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# How to Calculate Days Elapsed Since a Certain Date in Pandas
- URL: https://datascientyst.com/how-to-calculate-days-elapsed-since-a-certain-date-in-pandas/
- Published: 2025-02-18T13:52:22.000Z
- Updated: 2025-02-18T13:52:22.000Z
- Author: John D K
- Tags: Time Series

In this guide, I'll show you how to calculate days elapsed since a certain date in Pandas. Calculating the number of days elapsed since a certain date is a common task in data analysis.

**(1) Calculate days elapsed since today**

```python
df['days_elapsed'] = (pd.to_datetime('today') - pd.to_datetime(df['date_column'])).dt.days

```

**(2) Using Specific date**

```python
reference_date = pd.to_datetime('2023-12-31')
df['days_since_fixed'] = (reference_date - df['date_column']).dt.days

```

## 1\. Sample Data

Let's start with a sample dataset:

```python
import pandas as pd

data = {'date_column': ['2023-01-01', '2022-06-15', '2024-02-01']}
df = pd.DataFrame(data)

df['date_column'] = pd.to_datetime(df['date_column'])

```

This will produce a DataFrame like:

|   | date\_column |
| - | ------------ |
| 0 | 2023-01-01   |
| 1 | 2022-06-15   |
| 2 | 2024-02-01   |

---

## 2\. Calculating Days Elapsed - Today

To calculate the number of days elapsed since a given date, we subtract the date column from today’s date:

```python
df['days_elapsed'] = (pd.to_datetime('today') - df['date_column']).dt.days

```

### **Example Output**

|   | date\_column | days\_elapsed |
| - | ------------ | ------------- |
| 0 | 2023-01-01   | 405           |
| 1 | 2022-06-15   | 605           |
| 2 | 2024-02-01   | 9             |

## 3\. Handling Missing Values

If the column contains missing values (`NaT`), Pandas will return `NaN`. You can replace missing values with a default number:

```python
df['days_elapsed'] = df['days_elapsed'].fillna(0)

```

## 4\. Using a Fixed Date

If you need to calculate the days elapsed from a fixed reference date instead of today, specify the reference date:

```python
reference_date = pd.to_datetime('2023-12-31')
df['days_since_fixed'] = (reference_date - df['date_column']).dt.days

```

### **Example Output**

|   | date\_column | days\_since\_fixed |
| - | ------------ | ------------------ |
| 0 | 2023-01-01   | 364                |
| 1 | 2022-06-15   | 564                |
| 2 | 2024-02-01   | \-32               |

## 5\. Conclusion

Using Pandas, you can quickly compute the number of days that have passed since a certain date or until a future date. These calculations are useful for time-based analytics, forecasting, and tracking events.

## **Resources**

- [Pandas to\_datetime documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to%5Fdatetime.html?ref=datascientyst.com)
- [Pandas time series guide](https://pandas.pydata.org/pandas-docs/stable/user%5Fguide/timeseries.html?ref=datascientyst.com)