We can also use sklearn.preprocessing.MultiLabelBinarizer:

Often we want to use sparse DataFrame for the real world data in order to save a lot of RAM.

Sparse solution (for Pandas v0.25.0+)

from sklearn.preprocessing import MultiLabelBinarizermlb = MultiLabelBinarizer(sparse_output=True)df = df.join(            pd.DataFrame.sparse.from_spmatrix(                mlb.fit_transform(df.pop('Col3')),                index=df.index,                columns=mlb.classes_))

result:

In [38]: dfOut[38]:  Col1  Col2  Apple  Banana  Grape  Orange0    C  33.0      1       1      0       11    A   2.5      1       0      1       02    B  42.0      0       1      0       0In [39]: df.dtypesOut[39]:Col1                objectCol2               float64Apple     Sparse[int32, 0]Banana    Sparse[int32, 0]Grape     Sparse[int32, 0]Orange    Sparse[int32, 0]dtype: objectIn [40]: df.memory_usage()Out[40]:Index     128Col1       24Col2       24Apple      16    #  <--- NOTE!Banana     16    #  <--- NOTE!Grape       8    #  <--- NOTE!Orange      8    #  <--- NOTE!dtype: int64

Dense solution

mlb = MultiLabelBinarizer()df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('Col3')),                          columns=mlb.classes_,                          index=df.index))

Result:

In [77]: dfOut[77]:  Col1  Col2  Apple  Banana  Grape  Orange0    C  33.0      1       1      0       11    A   2.5      1       0      1       02    B  42.0      0       1      0       0

Option 1
Short Answer
pir_slow

df.drop('Col3', 1).join(df.Col3.str.join('|').str.get_dummies())  Col1  Col2  Apple  Banana  Grape  Orange0    C  33.0      1       1      0       11    A   2.5      1       0      1       02    B  42.0      0       1      0       0

Option 2
Fast Answer
pir_fast

v = df.Col3.valuesl = [len(x) for x in v.tolist()]f, u = pd.factorize(np.concatenate(v))n, m = len(v), u.sizei = np.arange(n).repeat(l)dummies = pd.DataFrame(    np.bincount(i * m + f, minlength=n * m).reshape(n, m),    df.index, u)df.drop('Col3', 1).join(dummies)  Col1  Col2  Apple  Orange  Banana  Grape0    C  33.0      1       1       1      01    A   2.5      1       0       0      12    B  42.0      0       0       1      0

Option 3
pir_alt1

df.drop('Col3', 1).join(    pd.get_dummies(        pd.DataFrame(df.Col3.tolist()).stack()    ).astype(int).sum(level=0))  Col1  Col2  Apple  Orange  Banana  Grape0    C  33.0      1       1       1      01    A   2.5      1       0       0      12    B  42.0      0       0       1      0

Timing Results
Code Below

def maxu(df):    mlb = MultiLabelBinarizer()    d = pd.DataFrame(        mlb.fit_transform(df.Col3.values)        , df.index, mlb.classes_    )    return df.drop('Col3', 1).join(d)def bos(df):    return df.drop('Col3', 1).assign(**pd.get_dummies(df.Col3.apply(lambda x:pd.Series(x)).stack().reset_index(level=1,drop=True)).sum(level=0))def psi(df):    return pd.concat([        df.drop("Col3", 1),        df.Col3.apply(lambda x: pd.Series(1, x)).fillna(0)    ], axis=1)def alex(df):    return df[['Col1', 'Col2']].assign(**{fruit: [1 if fruit in cell else 0 for cell in df.Col3]                                        for fruit in set(fruit for fruits in df.Col3                                                         for fruit in fruits)})def pir_slow(df):    return df.drop('Col3', 1).join(df.Col3.str.join('|').str.get_dummies())def pir_alt1(df):    return df.drop('Col3', 1).join(pd.get_dummies(pd.DataFrame(df.Col3.tolist()).stack()).astype(int).sum(level=0))def pir_fast(df):    v = df.Col3.values    l = [len(x) for x in v.tolist()]    f, u = pd.factorize(np.concatenate(v))    n, m = len(v), u.size    i = np.arange(n).repeat(l)    dummies = pd.DataFrame(        np.bincount(i * m + f, minlength=n * m).reshape(n, m),        df.index, u    )    return df.drop('Col3', 1).join(dummies)results = pd.DataFrame(    index=(1, 3, 10, 30, 100, 300, 1000, 3000),    columns='maxu bos psi alex pir_slow pir_fast pir_alt1'.split())for i in results.index:    d = pd.concat([df] * i, ignore_index=True)    for j in results.columns:        stmt = '{}(d)'.format(j)        setp = 'from __main__ import d, {}'.format(j)        results.set_value(i, j, timeit(stmt, setp, number=10))

You can use the functions explode (new in version 0.25.0.) and crosstab:

s = df['Col3'].explode()df[['Col1', 'Col2']].join(pd.crosstab(s.index, s))

Output:

  Col1  Col2  Apple  Banana  Grape  Orange0    C  33.0      1       1      0       11    A   2.5      1       0      1       02    B  42.0      0       1      0       0