Question

想知道是否有更有效的方法将多列分成某列。比如说我有：

prev    open    close   volume
20.77   20.87   19.87   962816
19.87   19.89   19.56   668076
19.56   19.96   20.1    578987
20.1    20.4    20.53   418597

我想得到：

prev    open    close   volume
20.77   1.0048  0.9567  962816
19.87   1.0010  0.9844  668076
19.56   1.0204  1.0276  578987
20.1    1.0149  1.0214  418597

基本上，列打开＆＃39;和'关闭'＆＃39;已除以列＆＃39; prev。＆＃39;

中的值

我能够通过

来做到这一点

df['open'] = list(map(lambda x,y: x/y, df['open'],df['prev']))
df['close'] = list(map(lambda x,y: x/y, df['close'],df['prev']))

我想知道是否有更简单的方法？特别是如果有10列被分成相同的值呢？

Answer 1

为了提高性能，我建议使用底层数组数据和array-slicing作为要修改的两列按顺序使用视图 -

a = df.values
df.iloc[:,1:3] = a[:,1:3]/a[:,0,None]

为了更多地阐述阵列切片部分，a[:,[1,2]]会在那里强制复制，并且会减慢它的速度。数据框一侧的a[:,[1,2]]相当于df[['open','close']]，我猜也在减慢速度。因此，df.iloc[:,1:3]正在改进它。

示例运行 -

In [64]: df
Out[64]: 
    prev   open  close  volume
0  20.77  20.87  19.87  962816
1  19.87  19.89  19.56  668076
2  19.56  19.96  20.10  578987
3  20.10  20.40  20.53  418597

In [65]: a = df.values
    ...: df.iloc[:,1:3] = a[:,1:3]/a[:,0,None]
    ...: 

In [66]: df
Out[66]: 
    prev      open     close  volume
0  20.77  1.004815  0.956668  962816
1  19.87  1.001007  0.984399  668076
2  19.56  1.020450  1.027607  578987
3  20.10  1.014925  1.021393  418597

运行时测试

方法 -

def numpy_app(df): # Proposed in this post
    a = df.values
    df.iloc[:,1:3] = a[:,1:3]/a[:,0,None]
    return df

def pandas_app1(df): # @Scott Boston's soln
    df[['open','close']] = df[['open','close']].div(df['prev'].values,axis=0)
    return df

计时 -

In [44]: data = np.random.randint(15, 25, (100000,4)).astype(float)
    ...: df1 = pd.DataFrame(data, columns=(('prev','open','close','volume')))
    ...: df2 = df1.copy()
    ...: 

In [45]: %timeit pandas_app1(df1)
    ...: %timeit numpy_app(df2)
    ...: 
100 loops, best of 3: 2.68 ms per loop
1000 loops, best of 3: 885 µs per loop

Answer 2

columns_to_divide = ['open', 'close']
df[columns_to_divide] = df[columns_to_divide] / df['prev']

Answer 3

df2[['open','close']] = df2[['open','close']].div(df2['prev'].values,axis=0)

输出：

    prev      open     close  volume
0  20.77  1.004815  0.956668  962816
1  19.87  1.001007  0.984399  668076
2  19.56  1.020450  1.027607  578987
3  20.10  1.014925  1.021393  418597

将某些列除以pandas中的另一列

3 个答案: