>>> data.ravel()[np.random.choice(data.size, 5, replace=False)] = np.nan
>>> data
array([[-0.21556193,  0.50798317, -1.40910182, -2.13125538,  1.1835753 ,
         0.45158695,  0.73910367],
       [-0.87888441,  1.05993664, -0.77287598, -0.69139053, -0.29032073,
        -0.64202622, -0.28829388],
       [-1.60249368, -1.50622796,  1.46894158,         nan, -2.7252065 ,
         1.36411611, -0.57278577],
       [ 0.79703402, -1.5212633 ,  0.62016751,         nan,  1.09850942,
        -0.2358472 , -0.00723673],
       [ 0.8763736 , -1.07815499,  1.07747808, -0.20271076, -0.16235893,
                nan, -1.52423974],
       [ 0.27451099, -1.26743679, -0.05715345, -1.10172544,  0.02002978,
        -0.82632633,  0.54550534],
       [ 1.39432992,  0.9903974 , -1.56818002,  1.29163684, -0.393829  ,
         1.73997774,  0.86798373],
       [-0.07952965, -0.09397074,         nan,  1.53816504, -1.05609124,
         1.08434771, -0.2870059 ],
       [-0.41546041,  0.11339261,  0.14015969, -1.46552628,  0.7903862 ,
                nan,  0.08339854],
       [-1.01347812, -1.41749653, -1.0594971 , -0.84758429, -1.11227765,
         0.46318414,  0.94640032]])

Create a dataframe

>>> df = pd.DataFrame(data=data)

Get a list of row with missing data

>>> index_with_nan = df.index[df.isnull().any(axis=1)]
>>> index_with_nan
Int64Index([2, 3, 4, 7, 8], dtype='int64')

Remove the rows with missing data

>>> df.drop(index_with_nan,0, inplace=True)
>>> df
          0         1         2         3         4         5         6
0 -0.215562  0.507983 -1.409102 -2.131255  1.183575  0.451587  0.739104
1 -0.878884  1.059937 -0.772876 -0.691391 -0.290321 -0.642026 -0.288294
5  0.274511 -1.267437 -0.057153 -1.101725  0.020030 -0.826326  0.545505
6  1.394330  0.990397 -1.568180  1.291637 -0.393829  1.739978  0.867984
9 -1.013478 -1.417497 -1.059497 -0.847584 -1.112278  0.463184  0.946400

References