Question

我有一个函数，它在两个不同的列中查找某些字符串，并在满足函数条件时返回原始行值：

def functionator(row):
    if 'J44' in row['0_c']:
        if 'J44' in row['A0']:
            return row['0_c']
        else:
            return np.nan
    elif 'I50' in row['0_c']:
        if 'I50' in row['A0']:
            return row['0_c']
        else:
            return np.nan
    elif 'I51' in row['0_c']:
        if ('I50' or 'I51') in row['A0']:
            return row['0_c']
        else:
            return np.nan
    elif 'F03X' in row['0_c']:
        if ('F00' or 'F01' or 'F02') in row['A0']:
            return row['0_c']
        else:
            return np.nan
    elif 'N18' in row['0_c']:
        if 'N18' in row['A0']:
            return row['0_c']
        else:
            return np.nan
    else:
        return np.nan

df['0_c'] = df.apply(functionator, axis=1)

但是，我想在一系列列中应用此功能，因此我只想通过row['0_c']检查它，而不是只检查row['i_c'] for i in range(n)，并检查row['Ai'] for i in range(m)的范围。 {1}}

提前感谢您的帮助！

Answer 1

这种代码非常低效。你应该首先重组它来对矢量而不是标量进行操作：

X = [1, 2, 3]
Y = [2, 5, 8]

colors=["violet", "green", "yellow"]

fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(X, Y, color=colors)
plt.show()

然后它很容易运行几次：

def vectorator(df, col1, col2):
    col_0_c = df[col1].str.contains
    col_A0 = df[col2].str.contains

    J44 = col_0_c('J44') & col_A0('J44')
    I50 = col_0_c('I50') & col_A0('I50')
    I51 = col_0_c('I51') & col_A0('I5[01]')
    F03X = col_0_c('F03X') & col_A0('F0[012]')
    N18 = col_0_c('N18') & col_A0('N18')

    matches = J44 | I50 | I51 | F03X | N18
    df[col1][~matches] = np.nan

在不同列的范围内应用函数

1 个答案: