blocks|key|3225179|text|在regex中，\D的意思不是数字..。所以我们可以使用pd.Series.str.replace|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3225180|dataframe.amount.replace('\D',+'',+regex=True).astype(int)

0++++309244
1++++309244
Name:+amount,+dtype:+int64|code-block|syntax|javascript|3225181|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.replace.html^0|8|2|S|L|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

in regex <code>\D</code> means not digit... so we can use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.replace.html" rel="noreferrer"><code>pd.Series.str.replace</code></a>

<pre><code>dataframe.amount.replace('\D', '', regex=True).astype(int)

0 309244
1 309244
Name: amount, dtype: int64
</code></pre>

blocks|key|4215566|text|可以将Series.replace或Series.str.replace与Series.astype结合使用。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4215567|dataframe+=+pd.DataFrame(data={'amount':['$3,092.44',+'$3,092.44']})
print+(dataframe)
++++++amount
0++$3,092.44
1++$3,092.44

dataframe['amount']+=+dataframe['amount'].replace('[\$\,\.]',+'',+regex=True).astype(int)

print+(dataframe)
+++amount
0++309244
1++309244|code-block|syntax|javascript|4215568|dataframe['amount']+=+dataframe['amount'].astype(int)

print+(dataframe)
+++amount
0++309244
1++309244|4215569|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.replace.html|1|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.replace.html|2|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html^0|3|E|I|I|11|D|3|E|0|I|I|1|11|D|2|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]|$9|13|A|14|B|C]]|D|@$9|15|A|16|1|17]|$9|18|A|19|1|1A]|$9|1B|A|1C|1|1D]]|E|$]]|$1|F|3|G|5|H|7|1E|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|H|7|1F|8|@]|D|@]|E|$I|J]]|$1|M|3|-4|5|6|7|1G|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]|U|$5|P|Q|R|E|$S|V]]|W|$5|P|Q|R|E|$S|X]]]]

You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.replace.html" rel="noreferrer"><code>Series.replace</code></a> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.replace.html" rel="noreferrer"><code>Series.str.replace</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html" rel="noreferrer"><code>Series.astype</code></a>:

<pre><code>dataframe = pd.DataFrame(data={'amount':['$3,092.44', '$3,092.44']})
print (dataframe)
 amount
0 $3,092.44
1 $3,092.44

dataframe['amount'] = dataframe['amount'].replace('[\$\,\.]', '', regex=True).astype(int)

print (dataframe)
 amount
0 309244
1 309244
</code></pre>

<hr>

<pre><code>dataframe['amount'] = dataframe['amount'].astype(int)

print (dataframe)
 amount
0 309244
1 309244
</code></pre>

blocks|key|3802149|text|这就是你如何做到这一点，同时也丢弃了美分：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3802150|car_sales["Price"]+=+car_sales["Price"].str.replace('[\$\,]%7C\.\d*',+'').astype(int)|code-block|syntax|javascript|3802151|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is how you do it while also discarding the cents:

<pre><code>car_sales["Price"] = car_sales["Price"].str.replace('[\$\,]|\.\d*', '').astype(int)
</code></pre>

blocks|key|3225238|text|dataframe["amount"]+=+dataframe["amount"].str.replace('[\$\,\.]',+'').astype(int)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|3225239|++++++++Make+++++Colour++++Odometer+(KM)++++++++Doors++++++++++++++Price
0+++++++Toyota+++White+++++150043++++++++++++++++4+++++++++++++++++$4,000.00
1+++++++Honda++++Red+++++++87899+++++++++++++++++4+++++++++++++++++$5,000.00
2+++++++Toyota+++Blue++++++32549+++++++++++++++++3+++++++++++++++++$7,000.00
3+++++++BMW++++++Black+++++11179+++++++++++++++++5+++++++++++++++++$22,000.00
4+++++++Nissan+++White+++++213095++++++++++++++++4+++++++++++++++++$3,500.00
5+++++++Toyota+++Green+++++99213+++++++++++++++++4+++++++++++++++++$4,500.00
6+++++++Honda++++Blue++++++45698+++++++++++++++++4+++++++++++++++++$7,500.00
7+++++++Honda++++Blue++++++54738+++++++++++++++++4+++++++++++++++++$7,000.00
8+++++++Toyota+++White+++++60000+++++++++++++++++4+++++++++++++++++$6,250.00
9+++++++Nissan+++White+++++31600+++++++++++++++++4+++++++++++++++++$9,700.00|3225240|car_sales["Price"].dtype
output-dtype('O')

