Question

我有一个Pandas数据框，我将添加一个新列（SUGGESTED）。添加新列后，我将使用以下模式使用基于QUERY列值的新值更新它。例如：

QUERY = 'query'
SUGGESTED = 'suggested'
df[SUGGESTED] = numpy.nan
s_query = 'de'
new_value = 'delaware'
df.loc[(df[QUERY] == s_query), [SUGGESTED]] = new_value

示例：

query suggested
al      alabama
ca      california
de      NaN

后：

query suggested
    al      alabama
    ca      california
    de      delaware

到目前为止似乎工作，不确定在Pandas中是否有更有效的方法。

Answer 1

我认为您可以先在df[SUGGESTED] = numpy.nan和loc解决方案中省略np.where，因为它会添加新列：

QUERY = 'query'
SUGGESTED = 'suggested'
s_query = 'de'
new_value = 'delaware'

#if need update existing column
df[SUGGESTED] = df[SUGGESTED].mask(df[QUERY] == s_query, new_value)
print (df)
  query   suggested
0    al     alabama
1    ca  california
2    de    delaware

使用loc的解决方案可以简化删除()（如果只有一个条件）并删除[]（如果只有一列）：

#for updating existing column 
df.loc[df[QUERY] == s_query, SUGGESTED] = new_value
print (df)
  query   suggested
0    al     alabama
1    ca  california
2    de    delaware

#same for creating new column
df.loc[df[QUERY] == s_query, SUGGESTED] = new_value
print (df)
  query suggested
0    al       NaN
1    ca       NaN
2    de  delaware

如果需要替换NaN，则不匹配：

#same for creating and updating existing column
df[SUGGESTED] = np.where(df[QUERY] == s_query, new_value, np.nan)
print (df)
  query suggested
0    al       nan
1    ca       nan
2    de  delaware

熊猫分配效率

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