Histogram or kde from datetime column

[JulianWgs]

It always annoyed me that it was not possible to generate a histogram or kernel density estimation plot from a datetime column.

Is there interest in a solution following this approach?
Link to SO

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.DataFrame({"datetime": pd.to_datetime(np.random.randint(1582800000000000000, 1583500000000000000, 100, dtype=np.int64))})
fig, ax = plt.subplots()
df["datetime"].astype(np.int64).plot.hist(ax=ax)
labels = ax.get_xticks().tolist()
labels = pd.to_datetime(labels)
ax.set_xticklabels(labels, rotation=90)
plt.show()

I didn't look into the the pandas source code yet, but if there is interest in something like this I will look into it.

I think the main issue will be finding good x tick labels, if thats a requirement.

Greetings!

[JulianWgs]

An answer would be appreciated :)

[JulianWgs]

An answer is still appreciated :)

[JulianWgs]

@jbrockmendel

[jbrockmendel]

I have no strong opinion on this. The SO link appears to have a few viable options.

[JulianWgs]

Thanks for the response! I propose my option, since a groupby is not really a histogram like one would expect.

[jbrockmendel]

cc @datapythonista thoughts?

[datapythonista]

A datetime is a float under the hood, and we support floats, so I think it shouldn't be difficult to make this work. The approach would be similar to the code that you showed (I think casting to float makes more sense than casting to int, but the idea is the same). Feel free to open a pull request for this.

[onshek]

Hi everyone here. I find it a little strange: data["datetime"].hist() can work well for calling hist_series() which dose not check whether the data is numerical. While data.hist() calls hist_frame() and fails at data = data._get_numeric_data() for data here has no columns any more and breaks down in Line415 resulting the final ValueError.
Could you give me more suggestions and I would like to have a try to fix this.
@jbrockmendel @datapythonista @JulianWgs @jreback @charlesdong1991

[AleGuarnieri]

take