Tick labels overlap in pandas bar chart

Question

TL;DR: In pandas how do I plot a bar chart so that its x axis tick labels look like those of a line chart?

I made a time series with evenly spaced intervals (one item each day) and can plot it like such just fine:

intensity[350:450].plot()
plt.show()

But switching to a bar chart created this mess:

intensity[350:450].plot(kind = 'bar')
plt.show()

I then created a bar chart using matplotlib directly but it lacks the nice date time series tick label formatter of pandas:

def bar_chart(series):
    fig, ax = plt.subplots(1)
    ax.bar(series.index, series)
    fig.autofmt_xdate()
    plt.show()

bar_chart(intensity[350:450])

Here's an excerpt from the intensity Series:

intensity[390:400]

2017-03-07    3
2017-03-08    0
2017-03-09    3
2017-03-10    0
2017-03-11    0
2017-03-12    0
2017-03-13    2
2017-03-14    0
2017-03-15    3
2017-03-16    0
Freq: D, dtype: int64 

I could go all out on this and just create the tick labels by hand completely but I'd rather not have to baby matplotlib and let do pandas its job and do what it did in the very first figure but with a bar plot. So how do I do that?

Answer 1

Pandas bar plots are categorical plots. They create one tick (+label) for each category. If the categories are dates and those dates are continuous one may aim at leaving certain dates out, e.g. to plot only every fifth category,

ax = series.plot(kind="bar")
ax.set_xticklabels([t if not i%5 else "" for i,t in enumerate(ax.get_xticklabels())])

In contrast, matplotlib bar charts are numberical plots. Here a useful ticker can be applied, which ticks the dates weekly, monthly or whatever is needed.

In addition, matplotlib allows to have full control over the tick positions and their labels.

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

index = pd.date_range("2018-01-26", "2018-05-05")
series = pd.Series(np.random.rayleigh(size=100), index=index)

plt.bar(series.index, series.values)
plt.gca().xaxis.set_major_locator(dates.MonthLocator())
plt.gca().xaxis.set_major_formatter(dates.DateFormatter("%b\n%Y"))
plt.show()