Pandas compare next row

Question

I have a dataframe like this

d={}
d['z']=['Q8','Q8','Q7','Q9','Q9']
d['t']=['10:30','10:31','10:38','10:40','10:41']
d['qty']=[20,20,9,12,12]

I want compare first row with second row

1. is qty same as next row AND
2. is t greater in the next row AND
3. is z value same as next row

The desired value is

   qty                   t   z  valid
0   20 2015-06-05 10:30:00  Q8  False
1   20 2015-06-05 10:31:00  Q8   True
2    9 2015-06-05 10:38:00  Q7  False
3   12 2015-06-05 10:40:00  Q9  False
4   12 2015-06-05 10:41:00  Q9   True

Answer 1

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_celcius

2015-05-15            21
2015-05-16            28
2015-05-17            27
2015-05-18            21
2015-05-19            25
2015-05-20            28
2015-05-21            25
2015-05-22            22
2015-05-23            29
2015-05-24            25

df["temp_c_yesterday"] = df["temp_celcius"].shift(1)
df
            temp_celcius  temp_c_yesterday
2015-05-15            21               NaN
2015-05-16            28                21
2015-05-17            27                28
2015-05-18            21                27
2015-05-19            25                21
2015-05-20            28                25
2015-05-21            25                28
2015-05-22            22                25
2015-05-23            29                22
2015-05-24            25                29

df["warmer_than_yesterday"] = df["temp_celcius"] > df["temp_c_yesterday"]
            temp_celcius  temp_c_yesterday warmer_than_yesterday
2015-05-15            21               NaN                 False
2015-05-16            28                21                  True
2015-05-17            27                28                 False
2015-05-18            21                27                 False
2015-05-19            25                21                  True
2015-05-20            28                25                  True
2015-05-21            25                28                 False
2015-05-22            22                25                 False
2015-05-23            29                22                  True
2015-05-24            25                29                 False

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