car_sales["Price"]=car_sales["Price"].str.replace('[\$\,\.]',+'').astype(int)
car_sales["Price"]|3225241|产出：|unstyled|3225242|0+++++400000
1+++++500000
2+++++700000
3++++2200000
4+++++350000
5+++++450000
6+++++750000
7+++++700000
8+++++625000
9+++++970000
Name:+Price,+dtype:+int32|3225243|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|6|7|P|8|@]|9|@]|A|$B|C]]|$1|F|3|G|5|6|7|Q|8|@]|9|@]|A|$B|C]]|$1|H|3|I|5|J|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$B|C]]|$1|M|3|-4|5|J|7|T|8|@]|9|@]|A|$]]]|N|$]]

<pre><code>dataframe["amount"] = dataframe["amount"].str.replace('[\$\,\.]', '').astype(int)
</code></pre>

<pre><code> Make Colour Odometer (KM) Doors Price
0 Toyota White 150043 4 $4,000.00
1 Honda Red 87899 4 $5,000.00
2 Toyota Blue 32549 3 $7,000.00
3 BMW Black 11179 5 $22,000.00
4 Nissan White 213095 4 $3,500.00
5 Toyota Green 99213 4 $4,500.00
6 Honda Blue 45698 4 $7,500.00
7 Honda Blue 54738 4 $7,000.00
8 Toyota White 60000 4 $6,250.00
9 Nissan White 31600 4 $9,700.00
</code></pre>

<pre><code>car_sales["Price"].dtype
output-dtype('O')

car_sales["Price"]=car_sales["Price"].str.replace('[\$\,\.]', '').astype(int)
car_sales["Price"]
</code></pre>

output:

<pre><code>0 400000
1 500000
2 700000
3 2200000
4 350000
5 450000
6 750000
7 700000
8 625000
9 970000
Name: Price, dtype: int32
</code></pre>

blocks|key|3225124|text|假设您的列名是amount，下面是您应该做的事情：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3225125|dataframe['amount']+=+dataframe.amount.str.replace('\$%7C\.%7C\,',+'').astype(int)|code-block|syntax|javascript|3225126|entityMap^0|7|6|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Assuming your column name is <code>amount</code>, here is what you should do:

<pre><code>dataframe['amount'] = dataframe.amount.str.replace('\$|\.|\,', '').astype(int)
</code></pre>

blocks|key|4215759|text|如果要将价格转换为字符串，则可以使用以下方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4215760|car_sales["Price"]+=+car_sales["Price"].replace('[\$\,]',+'').astype(str)

car_sales["Price"]

0+++++400000
1+++++500000
2+++++700000
3++++2200000
4+++++350000
5+++++450000
6+++++750000
7+++++700000
8+++++625000
9+++++970000
Name:+Price,+dtype:+object|code-block|syntax|javascript|4215761|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

If you want to convert a price into string then you can use the below method:
<pre><code>car_sales[&quot;Price&quot;] = car_sales[&quot;Price&quot;].replace('[\$\,]', '').astype(str)

car_sales[&quot;Price&quot;]

