import pandas as pd
    
pd.read_csv( car-sales.csv )
    
car_sales[ Price ]=car_sales[ Price ].str.replace( $ ,  ,regex=False).str.replace( , ,  ,regex=False).astype(float).astype(int)
# Initially the code removes all the dollar signs and commas.
# Then it converts the string type values into float type values (the code can t directly convert the string type values into int type values).
# And then finally it converts the float type values into int type values

该代码背后的原始数据是:。

产出数据为:。

您可以通过下列方式使:

df[ amount ] = df[ amount ].astype(np.int)

如果你想把沙尔语提上“Int”一栏,则使用:

#assuming you re reading from a file
pd.read_csv(file_name, dtype={ amount :np.int32})

还将开展工作:dframe.amount.str.replace(“$””)astype(int)。

这应当简单易懂,仅用“$, commas(,)和 decimals(. dots)替换成零件()并删除多余零件。

your_column_name = your_column_name.str.replace( [$,]|.d* ,   ).astype(int)

我认为,使用“拉姆布达”和无视美元也是更好的解决办法。

dollarizer = lambda x: float(x[1:-1])
dataframe.amount = dataframe.amount.apply(dollarizer)

为了避免在将物体转换为暗中时添加ZEROs,请将物体(3 092.440美元)改为使用以下代码浮动:

Syntax:

your_dataframe["your_column_name"] = your_dataframe["your_column_name"].str.replace( [$,] ,   ).astype(float)

<>>Example:

car_sales["Price"] = car_sales["Price"].replace( [$,] ,   ).astype(float)

Result:

4000.0

dataframe["amount"] = dataframe["amount"].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

export_car_sales["Price"] = export_car_sales["Price"].replace( [$,.] ,   , regex=True).astype(int)

引证:

car_sales["Price"] = car_sales["Price"].str.replace( [$,]|.d* ,   ).astype(int)

但是,为了消除即将产生的额外零,你必须将其分成100个,这样,你才能执行这一补充指示:

car_sales["Price"]=car_sales["Price"].apply(lambda x: x/100)

In the above code we have to use float instead of integer so that the cent value would be remain as cents.

df[ Price ] = df[ Price ].str.replace( [$,] ,  ).astype(float)

This worked for me

car_sales = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales.csv")
car_sales["Price"] = car_sales["Price"].replace("[$,.]", "", regex=True).map(lambda x: str(x)[:-2]).astype(int)
car_sales

car_sales["Price"] = car_sales["Price"].replace( [$,] ,   , regex=True).astype(float)

There are two ways to solve this problem, using or not using regex, which is a parameter that specifies whether or not you will use a regular expression, in our case ("[$,.]"). In the first case you will not use regex, which makes the code a little more verbose:

data_frame[ Price ] = data_frame[ Price ].str.replace( $ ,   ).str.replace( , ,   ).str.replace( . ,   )

data_frame["Price"] = data_frame["Price"].astype(int)

在第二种情况下,我们使用reg,但我们需要启动并使用常规表述:

data_frame["Price"] = data_frame["Price"].str.replace( [$,.] ,   , regex=True).astype(int)

汽车["Price"] = 汽车["Price"].replace( [$,] ,   , regex=True).astype(float).astype(int)

汽车