matplotlib is not automatically reading dataframe as date

Question

Since pandas last update, the x axis is not reading the index as a date. Any clues on what changed? As an example, the following code (Source) creates a random df. The matplotlib part is exactly what I'm have been doing with my real dataset (dates in my data where made using time.strftime("%Y-%m-%d")):

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(7), freq='D')
np.random.seed(seed=1111)
data = np.random.randint(1, high=100, size=len(days))
df = pd.DataFrame({'test': days, 'col2': data})
df = df.set_index('test')
# creates graph:
import matplotlib.pyplot as plt
fig = plt.plot(df.index, df["col2"])
fig = plt.xticks(rotation=30), plt.legend(loc='best'), plt.xlabel("Weeks")
fig = plt.style.use(['bmh', 'seaborn-paper'])
fig = plt.title("Index", fontsize=14, fontweight='bold')
plt.show()

The resulting graph has the x axis in number format. Before updating, my graphs automatically had dates in the index (because the index is in date format).

Answer 1

Pandas used to import the units handlers for datetime64, but as of 0.21 stopped (though it may be back for 0.22). The way to get the old behaviour without explicit conversion is

from pandas.tseries import converter as pdtc
pdtc.register()

Answer 2

Solution 1

Use pandas .plot on the dataframe:

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(7), freq='D')
np.random.seed(seed=1111)
data = np.random.randint(1, high=100, size=len(days))
df = pd.DataFrame({'test': days, 'col2': data})
df = df.set_index('test')
# creates graph:
import matplotlib.pyplot as plt
sub = df.plot()
fig = plt.xticks(rotation=30), plt.legend(loc='best'), plt.xlabel("Weeks")
fig = plt.style.use(['bmh', 'seaborn-paper'])
fig = plt.title("Index", fontsize=14, fontweight='bold') 

Solution 2

Convert them Python datetime objects:

fig = plt.plot(df.index.to_pydatetime(), df["col2"])

Result of both approaches