I am developing a python solution to a data transformation challenge and am experiencing a small hurdle that I can't seem to figure out. I want to merge the two data frames (i.e., df1 & df2) to create a new data frame that has inserted df2['value2'] into df1['value1'] while considering date and 'Type' alignment. Has anyone ever solved a similar problem? Is pandas the best library for solving this problem?
Please see the code snippet I am using in my jupyter notebook for solution testing below.
import pandas as pd
df1 = pd.DataFrame({ 'Type': ['Labor', 'Material', 'Labor', 'Material' , 'Labor', 'Material', 'Labor', 'Material'],'date1': ['2021-01-01', '2021-01-01', '2021-02-01', '2021-02-01', '2021-03-01', '2021-03-01', '2021-04-01', '2021-04-01'], 'value1': [0,0,0,0,0,0,0,0]})
df2 = pd.DataFrame({ 'Type': ['Labor', 'Material', 'Labor', 'Material'],'date2': ['2021-01-
11', '2021-02-22', '2021-02-05', '2021-03-15'], 'value2': [10,20,25,45]})
display(df1)
display(df2)
df1['date1'] = pd.to_datetime(df1['date1'])
df2['date2'] = pd.to_datetime(df2['date2'])
df1 = df1.sort_values(['date1', 'Type'])
df2 = df2.sort_values(['date2', 'Type'])
merge_df = pd.merge_asof(df2, df1, left_on= 'date2', right_on= 'date1' , by = 'Type',
direction = 'nearest')
display(merge_df)
The output I am trying to achieve is visualized below.
| Type | Date | Value |
|---|---|---|
| Labor | 2021-01-01 | 10 |
| Material | 2021-01-01 | 0 |
| Labor | 2021-02-01 | 25 |
| Material | 2021-02-01 | 20 |
| Labor | 2021-03-01 | 0 |
| Material | 2021-03-01 | 45 |
| Labor | 2021-04-01 | 0 |
| Material | 2021-04-01 | 0 |
merge_asof is a left merge, you need to use df1 on the left. Also you have to use the month as by:
out = (pd.merge_asof(df1.assign(month=df1['date1'].dt.to_period('M')),
df2.assign(month=df2['date2'].dt.to_period('M')),
left_on='date1', right_on='date2',
by=['Type', 'month'], direction='nearest')
.fillna({'value2': 0})
)
Output:
Type date1 value1 month date2 value2
0 Labor 2021-01-01 0 2021-01 2021-01-11 10.0
1 Material 2021-01-01 0 2021-01 NaT 0.0
2 Labor 2021-02-01 0 2021-02 2021-02-05 25.0
3 Material 2021-02-01 0 2021-02 2021-02-22 20.0
4 Labor 2021-03-01 0 2021-03 NaT 0.0
5 Material 2021-03-01 0 2021-03 2021-03-15 45.0
6 Labor 2021-04-01 0 2021-04 NaT 0.0
7 Material 2021-04-01 0 2021-04 NaT 0.0
Intermediates:
# df1.assign(month=df1['date1'].dt.to_period('M'))
Type date1 value1 month
0 Labor 2021-01-01 0 2021-01
1 Material 2021-01-01 0 2021-01
2 Labor 2021-02-01 0 2021-02
3 Material 2021-02-01 0 2021-02
4 Labor 2021-03-01 0 2021-03
5 Material 2021-03-01 0 2021-03
6 Labor 2021-04-01 0 2021-04
7 Material 2021-04-01 0 2021-04
# df2.assign(month=df2['date2'].dt.to_period('M'))
Type date2 value2 month
0 Labor 2021-01-11 10 2021-01
2 Labor 2021-02-05 25 2021-02
1 Material 2021-02-22 20 2021-02
3 Material 2021-03-15 45 2021-03