0 400000
1 500000
2 700000
3 2200000
4 350000
5 450000
6 750000
7 700000
8 625000
9 970000
Name: Price, dtype: object
</code></pre>

blocks|key|3802349|text|下面是一个简单的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3802350|cars["amount"]+=+cars["amount"].str.replace("$"+,+"").str.replace(","+,+"").astype("float").astype("int")|code-block|syntax|javascript|3802351|首先，您删除美元标志。|ordered-list-item|3802352|接下来，删除逗号。|3802353|然后将列转换为浮动。如果您试图将列直接转换为整数，您将得到以下错误:只能对字符串值使用.str访问器！|3802354|最后，将列转换为整数。|3802355|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|S|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|I|7|T|8|@]|9|@]|A|$]]|$1|J|3|K|5|I|7|U|8|@]|9|@]|A|$]]|$1|L|3|M|5|I|7|V|8|@]|9|@]|A|$]]|$1|N|3|O|5|I|7|W|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|Q|$]]

Here is a simple way to do it:
<pre><code>cars[&quot;amount&quot;] = cars[&quot;amount&quot;].str.replace(&quot;$&quot; , &quot;&quot;).str.replace(&quot;,&quot; , &quot;&quot;).astype(&quot;float&quot;).astype(&quot;int&quot;)
</code></pre>
<ol>
<li>First you remove the dollar sign</li>
<li>Next you remove the comma</li>
<li>Then you convert the column to float. If you try to convert the column straight to integer, you will get the following error: Can only use .str accessor with string values!</li>
<li>Finally you convert the column to integer</li>
</ol>

blocks|key|4215450|text|您可以通过以下方式将其设置为Int：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4215451|df['amount']+=+df['amount'].astype(np.int)|code-block|syntax|javascript|4215452|如果您想让Python首先将该列读入Int，请使用：|4215453|#assuming+you're+reading+from+a+file
pd.read_csv(file_name,+dtype={'amount':np.int32})|4215454|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

You can set it to Int by:

<pre><code>df['amount'] = df['amount'].astype(np.int)
</code></pre>

If you want to tell Python to read the column as Int at first place, use:

<pre><code>#assuming you're reading from a file
pd.read_csv(file_name, dtype={'amount':np.int32})
</code></pre>

blocks|key|3802192|text|这也是可行的：dframe.amount.str.replace("$","").astype(int)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3802193|entityMap^0|7|19|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

This will also work: <code>dframe.amount.str.replace("$","").astype(int)</code>

blocks|key|3225248|text|这应该很简单，只要将$、逗号(,)和小数(.点)替换为nothing+('')并去掉额外的零，就可以了。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3225249|your_column_name+=+your_column_name.str.replace('[\$\,]%7C\.\d*',+'').astype(int)|code-block|syntax|javascript|3225250|entityMap^0|A|1|F|1|L|1|10|2|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|L|$]]

This should be simple, just by replacing <code>$</code>, commas(<code>,</code>), and decimals (<code>.</code> dots) with nothing (<code>''</code>) and removing extra zeros, it would work.
<pre><code>your_column_name = your_column_name.str.replace('[\$\,]|\.\d*', '').astype(int)
</code></pre>

blocks|key|4215708|text|我认为使用lambda和忽略$也是更好的解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4215709|dollarizer+=+lambda+x:+float(x[1:-1])
dataframe.amount+=+dataframe.amount.apply(dollarizer)|code-block|syntax|javascript|4215710|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I think using lambda and ignoring $ is also better solution
<pre><code>dollarizer = lambda x: float(x[1:-1])
dataframe.amount = dataframe.amount.apply(dollarizer)
</code></pre>

blocks|key|4215720|text|在将对象转换为int时避免额外的零。您应该使用以下代码将对象($3,092.440)转换为浮动：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4215721|语法：|offset|length|style|BOLD|4215722|your_dataframe["your_column_name"]+=+your_dataframe["your_column_name"].str.replace('[\$\,]',+'').astype(float)|code-block|syntax|javascript|4215723|示例：|4215724|car_sales["Price"]+=+car_sales["Price"].replace('[\$\,]',+'').astype(float)|4215725|结果：|4215726|4000.0|4215727|entityMap^0|0|0|3|0|0|0|3|0|0|0|3|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@$D|Y|E|Z|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|10|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|11|8|@$D|12|E|13|F|G]]|9|@]|A|$]]|$1|O|3|P|5|J|7|14|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|15|8|@$D|16|E|17|F|G]]|9|@]|A|$]]|$1|S|3|T|5|J|7|18|8|@]|9|@]|A|$K|L]]|$1|U|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|V|$]]

