Python 如何使用pandas取消数据帧透视
例如,我有这样一个数据帧Python 如何使用pandas取消数据帧透视,python,pandas,dataframe,melt,Python,Pandas,Dataframe,Melt,例如,我有这样一个数据帧 df = {'name':['Jennifer','Vivian','Trisha'], 'married':[1,1,0], 'Mon': [0, 0,1], 'Tu':[1,0,0], 'Wed':[0,1,0]} 如何将虚拟变量融为一列,如下所示: 我尝试使用pd.melt(),但它只是将几列堆叠成一列,并更改列的长度。有人能帮我吗?提前谢谢你 这是一种方法 res = pd.melt(df, id_vars=['ma
df = {'name':['Jennifer','Vivian','Trisha'],
'married':[1,1,0],
'Mon': [0, 0,1],
'Tu':[1,0,0],
'Wed':[0,1,0]}
如何将虚拟变量融为一列,如下所示:
我尝试使用pd.melt(),但它只是将几列堆叠成一列,并更改列的长度。有人能帮我吗?提前谢谢你 这是一种方法
res = pd.melt(df, id_vars=['married', 'name'], value_vars=['Mon', 'Tu', 'Wed'],
var_name='Workday')
res = res[res['value'] == 1].reset_index(drop=True)
d = {0: 'single', 1: 'married'}
res['married'] = res['married'].map(d)
print(res)
# married name Workday value
# 0 single Trisha Mon 1
# 1 married Jennifer Tu 1
# 2 married Vivian Wed 1
这应该起作用:
df.replace({'married':{1:'Married', 0: 'Single'}}). \
melt(id_vars=['married', 'name'], var_name='Workday'). \
query('value == 1'). \
drop('value', axis=1)
# married name Workday
# 2 Single Trisha Mon
# 3 Married Jennifer Tu
# 7 Married Vivian Wed