快速绘制值的方法-熊猫

以我的经验,整数键会导致更快的合并时间。为此,您可以映射 Val1 Val2 两个数据帧上的整数( df1 df2 )然后合并 瓦尔1 和 .

我相信有更有效的方法来绘制地图 瓦尔1 和 Val2型 但这个答案的目的是为了说明在整数上合并更快。

# Turn Val1 and Val2 to categorical dtypes
df2[['Val1','Val2']] = df2[['Val1','Val2']].apply(lambda x: pd.Categorical(x))
# Turn categories to dictionaries
d1 = dict(enumerate(df2['Val1'].cat.categories))
d2 = dict(enumerate(df2['Val2'].cat.categories))
# Reverse keys and values in each dictionary
d1 = {v:k for k,v in d1.items()}
d2 = {v:k for k,v in d2.items()}
# Replace columns in df1
df1['Val1'].replace(d1, inplace=True)
df2['Val1'].replace(d1, inplace=True)
df1['Val2'].replace(d2, inplace=True)
df2['Val2'].replace(d2, inplace=True)
# Merge by integer versions of Val1 and Val2
df2.merge(df1, on=['Val1','Val2'], how='left')

# Merge with original keys
start = time.time()
df2.merge(df1, on=['Val1','Val2'], how='left')
round(time.time() - start, 5)

0.00672

# Merge with integer keys
start = time.time()
df2.merge(df1, on=['Val1','Val2'])
round(time.time() - start, 5)

0.00485