擅长:python、mysql、java
<p>您可以执行以下操作:</p>
<pre><code>'''
First we make a artificial key column to be able to merge
We basically just substract the floating numbers from the string
And convert it to type float
'''
df1['price_key'] = df1['price'].str.replace(',', '').str.extract('(\d+\.\d+)').astype(float)
# After that we do a merge on price and price_key and drop the columns which we dont need
df_final = pd.merge(df1, df2, left_on='price_key', right_on='price', suffixes=['', '_2'])
df_final = df_final.drop(['type', 'price_key', 'price_2'], axis='columns')
</code></pre>
<p><strong>输出</strong></p>
<pre><code> id price type_2
0 easdca Rs.1,599.00 was trasn by you basketball
1 vbbngy txn of INR 191.00 using movie
2 awerfa Rs.190.78 credits was used by you food
3 zxcmo5 DLR.2000.78 credits was used by you football
</code></pre>
<p>我假设您在<code>xyz</code>表中输入了一个错误,第三个价格应该是<code>2000.78</code>,而不是<code>2000</code>。你知道吗</p>