pandas 比较下一行 [英] Pandas compare next row

问题描述

我有一个这样的数据框

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]

我想将第一行与第二行进行比较

I want compare first row with second row

1. 的数量与下一行的数量一致AND


2. 在下一行中更大，而


3. 的z值与下一行相同

所需的值为

   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

推荐答案

看起来你想使用 Series.shift 方法。

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

使用此方法，可以生成与原始列偏移的新列。像这样：

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)

现在可以比较这些：

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

如何 Series.shift 可以将下一行与当前的值进行比较：

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

如果我误解您的查询，请发表评论，我会更新我的答案。

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

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