pandas shared date merge

# Create Pandas DF with one week of random dates...
dates1 = pd.date_range(start='2017-09-01', end='2017-09-07', freq='5s')
# Create DataFrame...
Z = [('Date',dates1)]
ZZ = pd.DataFrame.from_items(Z)
# Add random ID column for later reference...
ZZ['ID'] = np.random.choice(range(1,500), ZZ.shape[0])

# Take first sample...
df1 = ZZ.sample(200)
# Create a list of Unique dates...
uniquedates1 = list(df1['Date'].dt.normalize().unique())

# Take second sample...
df2 = ZZ.sample(200)
uniquedates2 = list(df2['Date'].dt.normalize().unique())

# Find the intersection (shared dates)
sd = set(uniquedates1) & set(uniquedates2)
shareddates = list(sd)

# Merge on Date
merged = {}
for date in shareddates:
    print(date)
    # Select whole day
    # Create date selection mask using one date and timedelta addition
    mask = (df1['Date'] > date) & (df1['Date'] <= date + np.timedelta64(24, 'h'))
    df11 = df1.loc[mask]
    mask = (df2['Date'] > date) & (df2['Date'] <= date + np.timedelta64(24, 'h'))

    df22 = df2.loc[mask]
    merged[date] = df11.merge(df22, how='outer', left_on='Date', right_on='Date')

merged = {}
for date in shareddates:
    print(date)
    # Select whole day
    # Create date selection mask using one date and timedelta addition
    mask = (df1['Date'] > date) & (df1['Date'] <= date + np.timedelta64(24, 'h'))
    df11 = df1.loc[mask]

    # Resample to minutes
    df11.index = df11['Date']
    del df11['Date']
    df111 = df11.resample('T')

    mask = (df2['Date'] > date) & (df2['Date'] <= date + np.timedelta64(24, 'h'))
    df22 = df2.loc[mask]

    # Resample to minutes
    df22.index = df22['Date']
    del df22['Date']
    df222 = df22.resample('T')

    merged[date] = df1111.merge(df222, how='outer', left_index=True, right_index=True)

# ValueError: can not merge DataFrame with instance of type <class 'pandas.core.resample.DatetimeIndexResampler'>