Price column object to int in pandas

You can set it to Int by:

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

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

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

Assuming your column name is amount, here is what you should do:

dataframe['amount'] = dataframe.amount.str.replace('$|.|,', '').astype(int)

You can use Series.replace or Series.str.replace with Series.astype:

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

dataframe['amount'] = dataframe['amount'].astype(int)

print (dataframe)
   amount
0  309244
1  309244

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

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

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

dataframe["amount"] = dataframe["amount"].str.replace('[$,.]', '').astype(int)

        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

car_sales["Price"].dtype
output-dtype('O')

car_sales["Price"]=car_sales["Price"].str.replace('[$,.]', '').astype(int)
car_sales["Price"]

output:

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

This should be simple, just by replacing $, commas(,), and decimals (. dots) with nothing ('') and removing extra zeros, it would work.

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

I think using lambda and ignoring $ is also better solution

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

To avoid extra ZEROs while converting object to int. you should convert the object ($3,092.440) to float using following code:

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

If you want to convert a price into string then you can use the below method:

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

Here is a simple way to do it:

cars["amount"] = cars["amount"].str.replace("$" , "").str.replace("," , "").astype("float").astype("int")

1. First you remove the dollar sign
2. Next you remove the comma
3. 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!
4. Finally you convert the column to integer

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

Try with this one:

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

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:

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 should work:

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

The original data behind this code is:

The output data is:

Hope this was useful!

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)