Btukfyl

Consider this simple example

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

import matplotlib.dates as mdates



pd.__version__

Out[147]: u'0.22.0'



idx = pd.date_range('2017-01-01 05:03', '2017-01-01 18:03', freq = 'min')



df = pd.Series(np.random.randn(len(idx)),  index = idx)

df.head()

Out[145]: 

2017-01-01 05:03:00   0.4361

2017-01-01 05:04:00   0.9737

2017-01-01 05:05:00   0.8430

2017-01-01 05:06:00   0.4292

2017-01-01 05:07:00   0.5739

Freq: T, dtype: float64

I want to plot this, and have ticks every hour. I use:

fig, ax = plt.subplots()

hours = mdates.HourLocator(interval = 1)  #

h_fmt = mdates.DateFormatter('%H:%M:%S')



df.plot(ax = ax, color = 'black', linewidth = 0.4)



ax.xaxis.set_major_locator(hours)

ax.xaxis.set_major_formatter(h_fmt)

which gives

why dont the ticks appear every hour here? Thanks for your help!

The problem is that while pandas in general directly wraps the matplotlib plotting methods, this is not the case for plots with dates. As soon as dates are involved, pandas uses a totally different numerical representation of dates and hence also uses its own locators for the ticks.

In case you want to use matplotlib.dates formatters or locators on plots created with pandas you may use the x_compat=True option in pandas plots.

df.plot(ax = ax, color = 'black', linewidth = 0.4, x_compat=True)

This allows to use the matplotlib.dates formatters or locators as shown below.
Else you may replace df.plot(ax = ax, color = 'black', linewidth = 0.4) by

ax.plot(df.index, df.values, color = 'black', linewidth = 0.4)

Complete example:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.dates as mdates



idx = pd.date_range('2017-01-01 05:03', '2017-01-01 18:03', freq = 'min')

df = pd.Series(np.random.randn(len(idx)),  index = idx)



fig, ax = plt.subplots()

hours = mdates.HourLocator(interval = 1)

h_fmt = mdates.DateFormatter('%H:%M:%S')



ax.plot(df.index, df.values, color = 'black', linewidth = 0.4)

#or use

df.plot(ax = ax, color = 'black', linewidth = 0.4, x_compat=True)

#Then tick and format with matplotlib:

ax.xaxis.set_major_locator(hours)

ax.xaxis.set_major_formatter(h_fmt)



fig.autofmt_xdate()

plt.show()

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.dates as mdates



idx = pd.date_range('2017-01-01 05:03', '2017-01-01 18:03', freq = 'min')



df = pd.DataFrame(np.cumsum(np.random.randn(len(idx), 2),0), 

                  index = idx, columns=list("AB"))



fig, ax = plt.subplots()

ax.plot(df.index, df["A"], color = 'black')

ax2 = ax.twinx()

ax2.plot(df.index, df["B"], color = 'indigo')



hours = mdates.HourLocator(interval = 1)

h_fmt = mdates.DateFormatter('%H:%M:%S')

ax.xaxis.set_major_locator(hours)

ax.xaxis.set_major_formatter(h_fmt)



fig.autofmt_xdate()

plt.show()