Pandas compare next row

Looks like you want to use the Series.shift method.

Using this method, you can generate new columns which are offset to the original columns. Like this:

df['qty_s'] = df['qty'].shift(-1)df['t_s'] = df['t'].shift(-1)df['z_s'] = df['z'].shift(-1)

Now you can compare these:

df['is_something'] = (df['qty'] == df['qty_s']) & (df['t'] < df['t_s']) & (df['z'] == df['z_s'])

Here is a simplified example of how Series.shift works to compare next row to the current:

df = pd.DataFrame({"temp_celcius":pd.np.random.choice(10, 10) + 20}, index=pd.date_range("2015-05-15", "2015-05-24")) df            temp_celcius2015-05-15            212015-05-16            282015-05-17            272015-05-18            212015-05-19            252015-05-20            282015-05-21            252015-05-22            222015-05-23            292015-05-24            25df["temp_c_yesterday"] = df["temp_celcius"].shift(1)df            temp_celcius  temp_c_yesterday2015-05-15            21               NaN2015-05-16            28                212015-05-17            27                282015-05-18            21                272015-05-19            25                212015-05-20            28                252015-05-21            25                282015-05-22            22                252015-05-23            29                222015-05-24            25                29df["warmer_than_yesterday"] = df["temp_celcius"] > df["temp_c_yesterday"]            temp_celcius  temp_c_yesterday warmer_than_yesterday2015-05-15            21               NaN                 False2015-05-16            28                21                  True2015-05-17            27                28                 False2015-05-18            21                27                 False2015-05-19            25                21                  True2015-05-20            28                25                  True2015-05-21            25                28                 False2015-05-22            22                25                 False2015-05-23            29                22                  True2015-05-24            25                29                 False

If I misunderstood your query, please post a comment and I'll update my answer.