如何将值拆分为列中的 int 和 string 并计算持续时间

Question

In my dataframe, there are several columns. One of them is based on time duration. While some of them is based on hours and others are based on minutes.

Like

2.5 hours
1 hours
41 minutes
42 hours
...

The column info is here

content_duration     3683 non-null   object

I want to convert all datas as minute values and their column are based on float or int type.

I wrote a function to perform this process but there is a problem to convert string to int.

How can I fix the issue?

Here is my code snippet shown below.

def convertminutes(column):
    value = column.str.split(' ').str[0].astype(float)
    timetype = column.str.split(' ').str[1]
    for i in range(len(timetype)):
        if timetype[i] == "hours" or timetype[i] == "hour":
            value[i] = value[i] * 60
        else:
            value[i] = value[i]        
    return value 

I Called the function in this code shown below.

df["content_duration"] = convertminutes(df["content_duration"])

Here is the screenshot

Answer 1

Try this:

import pandas as pd

def convert_to_minutes(x):
    return float(x.split()[0]) * 60 if 'hour' in x else float(x.split()[0])

df = pd.DataFrame({'content_duration': ['1 hour', '2.5 hours', '18 minutes','3 hours', '12 hours', '16 minutes', ]})
df['content_duration'] = df['content_duration'].apply(convert_to_minutes)
print(df)

Output:

   content_duration
0              60.0
1             150.0
2              18.0
3             180.0
4             720.0
5              16.0

Answer 2

Here's my attempt:

def convertminutes(column):
    map_dict = {"hours": 60, "hour": 60, "minute": 1, "minutes": 1}
    expanded_df = column.str.split(' ', expand=True)
    expanded_df[1] = copy_df[1].map(map_dict)
    return expanded_df[0].astype(float) * expanded_df[1]

Not the most efficient route, but it works.