Date range¶

start/end dates to intervals¶

pd.date_range("2023-07-01", "2023-07-02", freq="30min", inclusive='left')

split date range to intervals¶

df1 has columns datetime and value and df2 has start_date, end_date and value2. The best way to add value2 to value based on the datetime

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({
    'datetime': ['2023-01-01 00:00:00', '2023-01-01 01:00:00', '2023-01-01 02:00:00'],
    'value': [10, 20, 30]
})

df2 = pd.DataFrame({
    'start_date': ['2023-01-01 00:00:00', '2023-01-01 01:00:00'],
    'end_date': ['2023-01-01 01:00:00', '2023-01-01 02:00:00'],
    'value2': [1, 2]
})

# Convert datetime columns to datetime type
df1['datetime'] = pd.to_datetime(df1['datetime'])
df2['start_date'] = pd.to_datetime(df2['start_date'])
df2['end_date'] = pd.to_datetime(df2['end_date'])

# Add a unique key to the df2 dataframe
df2['key'] = range(len(df2))

# Merge dataframes based on datetime range
merged_df = pd.merge(df1, df2, how='left', left_on=[(df1['datetime'] >= df2['start_date']) & (df1['datetime'] <= df2['end_date'])], right_on=['key'])

# Calculate new value column by adding value and value2
merged_df['new_value'] = merged_df['value'] + merged_df['value2'].fillna(0)

# Drop unnecessary columns
merged_df = merged_df.drop(['start_date', 'end_date', 'value2', 'key'], axis=1)

print(merged_df)

split date range to intervals first?¶

In general, expanding the date range into timestamp intervals first and then doing a merge

might be faster if you have a large dataset,
since it reduces the number of comparisons that need to be made during the merge.
However, this approach can also significantly increase the size of your dataframe and consume a lot of memory,
so it might not be the best approach for all use cases

In contrast, the conditional join approach is

more memory-efficient and
can be faster if you have a relatively small dataset or
if the number of overlapping date ranges is small.
However, if the number of overlapping date ranges is large,
then the conditional join approach might become slower compared to expanding the date range into timestamp intervals first.

In general, it's a good idea to test both approaches on your specific data and use case to determine which one is faster and more efficient for your needs.