如何对数据子集应用前向填充以确保每月数据连续？

问题描述 投票：0回答：1

我有一个以下 df，需要完成以下标准，

假设：今天是 2023 年 12 月

标准：

  1. A1-sup1 于 2023 年 1 月提交，下一次提交是在 2023 年 4 月，因为缺少 2 月和 3 月 - 需要复制 1 月的所有数据作为向前填充到 2023 年 2 月和 2023 年 3 月
  2. A1-sup1 于 2023 年 4 月提交，直到今天，即 2023 年 12 月才提交，因此将 4 月的所有数据复制为前向填充至 5 月、6 月、7 月、8 月、9 月、10 月、11 月、12 月
  3. A2-sup2 于 10 月提交，直到今天（即 2023 年 12 月）才提交，因此将 4 月的所有数据复制为远期填充到 11 月、12 月

输入数据框如下，

import pandas as pd

input_data = [
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Jan-2023","cost": 100},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Feb-2023","cost": 105},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Mar-2023","cost": 108},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 106},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Oct-2023","cost": 101},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Nov-2023","cost": 102},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Dec-2023","cost": 109},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Jan-2024","cost": 104},
]

# Create the DataFrame
input_data = pd.DataFrame(input_data)

# Print the DataFrame (optional)
print(input_data.shape)
input_data

预期结果，

import pandas as pd

output_data = [
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Jan-2023","cost": 100},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Feb-2023","cost": 105},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Mar-2023","cost": 108},
{"submissionmonth": "Jan-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 106},
{"submissionmonth": "Feb-2023", "sku": "A1","location": "sup1","forecastmonth": "Jan-2023","cost": 100},
{"submissionmonth": "Feb-2023", "sku": "A1","location": "sup1","forecastmonth": "Feb-2023","cost": 105},
{"submissionmonth": "Feb-2023", "sku": "A1","location": "sup1","forecastmonth": "Mar-2023","cost": 108},
{"submissionmonth": "Feb-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 106},
{"submissionmonth": "Mar-2023", "sku": "A1","location": "sup1","forecastmonth": "Jan-2023","cost": 100},
{"submissionmonth": "Mar-2023", "sku": "A1","location": "sup1","forecastmonth": "Feb-2023","cost": 105},
{"submissionmonth": "Mar-2023", "sku": "A1","location": "sup1","forecastmonth": "Mar-2023","cost": 108},
{"submissionmonth": "Mar-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 106},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Apr-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "May-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "May-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "May-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "May-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Jun-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Jun-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Jun-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Jun-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Jul-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Jul-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Jul-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Jul-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Aug-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Aug-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Aug-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Aug-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Sept-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Sept-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Sept-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Sept-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Oct-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Oct-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Oct-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Oct-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Oct-2023","cost": 101},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Nov-2023","cost": 102},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Dec-2023","cost": 109},
{"submissionmonth": "Oct-2023", "sku": "A2","location": "sup2","forecastmonth": "Jan-2024","cost": 104},
{"submissionmonth": "Nov-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Nov-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Nov-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Nov-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Nov-2023", "sku": "A2","location": "sup2","forecastmonth": "Oct-2023","cost": 101},
{"submissionmonth": "Nov-2023", "sku": "A2","location": "sup2","forecastmonth": "Nov-2023","cost": 102},
{"submissionmonth": "Nov-2023", "sku": "A2","location": "sup2","forecastmonth": "Dec-2023","cost": 109},
{"submissionmonth": "Nov-2023", "sku": "A2","location": "sup2","forecastmonth": "Jan-2024","cost": 104},
{"submissionmonth": "Dec-2023", "sku": "A1","location": "sup1","forecastmonth": "Apr-2023","cost": 101},
{"submissionmonth": "Dec-2023", "sku": "A1","location": "sup1","forecastmonth": "May-2023","cost": 102},
{"submissionmonth": "Dec-2023", "sku": "A1","location": "sup1","forecastmonth": "Jun-2023","cost": 109},
{"submissionmonth": "Dec-2023", "sku": "A1","location": "sup1","forecastmonth": "Jul-2023","cost": 104},
{"submissionmonth": "Dec-2023", "sku": "A2","location": "sup2","forecastmonth": "Oct-2023","cost": 101},
{"submissionmonth": "Dec-2023", "sku": "A2","location": "sup2","forecastmonth": "Nov-2023","cost": 102},
{"submissionmonth": "Dec-2023", "sku": "A2","location": "sup2","forecastmonth": "Dec-2023","cost": 109},
{"submissionmonth": "Dec-2023", "sku": "A2","location": "sup2","forecastmonth": "Jan-2024","cost": 104},
]

# Create the DataFrame
output_data = pd.DataFrame(output_data)

# Print the DataFrame (optional)
print(output_data.shape)
output_data
python pandas ffill
1个回答
0
投票

使用

to_dict(orient='records')
中的
pandas
方法。希望这对您有帮助。

import pandas as pd

input_data = {
    'submissionmonth': ['Jan 2023', 'Jan 2023', 'Jan 2023', 'Jan 2023', 'Apr 2023', 'Apr 2023', 'Apr 2023', 'Apr 2023', 'Oct 2023', 'Oct 2023', 'Oct 2023', 'Oct 2023'],
    'sku': ['A1', 'A1', 'A1', 'A1', 'A1', 'A1', 'A1', 'A1', 'A2', 'A2', 'A2', 'A2'],
    'location': ['sup1', 'sup1', 'sup1', 'sup1', 'sup1', 'sup1', 'sup1', 'sup1', 'sup2', 'sup2', 'sup2', 'sup2'],
    'forecastmonth': ['Jan 2023', 'Feb 2023', 'Mar 2023', 'Apr 2023', 'Apr 2023', 'May 2023', 'Jun 2023', 'Jul 2023', 'Oct 2023', 'Nov 2023', 'Dec 2023', 'Jan 2024'],
    'cost': [100, 105, 108, 106, 101, 102, 109, 104, 101, 102, 109, 104]
}

df = pd.DataFrame(input_data)
data_list = df.to_dict(orient='records')
print(data_list)
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