Pandas：将一列中的值分隔为多行

Question

If a column contains several values, separated by , , how can I separate them in different rows?

The sample data sets:

name   age      taskID
----------------------------
AA      20      T01,T02
BB      22      T03,T02,T03
CC      24      T01,T05
DD      21      T02,T06 

Output:

name   age      taskID
-----------------------
AA      20      T01
AA      20      T02
BB      22      T03
BB      22      T02
CC      24      T01
CC      24      T05
CC      24      T03
DD      21      T02 
DD      21      T06

Answer 1

for pandas 0.25 and above you can use

df = pd.DataFrame([['AA', '20', 'T01,T02'], ['BB', '22', 'T03,T02,T03'], ['CC', '24', 'T01,T05'], ['DD', '21', 'T02,T06']], columns=('name', 'age', 'taskID'))

df["taskID"] = df["taskID"].str.split(",")
df.explode("taskID")

for pandas below 0.25

from itertools import chain
import numpy as np
import pandas as pd

df = pd.DataFrame([['AA', '20', 'T01,T02'], ['BB', '22', 'T03,T02,T03'], ['CC', '24', 'T01,T05'], ['DD', '21', 'T02,T06']], columns=('name', 'age', 'taskID'))
df["taskID"] = df["taskID"].str.split(",")

arr = np.repeat(df.iloc[:,:-1].values, df["taskID"].apply(len), axis=0)
df2 = pd.DataFrame(arr, columns=df.columns[:-1])
df2["TaskID"] = list(chain(*df["taskID"]))

df2