Если у вас есть примерный фрейм данных, вы можете предоставить код для этого. Просто выберите столбец в виде списка (шаг 1 и 2) и используйте этот список для построения кадра данных с кодом (шаги 3 и 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()
