Python Pandas - 不同日期索引的動態匹配

Question

我有兩個具有不同時間序列數據的數據框（參見下面的示例）。 Dataframe1 每月包含多個每日觀察，而 Dataframe2 每月僅包含一個觀察。

我現在要做的是將Dataframe2中的數據與Dataframe1中每個月的最后一天對齊。 Dataframe1 中每月的最后一天不一定是相應日歷月的最后一天。

我很感激每一個提示如何以有效的方式解決這個問題（因為數據框可能非常大）

Dataframe1
----------------------------------
date            A          B        
1980-12-31      152.799    209.132
1981-01-01      152.799    209.132
1981-01-02      152.234    209.517
1981-01-05      152.895    211.790
1981-01-06      155.131    214.023
1981-01-07      152.596    213.044
1981-01-08      151.232    211.810
1981-01-09      150.518    210.887
1981-01-12      149.899    210.340
1981-01-13      147.588    207.621
1981-01-14      148.231    208.076
1981-01-15      148.521    208.676
1981-01-16      148.931    209.278
1981-01-19      149.824    210.372
1981-01-20      149.849    210.454
1981-01-21      150.353    211.644
1981-01-22      149.398    210.042
1981-01-23      148.748    208.654
1981-01-26      148.879    208.355
1981-01-27      148.671    208.431
1981-01-28      147.612    207.525
1981-01-29      147.153    206.595
1981-01-30      146.330    205.558
1981-02-02      145.779    206.635

Dataframe2
---------------------------------          
date                C        D     
1981-01-13          53.4     56.5
1981-02-15          52.2     60.0
1981-03-15          51.8     58.0
1981-04-14          51.8     59.5
1981-05-16          50.7     58.0
1981-06-15          50.3     59.5
1981-07-15          50.6     53.5
1981-08-17          50.1     44.5
1981-09-12          50.6     38.5

Answer 1

為了提供一個可讀的例子，我准備了如下測試數據：

df1 - 1 月和 2 月的一些觀察結果：

        date        A        B
0 1981-01-02  152.234  209.517
1 1981-01-07  152.596  213.044
2 1981-01-13  147.588  207.621
3 1981-01-20  151.232  211.810
4 1981-01-27  150.518  210.887
5 1981-02-05  149.899  210.340
6 1981-02-14  152.895  211.790
7 1981-02-16  155.131  214.023
8 1981-02-21  180.000  200.239

df2 - 您的數據，同樣來自 1 月和 2 月：

        date     C     D
0 1981-01-13  53.4  56.5
1 1981-02-15  52.2  60.0

兩個數據框都有日期時間類型的日期列。

從從df1獲取每個月的最后一次觀察開始：

res1 = df1.groupby(df1.date.dt.to_period('M')).tail(1)

對於我的數據，結果是：

        date        A        B
4 1981-01-27  150.518  210.887
8 1981-02-21  180.000  200.239

然后，要加入觀察，必須在整個月期間執行加入，而不是確切的日期。 為此，請運行：

res = pd.merge(res1.assign(month=res1['date'].dt.to_period('M')),
    df2.assign(month=df2['date'].dt.to_period('M')),
    how='left', on='month', suffixes=('_1', '_2'), )

結果是：

      date_1        A        B   month     date_2     C     D
0 1981-01-27  150.518  210.887 1981-01 1981-01-13  53.4  56.5
1 1981-02-21  180.000  200.239 1981-02 1981-02-15  52.2  60.0

如果您希望合並僅包含df1和df2中至少有一個觀察值的月份的數據，請刪除how參數。 它的默認值是inner ，在這種情況下是正確的模式。

Answer 2

當您有一個示例 dataframe 時，您可以提供執行此操作的代碼。 只需將 select 列作為列表（步驟 1 和 2）並使用該列表構建 dataframe 和代碼（步驟 3 和 4）。

import pandas as pd

# Step 1: create your dataframe, and print each column as a list, copy-paste into code example below.
df_1 = pd.read_csv('dataset1.csv')
print(list(df_1['date']))
print(list(df_1['A']))
print(list(df_1['B']))

# Step 2: create your dataframe, and print each column as a list, copy-paste into code example below.
df_2 = pd.read_csv('dataset2.csv')
print(list(df_2['date']))
print(list(df_2['C']))
print(list(df_2['D']))

# Step 3: create sample dataframe ... good if you can provide this in your future questions
df_1 = pd.DataFrame({
    'date': ['12/31/1980', '1/1/1981', '1/2/1981', '1/5/1981', '1/6/1981', 
             '1/7/1981', '1/8/1981', '1/9/1981', '1/12/1981', '1/13/1981',
             '1/14/1981', '1/15/1981', '1/16/1981', '1/19/1981', '1/20/1981',
             '1/21/1981', '1/22/1981', '1/23/1981', '1/26/1981', '1/27/1981',
             '1/28/1981', '1/29/1981', '1/30/1981', '2/2/1981'],
    'A': [152.799, 152.799, 152.234, 152.895, 155.131,
          152.596, 151.232, 150.518, 149.899, 147.588,
          148.231, 148.521, 148.931, 149.824, 149.849,
          150.353, 149.398, 148.748, 148.879, 148.671,
          147.612, 147.153, 146.33, 145.779],
    'B': [209.132, 209.132, 209.517, 211.79, 214.023,
          213.044, 211.81, 210.887, 210.34, 207.621,
          208.076, 208.676, 209.278, 210.372, 210.454,
          211.644, 210.042, 208.654, 208.355, 208.431,
          207.525, 206.595, 205.558, 206.635]
})

# Step 4: create sample dataframe ... good if you can provide this in your future questions
df_2 = pd.DataFrame({
    'date': ['1/13/1981', '2/15/1981', '3/15/1981', '4/14/1981', '5/16/1981',
             '6/15/1981', '7/15/1981', '8/17/1981', '9/12/1981'],
    'C': [53.4, 52.2, 51.8, 51.8, 50.7, 50.3, 50.6, 50.1, 50.6],
    'D': [56.5, 60.0, 58.0, 59.5, 58.0, 59.5, 53.5, 44.5, 38.5]
})

# Step 5: make sure the date field is actually a date, not a string
df_1['date'] = pd.to_datetime(df_1['date']).dt.date

# Step 6: create new colum with year and month
df_1['date_year_month'] = pd.to_datetime(df_1['date']).dt.to_period('M')

# Step 7: create boolean mask that grabs the max date for each year-month
mask_last_day_month = df_1.groupby('date_year_month')['date'].transform(max) == df_1['date']

# Step 8: create new dataframe with only last day of month
df_1_max = df_1.loc[mask_last_day_month]
print('here is dataframe 1 with only last day in the month')
print(df_1_max)
print()

# Step 9: make sure the date field is actually a date, not a string
df_2['date'] = pd.to_datetime(df_2['date']).dt.date

# Step 10: create new colum with year and month
df_2['date_year_month'] = pd.to_datetime(df_2['date']).dt.to_period('M')
print('here is the original dataframe 2')
print(df_2)
print()