列拆分为两个单独的列

Question

I am trying to split my Period column into two different column. First will be quarter and second will be its corresponding year.

import pandas as pd
df = pd.DataFrame({'Period': ["Q3'16", "Q1'17", "Q2'17","Q3'17"]})

dtype: Object

Result should look like this:

df = pd.Dataframe({'Quarter': ['Q3', 'Q1', 'Q2','Q3'],
'Year': ['2016','2017','2017','2017']})

Since this doesn't belong to any timestamp format. I am having some difficulties figuring this out.

For reference my original df looks like this. And the column is object type.

Answer 1

Use Series.str.extract with convert periods to datetimes and extract year :

dates = pd.to_datetime(df['Period'].replace(r"(Q\d)'(\d+)", r'\2-\1', regex=True))

df['Quarter'] = df['Period'].str.extract(r"(Q\d)")
df['Year']  = dates.dt.strftime('%Y')

---

Or if all years are greater like 2000 use str.extract :

df['Quarter'] = df['Period'].str.extract(r"(Q\d)")
df['Year']  = '20' + df['Period'].str.extract(r"'(\d+)")

Or solution with Series.str.split :

s = df['Period'].str.split("'")
df['Quarter'] = s.str[0]
df['Year']  = '20' + s.str[1]

Alternative:

df[['Quarter','Year']] = df['Period'].str.split("'", expand=True)
df['Year']  = '20' + df['Year']

---

print (df)
  Period Quarter  Year
0  Q3'16      Q3  2016
1  Q1'17      Q1  2017
2  Q2'17      Q2  2017
3  Q3'17      Q3  2017
    

Answer 2

import pandas as pd
df = pd.DataFrame({'Period': ["Q3'16", "Q1'17", "Q2'17","Q3'17"]})
df = pd.DataFrame(df.Period.str.split("'",1).tolist(),
                             columns = ['Quarter','Year'])
df["Year"] = "20"+df["Year"] 
print(df)