To avoid extra ZEROs while converting object to int. you should convert the object ($3,092.440) to float using following code:
Syntax:
<pre><code>your_dataframe[&quot;your_column_name&quot;] = your_dataframe[&quot;your_column_name&quot;].str.replace('[\$\,]', '').astype(float)
</code></pre>
Example:
<pre><code>car_sales[&quot;Price&quot;] = car_sales[&quot;Price&quot;].replace('[\$\,]', '').astype(float)
</code></pre>
Result:
<pre><code>4000.0
</code></pre>

blocks|key|4215731|text|dataframe["amount"]+=+dataframe["amount"].str.replace('[$,.]%7C..$','',regex=True).astype(int)

in+str.replace(...)

[$,.]+mean+find+$+,+.
%7C+mean+or

..$+mean+find+any+last+2+character

so+'[$,.]%7C..$'+mean+find+$+,+.+or+any+last+2+character|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4215732|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>dataframe[&quot;amount&quot;] = dataframe[&quot;amount&quot;].str.replace('[$,.]|..$','',regex=True).astype(int)

in str.replace(...)

[$,.] mean find $ , .
| mean or

..$ mean find any last 2 character

so '[$,.]|..$' mean find $ , . or any last 2 character
</code></pre>

blocks|key|3225372|text|export_car_sales["Price"]+=+export_car_sales["Price"].replace('[\$\,\.]',+'',+regex=True).astype(int)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|3225373|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>export_car_sales[&quot;Price&quot;] = export_car_sales[&quot;Price&quot;].replace('[\$\,\.]', '', regex=True).astype(int)
</code></pre>

blocks|key|3802399|text|试着用这个：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3802400|car_sales["Price"]+=+car_sales["Price"].str.replace('[\$\,]%7C\.\d*',+'').astype(int)|code-block|syntax|javascript|3802401|但您必须将其除以100以移除将要创建的附加零，因此您必须运行以下附加指令：|3802402|car_sales["Price"]=car_sales["Price"].apply(lambda+x:+x/100)|3802403|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Try with this one:
<pre><code>car_sales[&quot;Price&quot;] = car_sales[&quot;Price&quot;].str.replace('[\$\,]|\.\d*', '').astype(int)
</code></pre>
but you have to divide it by 100 to remove the additional zeros that are going to be created, so you will have to run this additional instruction:
<pre><code>car_sales[&quot;Price&quot;]=car_sales[&quot;Price&quot;].apply(lambda x: x/100)
</code></pre>

blocks|key|3225432|text|在上面的代码中，我们必须使用float而不是integer，这样分值将保持为美分。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3225433|df['Price']+=+df['Price'].str.replace('[\$\,]','').astype(float)|code-block|syntax|javascript|3225434|entityMap^0|E|5|M|7|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

In the above code we have to use <code>float</code> instead of <code>integer</code> so that the cent value would be remain as cents.
<pre><code>df['Price'] = df['Price'].str.replace('[\$\,]','').astype(float)
</code></pre>

I have a column called amount with holds values that look like this: $3,092.44 when I do <code>dataframe.dtypes()</code> it returns this column as an object how can i convert this column to type int?

Price column object to int in pandas

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个名为amount的列，其包含的值如下所示：$3,092.44当我执行dataframe.dtypes()时，它作为对象返回该列，如何将该列转换为int类型？

问熊猫价格栏对象int
EN

回答 16

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问熊猫价格栏对象intEN

回答 16

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问熊猫价格栏对象int
EN