熊猫：如何填写缺少的“年，周”列？

Question

I have a dataframe with [Year] & [Week] columns sometimes missing. I have another dataframe that is a calendar for reference from which I can get these missing values. How to fill these missing columns using pandas?

I have tried using reindex to set them up, but I am getting the following error

ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

import pandas as pd

d1 = {'Year': [2019,2019,2019,2019,2019], 'Week':[1,2,4,6,7], 'Value': 
[20,40,60,75,90]}
d2 = {'Year': [2019,2019,2019,2019,2019,2019,2019,2019,2019,2019], 'Week':[1,2,3,4,5,6,7,8,9,10]}

df1 = pd.DataFrame(data=d1)
df2 = pd.DataFrame(data=d2)

df1 = df1.set_index(['Year', 'Week'])
df2 = df2.set_index(['Year', 'Week'])

df1 = df1.reindex(df2, fill_value=0)

print(df1)

Answer 1

You should adding index so df2.index

df1.reindex(df2.index,fill_value=0)
Out[851]: 
           Value
Year Week       
2019 1        20
     2        40
     3         0
     4        60
     5         0
     6        75
     7        90

---

df2.index.difference(df1.index)
Out[854]: 
MultiIndex(levels=[[2019], [3, 5]],
           labels=[[0, 0], [0, 1]],
           names=['Year', 'Week'],
           sortorder=0)

---

Update

s=df1.reindex(df2.index)
s[s.bfill().notnull().values].fillna(0)
Out[877]: 
           Value
Year Week       
2019 1      20.0
     2      40.0
     3       0.0
     4      60.0
     5       0.0
     6      75.0
     7      90.0

Answer 2

import pandas as pd

d1 = {'Year': [2019,2019,2019,2019,2019], 'Week':[1,2,4,6,7], 'Value': 
[20,40,60,75,90]}
d2 = {'Year': [2019,2019,2019,2019,2019,2019,2019], 'Week':[1,2,3,4,5,6,7]}

df1 = pd.DataFrame(data=d1)
df2 = pd.DataFrame(data=d2)

df1 = df1.set_index(['Year', 'Week'])
df2 = df2.set_index(['Year', 'Week'])

fill_value = df1['Value'].mean() #value to fill `NaN` rows with - can choose another logic if you do not want the mean
df1 = df1.join(df2, how='right')


df1.fillna(value=fill_value,axis=1) # Fill missing data here
print(df1)