Missing default matplotlib labels with matplotlib.dates

Question

I am following this demo to use matplotlib.dates in order to add "ticks" per month using pandas series data. My pandas series is named weekly_data1991

Datetime
1991-01-08    2245
1991-01-09    2678 
1991-01-10    2987
1991-01-11    2258
....
Freq: W-SUN, dtype: int64

Unfortunately, this wipes out many of the "default" labels when just using straightforward matplotlib.pyplot.plot()

For example, if I use

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12,5))
plt.plot(weekly_data1991, color = 'green' )

I get in return

Using matplotlib.dates ,

from matplotlib import dates as mdates
fig = plt.figure(figsize=(12,5))
ax = plt.subplot(111)
plt.plot(weekly_data1991, color = 'green' )
years = mdates.YearLocator()
months = mdates.MonthLocator()
yearsFmt = mdates.DateFormatter('%Y')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.grid(which='both', axis='x')
plt.show()

which outputs the plot

I can I put the labels back in?

Answer 1

I find (Matplotlib 1.4.3) that I can get both major and minor tick labels formatted as dates using mdates.DateFormatter as follows.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates as mdates

df = pd.read_csv('data.csv', sep=None, names=('Datetime', 'values'),
                 parse_dates=['Datetime'])

fig = plt.figure(figsize=(12,5))
ax = plt.subplot(111)
plt.plot(df['Datetime'], df['values'], color = 'green' )
years = mdates.YearLocator()
months = mdates.MonthLocator()
yearsFmt = mdates.DateFormatter('%Y')
monthsFmt = mdates.DateFormatter('%b')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_formatter(monthsFmt)
ax.xaxis.set_minor_locator(months)
ax.grid(which='both', axis='x')
for tick in ax.get_xaxis().get_major_ticks():
    tick.set_pad(15)
plt.show()