<p>测向</p>
<pre><code> id price type
0 easdca Rs.1,599.00 was trasn by you unknown
1 vbbngy txn of INR 191.00 using unknown
2 awerfa Rs.190.78 credits was used by you unknown
3 zxcmo5 DLR.2000 credits was used by you unknown
</code></pre>
<p>df2型</p>
<pre><code> price type
0 190.78 food
1 191.00 movie
2 2,000 football
3 1,599.00 basketball
</code></pre>
<p>使用<code>re</code></p>
<pre><code>df['price_'] = df['price'].apply(lambda x: re.findall(r'(?<=[\.\s])[\d\.]+',x.replace(',',''))[0])
df2.columns = ['price_','type']
df2['price_'] = df2['price_'].str.repalce(',','')
</code></pre>
<p>将类型更改为浮动</p>
<pre><code>df2['price_'] = df2['price_'].astype(float)
df['price_'] = df['price_'] .astype(float)
</code></pre>
<p>使用<code>pd.merge</code></p>
<pre><code>df = df.merge(df2, on='price_')
df.drop('type_x', axis=1)
</code></pre>
<p><strong>输出</strong></p>
<pre><code> id price price_ type_y
0 easdca Rs.1,599.00 was trasn by you 1599.00 basketball
1 vbbngy txn of INR 191.00 using 191.00 movie
2 awerfa Rs.190.78 credits was used by you 190.78 food
3 zxcmo5 DLR.2000 credits was used by you 2000 football
</code></pre>