Time Series Data

Pandas Basics

3 min read

Published Sep 29 2025, updated Oct 24 2025

PandasPython

What is Time Series Data?

A sequence of data points indexed (or labeled) by time.
Examples:
- Daily stock prices
- Hourly temperature readings
- Monthly sales numbers
In Pandas, time series data is usually represented with a DatetimeIndex.

Creating DatetimeIndex

pd.date_range() creates a sequence of datetime values to use as an index.

Example:

import pandas as pd

index = pd.date_range(start='27/10/2019 00:00:00', periods=24, freq='h')

print(index)

Output:

DatetimeIndex(['2019-10-27 00:00:00', '2019-10-27 01:00:00',

'2019-10-27 02:00:00', ..., '2019-10-27 23:00:00'],

dtype='datetime64[ns]', freq='h')

It generates 24 hourly timestamps starting at midnight on Oct 27, 2019. So, it’s basically one full day, hour by hour.

Parameters Used:

start='27/10/2019 00:00:00' → the first datetime in the range.
periods=24 → number of timestamps to generate.
freq='h' → frequency = hourly.

Other Common Parameters:

end → specify an end date instead of periods.
freq → frequency of steps. Examples:
- 'B' — Business day frequency
- 'D' — Calendar day frequency
- 'h' — Hourly frequency
- 'min' — Minutely frequency
- 's' — Secondly frequency
- 'ms' — Millisecond frequency
- 'us' — Microsecond frequency
- 'ns' — Nanosecond frequency
- 'ME' — Month end frequency
- 'MS' — Month start frequency
- 'QE' — Quarter end frequency
- 'QS' — Quarter start frequency
- 'YE' — Year end frequency
- 'YS' — Year start frequency
- 'bh' — Business hour frequency
- '2D' — Every 2 days
- '3W' — Every 3 weeks
- '1H30T' — Every 1 hour and 30 minutes
tz → set a timezone.

Creating DataFrame example:

import numpy as np

import pandas as pd

np.random.seed(3)

df = pd.DataFrame(data={'Col1':np.random.randint(low=20,high=35,size=24) },

index=pd.date_range(start='27/10/2019 00:00:00', periods=24,freq='h'))

Output:

Col1

2019-10-27 00:00:00 30

2019-10-27 01:00:00 28

2019-10-27 02:00:00 29

2019-10-27 03:00:00 23

2019-10-27 04:00:00 28

2019-10-27 05:00:00 28

2019-10-27 06:00:00 20

2019-10-27 07:00:00 25

2019-10-27 08:00:00 33

2019-10-27 09:00:00 23

2019-10-27 10:00:00 30

2019-10-27 11:00:00 31

2019-10-27 12:00:00 29

2019-10-27 13:00:00 29

2019-10-27 14:00:00 30

2019-10-27 15:00:00 25

2019-10-27 16:00:00 27

2019-10-27 17:00:00 26

2019-10-27 18:00:00 20

2019-10-27 19:00:00 24

2019-10-27 20:00:00 32

2019-10-27 21:00:00 33

2019-10-27 22:00:00 27

2019-10-27 23:00:00 34

You can then access the different parts such as hour, year etc of the index. Here we add them as extra columns:

df['Hour'] = df.index.hour

df['Minute'] = df.index.minute

df['Seconds'] = df.index.second

df['Day'] = df.index.day

df['WeekDay_number'] = df.index.weekday

df['WeekDay_text'] = df.index.day_name()

df['Month_number'] = df.index.month

df['Month_short'] = df.index.month_name()

df['Year'] = df.index.year

df['Year-Month'] = df.index.to_period('M')

# Print just the top 5 rows

print(df.head())

Output:

Col1 Hour Minute Seconds Day WeekDay_number \

2019-10-27 00:00:00 30 0 0 0 27 6

2019-10-27 01:00:00 28 1 0 0 27 6

2019-10-27 02:00:00 29 2 0 0 27 6

2019-10-27 03:00:00 23 3 0 0 27 6

2019-10-27 04:00:00 28 4 0 0 27 6

WeekDay_text Month_number Month_short Year Year-Month

2019-10-27 00:00:00 Sunday 10 October 2019 2019-10

2019-10-27 01:00:00 Sunday 10 October 2019 2019-10

2019-10-27 02:00:00 Sunday 10 October 2019 2019-10

2019-10-27 03:00:00 Sunday 10 October 2019 2019-10

2019-10-27 04:00:00 Sunday 10 October 2019 2019-10

Accessing date parts from non-indexed columns:

df_date = pd.DataFrame(data={'Date':pd.date_range(start='27/10/2019 00:00:00', periods=24,freq='h'),

'Col1':np.random.randint(low=20,high=35,size=24)})

print(df_date.head())

Creates a table with a date range that is just a standard column.

Output:

Date Col1

0 2019-10-27 00:00:00 31

1 2019-10-27 01:00:00 20

2 2019-10-27 02:00:00 25

3 2019-10-27 03:00:00 24

4 2019-10-27 04:00:00 34

Access the date parts of the column:

df_date['Hour'] = df_date['Date'].dt.hour

df_date['Minute'] = df_date['Date'].dt.minute

df_date['Year'] = df_date['Date'].dt.year

print(df_date.head())

Add .dt before the date information when indicating the datetime data is a column.

Output:

Date Col1 Hour Minute Year

0 2019-10-27 00:00:00 31 0 0 2019

1 2019-10-27 01:00:00 20 1 0 2019

2 2019-10-27 02:00:00 25 2 0 2019

3 2019-10-27 03:00:00 24 3 0 2019

4 2019-10-27 04:00:00 34 4 0 2019

Converting to datetime

import pandas as pd

df = pd.DataFrame({

"Date": ["2025-01-01", "2025-01-02", "2025-01-03"],

"Sales": [100, 150, 200]

})

df["Date"] = pd.to_datetime(df["Date"])

Converts strings to datetime objects.
Benefits: allows arithmetic, slicing, and resampling.

Setting datetime as index:

df.set_index("Date", inplace=True)

print(df)

Output:

Sales

Date

2025-01-01 100

2025-01-02 150

2025-01-03 200

Now df.index is a DatetimeIndex.

Accessing Time Series Data

By specific date:

df.loc["2025-01-02"]

By date range:

df.loc["2025-01-01":"2025-01-02"]

By year, month, or day (via string slicing):

# all data in January 2025

df.loc["2025-01"]

# all data in 2025

df.loc["2025"]

Frequency and Resampling

Pandas supports regular time intervals (daily, monthly, yearly, etc.).

# Resample daily to monthly sum

df.resample("ME").sum()

# Resample monthly to daily mean

df.resample("D").mean()

Common frequency strings:

Time-based
- "s" → seconds
- "min" → minutes
- "h" → hours
- "D" → calendar day
- "B" → business day
Week-based
- "W" → weekly (default Sunday end)
- "W-MON" → weekly anchored to Monday
Month/Quarter/Year
- "ME" → month end
- "MS" → month start
- "QE" → quarter end
- "QS" → quarter start
- "YE" → year end
- "YS" → year start
Custom
- "2h" → every 2 hours
- "15min" → every 15 minutes
- "7D" → every 7 days