Python | Working with partial dates in python for time series slicing

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Slicing by partial dates

Slicing by partial dates in Python is also a common task when working with time series data. Here's an example of how to slice a pandas DataFrame by partial date range:

import pandas as pd

# Create a sample DataFrame

df = pd.DataFrame({

'date': pd.date_range('2022-01-01', '2022-01-31'),

'value': range(31)

})

# Set the date column as the index

df = df.set_index('date')

# Slice the DataFrame by partial date range

partial_date = '2022-01'

sliced_df = df.loc[partial_date]

# Print the sliced DataFrame

print(sliced_df)

In this example, we create a sample DataFrame with a date column and a value column. We then set the date column as the index using the set_index method. We can then slice the DataFrame by specifying a partial date using the loc method. In this case, we use the partial_date variable to specify that we want to slice the DataFrame by all rows in January 2022. Finally, we print the sliced DataFrame to the console.

This will output the following:

value

date

2022-01-01 0

2022-01-02 1

2022-01-03 2

2022-01-04 3

2022-01-05 4

2022-01-06 5

2022-01-07 6

2022-01-08 7

2022-01-09 8

2022-01-10 9

2022-01-11 10

2022-01-12 11

2022-01-13 12

2022-01-14 13

2022-01-15 14

2022-01-16 15

2022-01-17 16

2022-01-18 17

2022-01-19 18

2022-01-20 19

2022-01-21 20

2022-01-22 21

2022-01-23 22

2022-01-24 23

2022-01-25 24

2022-01-26 25

2022-01-27 26

2022-01-28 27

2022-01-29 28

2022-01-30 29

2022-01-31 30

This shows us that the DataFrame has been sliced to include only rows with dates in January 2022.