在pandas.DataFrame中向缺少的数据插入零

Question

I have a following kind of pandas.DataFrame:

sales_with_missing = pd.DataFrame({'month':[1,2,3,6,7,8,9,10,11,12],'code':[111]*10,  'sales':[np.random.randint(1500) for _ in np.arange(10)]})

You can see records for April and May are missing, and I'd like to insert sales as zero for those missing records:

sales = insert_zero_for_missing(sales_with_missing)
print(sales)

How can I implement the insert_zero_for_missing method?

Answer 1

• Set the month as the index,
• reindex to add rows for the missing months,
• call fillna to fill the missing values with zero, and then
• reset the index (to make month a column again):

---

import numpy as np
import pandas as pd

month = list(range(1,4)) + list(range(6,13))
sales = np.array(month)*100
df = pd.DataFrame(dict(month=month, sales=sales))
print(df.set_index('month').reindex(range(1,13)).fillna(0).reset_index())

yields

    month  sales
0       1    100
1       2    200
2       3    300
3       4      0
4       5      0
5       6    600
6       7    700
7       8    800
8       9    900
9      10   1000
10     11   1100
11     12   1200

Answer 2

# create a series of all months
all_months = pd.Series(data = range(1 , 13))
# get all missing months from your data frame in this example it will be 4 & 5
missing_months = all_months[~all_months.isin(sales_with_missing.month)]
# create a new data frame of missing months , it will be used in the next step to be concatenated to the original data frame
missing_df = pd.DataFrame({'month' : missing_months.values , 'code' : 111 , 'sales' : 0})

Out[36]:
code    month   sales
111        4    0
111        5    0
# then concatenate both data frames
pd.concat([sales_with_missing , missing_df]).sort_index(by = 'month')

Out[39]:
code    month   sales
111        1    1028
111        2    1163
111        3    961
111        4    0
111        5    0
111        6    687
111        7    31
111        8    607
111        9    1236
111        10   0863
111        11   11233
111        12   2780