使用pandas比较两列

使用pandas比较两列

您可以使用np.where。如果

cond

是布尔数组，

A

并且

B

是数组，则

C = np.where(cond, A, B)

将C定义为等于

A

哪里

cond

为True，

B

哪里

cond

为False。

import numpy as npimport pandas as pda = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]df = pd.Dataframe(a, columns=['one', 'two', 'three'])df['que'] = np.where((df['one'] >= df['two']) & (df['one'] <= df['three'])          , df['one'], np.nan)

产量

  one  two three  que0  10  1.2   4.2   101  15   70  0.03  NaN2   8    5     0  NaN

---

如果您有多个条件，则可以使用np.select代替。例如，如果你想

df['que']

等于

df['two']

时

df['one']< df['two']

，则

conditions = [    (df['one'] >= df['two']) & (df['one'] <= df['three']),     df['one'] < df['two']]choices = [df['one'], df['two']]df['que'] = np.select(conditions, choices, default=np.nan)

产量

  one  two three  que0  10  1.2   4.2   101  15   70  0.03   702   8    5     0  NaN

如果我们可以假设

df['one'] >= df['two']

when

df['one'] < df['two']

为False，那么条件和选择可以简化为

conditions = [    df['one'] < df['two'],    df['one'] <= df['three']]choices = [df['two'], df['one']]

（如果包含

df['one']

或

df['two']

包含NaN，则该假设可能不正确。）

---

注意

a = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]df = pd.Dataframe(a, columns=['one', 'two', 'three'])

用字符串值定义一个Dataframe。由于它们看起来是数字，因此最好将这些字符串转换为浮点数：

df2 = df.astype(float)

但是，这会改变结果，因为字符串会逐个字符地进行比较，而浮点数会进行数字比较。

In [61]: '10' <= '4.2'Out[61]: TrueIn [62]: 10 <= 4.2Out[62]: False