如何在Python中相乘两个对象数据类型？

Question

App                object
Category           object
Rating            float64
Reviews             int64
Size_MBs          float64
Installs           object
Type               object
Price              object
Content_Rating     object
Genres             object
dtype: object

import pandas as pd
df=pd.read_csv('apps.csv')
clean_df=df.dropna()
clean_df.loc[:, "Installs"] = clean_df.loc[:, "Installs"].astype(str).str.replace(",", "")
clean_df.loc[:, "Installs"] = pd.to_numeric(clean_df.loc[:, "Installs"])
clean_df.loc[:,"Price"] = clean_df.loc[:,"Price"].astype(str).str.replace('$', "")
clean_df.loc[:, "Price"] = pd.to_numeric(clean_df.loc[:,"Price"])
#Line 8:  
clean_df['Revenue_Estimate']= clean_df.Installs * clean_df.Price

A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: 
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
 clean_df['Revenue_Estimate']= clean_df.Installs * clean_df.Price

 df_apps_clean['Revenue_Estimate'] = df_apps_clean.Installs.mul(df_apps_clean.Price)

A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
clean_df['Revenue_Estimate'] = clean_df.Installs.mul(clean_df.Price)

这是来自Udemy培训课程的内容（第76天）。

我刚开始学习Python，所以如果我提问的方式不太对，请多包涵。任何帮助都非常感谢。谢谢！我有一个Excel表格，里面有好几列，每一列的数据类型如下所示。第8行在Pycharm中给我报错，说我不能进行乘法运算。我的Excel表格是：apps.csv，里面有10列。请看附上的图片。

apps.csv

我想把Excel表格中的“价格”和“安装”这两列相乘，然后添加一个新列，命名为“收入估算”。

错误信息：

=============

我也试过这个，但没有成功。

错误信息：

Answer 1

import pandas as pd

df = pd.read_csv('apps.csv')
clean_df = df.dropna()

clean_df['Installs'] = clean_df['Installs'].str.replace(',', '').astype(int)
clean_df['Price'] = clean_df['Price'].str.replace('$', '').astype(float)

clean_df['Revenue_Estimate'] = clean_df['Installs'] * clean_df['Price']

试着把安装数量转换成整数，把价格转换成浮点数，这样可能会对你有帮助。

如果这样还是不行，请再提供一些详细信息。

Answer 2

把第8行改成：

clean_df.insert(10, "Revenue Estimate", clean_df.loc[:, "Price"] * clean_df.loc[:, "Installs"], True)

这里的10是指我想在“收入估算”这个名字下面添加的那一列。