---
title: Smooth Trendline with Pandas DateTime Axis Using Matplotlib
date: "2022-06-26"
topics:
    - data
url: https://www.codymd.com/how-to-make-a-smooth-trendline-with-datetime-timestamp-variables-in-matplotlib/
cover: https://www.codymd.com/how-to-make-a-smooth-trendline-with-datetime-timestamp-variables-in-matplotlib/images/smooth_cover_iamge.png
---

# Smooth Trendline with Pandas DateTime Axis Using Matplotlib

### TLDR

This post shows an implementation for creating a smooth line over a datetime dataset with pandas and matplotlib.

### Introduction

I recently ran into an issue where I wanted to make a smooth line over a 2 dimensional dataset. I'd been messing around with R and thought the geom_smooth looked quite nice - so I sought to essentialy mimic that functionality.

I'm not an expert on the different methods to make a smooth line. Some googling will show you that there are many, and I'm sure there are reasons to use one or the other.

In my search I ended up at [this post](https://www.adamsmith.haus/python/answers/how-to-plot-a-smooth-line-with-matplotlib-in-python) which shows an implementation of make_interp_spline from the Scipy library. Basically, this interpolates a smooth line that goes through each point in the dataset.

Their implementation wasn't too difficult to code, but when I went to apply it to my data and realized I had an issue. I was using a datetime for the X axis.

After some robust googling I figured out how to adapt this implementation. There wasn't much on StackOverflow or even google for that matter, so I figured I'd make this simple blog post outlining my solution.

Briefly, the solution uses matplotlib.dates.date2num and matplotlib.dates.num2date to make the conversions all work nicely together.

### Imports

    import pandas as pd
    import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    from scipy.interpolate import make_interp_spline

### make a sample dataset

    data = pd.DataFrame({
        'time' : pd.date_range(start='1/1/2019', periods=10, freq='Q'),
        'values' : np.random.randint(low=50, high=100, size=10)
    }).set_index('time')
    data

|            |        |
|------------|--------|
| time       | values |
| 2019-03-31 | 52     |
| 2019-06-30 | 89     |
| 2019-09-30 | 78     |
| 2019-12-31 | 80     |
| 2020-03-31 | 79     |
| 2020-06-30 | 96     |
| 2020-09-30 | 67     |
| 2020-12-31 | 99     |
| 2021-03-31 | 81     |
| 2021-06-30 | 53     |

### Baseline figure

    fig,ax = plt.subplots(figsize=(8,5))
    ax.plot(data.index, data.values)

![](https://www.codymd.com/how-to-make-a-smooth-trendline-with-datetime-timestamp-variables-in-matplotlib/images/image.png)

### Use date2num to convert the min/max dates to numeric concordant with how matplotlib does for it's plots.

    min_date = mpl.dates.date2num(data.index.min())
    max_date = mpl.dates.date2num(data.index.max())
    x_new = np.linspace(min_date, max_date, 1000)
    print(f'min_date: {data.index.min()} -> {min_date}')
    print(f'max_date: {data.index.max()} -> {max_date}')

    min_date: 2019-03-31 00:00:00 -> 17986.0
    max_date: 2021-06-30 00:00:00 -> 18808.0

    x_new[:10]

    array([17986.        , 17986.82282282, 17987.64564565, 17988.46846847,
           17989.29129129, 17990.11411411, 17990.93693694, 17991.75975976,
           17992.58258258, 17993.40540541])

### Convert the index dates to numeric and use that to fit the Spline

    a_BSpline = make_interp_spline(data.index.map(mpl.dates.date2num), data.values)
    y_new = a_BSpline(x_new)

    y_new[:10]

    array([[52.        ],
           [52.80388298],
           [53.59690985],
           [54.37914392],
           [55.15064847],
           [55.91148681],
           [56.66172224],
           [57.40141806],
           [58.13063756],
           [58.84944405]])

### Final result

    fig,ax = plt.subplots(figsize=(8,5))
    ax.plot(data.index, data.values)
    ax.plot(x_new, y_new)

![](https://www.codymd.com/how-to-make-a-smooth-trendline-with-datetime-timestamp-variables-in-matplotlib/images/image-1.png)

### Conclusion

In conclusion, use matplotlib.dates.date2num and num2date in order to generate the necessary conversions to allow the use of the scipy methods.