Date-based indexing and slicing in Data Analysis Python - Step-by-Step Execution

Concept Flow - Date-based indexing and slicing

Create DateTimeIndex

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Create DataFrame with DateTimeIndex

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Select single date or date range

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Return subset of DataFrame

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Use result for analysis or display

We create a DataFrame indexed by dates, then select data by specifying dates or date ranges to get subsets.

Execution Sample

Data Analysis Python

import pandas as pd
idx = pd.date_range('2023-01-01', periods=5)
df = pd.DataFrame({'val':[10,20,30,40,50]}, index=idx)
subset = df['2023-01-02':'2023-01-04']
print(subset)

This code creates a DataFrame with dates as index and selects rows from Jan 2 to Jan 4.

Execution Table

Step	Action	Code/Expression	Result/Output
1	Create date range index	pd.date_range('2023-01-01', periods=5)	DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'], dtype='datetime64[ns]', freq='D')
2	Create DataFrame with date index	pd.DataFrame({'val':[10,20,30,40,50]}, index=idx)	DataFrame with 5 rows indexed by dates from 2023-01-01 to 2023-01-05
3	Select subset by date range	df['2023-01-02':'2023-01-04']	Rows for 2023-01-02, 2023-01-03, 2023-01-04 with values 20, 30, 40
4	Print subset	print(subset)	val 2023-01-02 20 2023-01-03 30 2023-01-04 40

💡 Selection ends after 2023-01-04, slicing includes end date in date-based indexing.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
idx	undefined	DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'])	same	same	same
df	undefined	undefined	DataFrame with 5 rows indexed by idx	same	same
subset	undefined	undefined	undefined	DataFrame with rows 2023-01-02 to 2023-01-04	same

Key Moments - 2 Insights

Why does the slice df['2023-01-02':'2023-01-04'] include the end date 2023-01-04?

In date-based slicing with pandas, the end date is included unlike normal Python slicing. See execution_table step 3 and 4 where 2023-01-04 row is included.

Can I select a single date like df['2023-01-03']?

Yes, selecting a single date returns the row for that date. This is similar to slicing but returns a single row instead of a DataFrame slice.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'val' for the date 2023-01-03 in the subset?

A 20

B 30

C 40

D 50

💡 Hint

Check execution_table row 3 and 4 for the subset values.

At which step is the DataFrame 'df' created with the date index?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at execution_table step 2 where df is created.

If you change the slice to df['2023-01-02':'2023-01-03'], how many rows will the subset have?

A 2

B 1

C 3

D 4

💡 Hint

Date slicing includes both start and end dates, so count rows between 2023-01-02 and 2023-01-03.

Concept Snapshot

Date-based indexing lets you select rows by dates in a DataFrame.
Use a DateTimeIndex for the DataFrame.
Slice with df['start_date':'end_date'] to get all rows in that range, inclusive.
Selecting a single date returns that row.
This is handy for time series data analysis.

Full Transcript

We start by creating a date range index from January 1 to January 5, 2023. Then, we create a DataFrame with this date index and some values. Next, we select a subset of the DataFrame by slicing from January 2 to January 4. The slice includes the end date, so rows for January 2, 3, and 4 are returned. This subset is printed showing the values for those dates. Date-based slicing in pandas includes the end date, unlike normal Python slicing. You can also select a single date to get one row. This method is useful for working with time series data.