如何替换包含 Python Panda 中某些单词的单元格中的值，就像在 Excel 中一样？

Question

I have my data as

Age	Gender
2 Year(s)	Male
3 Month(s)	Male
2 Day (s)	Female

And I want to make it to

Age	Gender
2	Male
Below 1 year	Male
Below 1 year	Female

Answer 1

Use the replace function. In your case:

df.Age.replace({
    "2 Year(s)": "2",
    "3 Month(s)": "Below 1 year",
    ...
    },
    inplace=True
)

Answer 2

Figured it out, I did this, I copy pasted the actual code from my code editor

#split Patient Age and append back to master list
alldata[['Age', 'Unit']] = alldata['Patient Age'].str.split(' ', expand=True)

#Separate into subdata of Years, Months, Days and Hours
dfunit = alldata.groupby(['Unit'])
dfyears = dfunit.get_group('Year(s)')
dfmonths = dfunit.get_group('Month(s)')
dfdays = dfunit.get_group('Day(s)')
dfhours = dfunit.get_group('Hour(s)')


#Replace value of Mpnths, Days and Hours with 0
dfmonths['Age'] = dfmonths['Age'].str.replace('\d', '0')
dfdays['Age'] = dfdays['Age'].str.replace('\d', '0')
dfhours['Age'] = dfhours['Age'].str.replace('\d', '0')


#Combine sublist back to master list
newdata = dfyears.append(dfmonths).append(dfdays).append(dfhours)

#Convert value in Age to int
newdata['Age'] = newdata['Age'].astype(int)
newdata['Age'].dtypes

#Separate Age into Age Group
bins = [0, 20, 30, 40, 50, 60, 70, 120]
labels = ['0-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70+']
newdata['Agerange'] = pd.cut(newdata['Age'], bins, labels = labels,include_lowest = True)