填写缺失的数据 pandas [英] Fill in missing data pandas

问题描述

如何在此日期范围内填写缺失的数据.

How can I fill in the missing data in this dateframe.

未进行销售的天数缺少值.我该如何填写某商品在特定商店和日期售出的0天中的缺失值?

Missing values for days when no sales are made. How can I fill in the missing values for days where 0 of an item were sold at a particular store and date?

输入

Dates            Store            Item        Sales           
2017-01-01       Chicago          Apple       10
2017-01-02       NewYork          Pear        10 
2017-01-03       Chicago          Apple       10

输出

Dates            Store            Item        Sales           
2017-01-01       Chicago          Apple       10
2017-01-01       Chicago          Pear        0
2017-01-02       Chicago          Apple       0
2017-01-02       Chicago          Pear        0
2017-01-03       Chicago          Apple       10
2017-01-03       Chicago          Pear        0    
2017-01-01       NewYork          Apple       0
2017-01-01       NewYork          Pear        0 
2017-01-02       NewYork          Apple       0 
2017-01-02       NewYork          Pear        10 
2017-01-03       NewYork          Apple       0 
2017-01-03       NewYork          Pear        0 

推荐答案

使用:

• 首先 set_index 用于Multiindex
• 创建新的Multiindex from_product
• reindex 并添加0缺少值
• 最后排序级别Store，由 sort_index 和 reset_index

• first set_index for Multiindex
• create new Multiindex from_product
• reindex and add 0 for missing values
• last sort level Store by sort_index and reset_index

df = df.set_index(['Dates','Store','Item'])
mux = pd.MultiIndex.from_product(df.index.levels, names=df.index.names)
df = df.reindex(mux, fill_value=0).sort_index(level='Store').reset_index()
print (df)
        Dates    Store   Item  Sales
0  2017-01-01  Chicago  Apple     10
1  2017-01-01  Chicago   Pear      0
2  2017-01-02  Chicago  Apple      0
3  2017-01-02  Chicago   Pear      0
4  2017-01-03  Chicago  Apple     10
5  2017-01-03  Chicago   Pear      0
6  2017-01-01  NewYork  Apple      0
7  2017-01-01  NewYork   Pear      0
8  2017-01-02  NewYork  Apple      0
9  2017-01-02  NewYork   Pear     10
10 2017-01-03  NewYork  Apple      0
11 2017-01-03  NewYork   Pear      0

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