Interpreting Multiindex datetime-CodePudding

I have the following code:

import pandas as pd
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import warnings
warnings.filterwarnings("ignore")

start = datetime.date(2020,1,1)
end = datetime.date.today()

stock =  'fb'

data = web.DataReader(stock, 'yahoo', start, end)
data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data = data[~data.index.duplicated(keep='first')]
data['year'] = data.index.year
data['month'] = data.index.month
data['week'] = data.index.week
data['day'] = data.index.day
data.set_index('year', append=True, inplace =True)
data.set_index('month',append=True,inplace=True)
data.set_index('week',append=True,inplace=True)
data.set_index('day',append=True,inplace=True)

fig, ax = plt.subplots(dpi=300, figsize =(30,4))
data.plot(y='Close', ax=ax, xlabel= 'Date')

plt.show()

What can I do to interpret the multiindex dates as the x axis in more readable year and month format? Such as in a format like strftime('%y -%m'). A similar question was asked here: Renaming months from number to name in pandas But I am unable to see how I can use this to rename the x axis. Any help would be appreciated.

CodePudding user response：

You can use the dates from matplotlib. See the following link for more details:

https://matplotlib.org/stable/api/dates_api.html#matplotlib.dates.ConciseDateFormatter

Here is the modified code:

import pandas as pd
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import warnings
warnings.filterwarnings("ignore")

from matplotlib import dates as mdates

start = datetime.date(2020,1,1)
end = datetime.date.today()

stock =  'fb'

data = web.DataReader(stock, 'yahoo', start, end)
data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data = data[~data.index.duplicated(keep='first')]
data['year'] = data.index.year
data['month'] = data.index.month
data['week'] = data.index.week
data['day'] = data.index.day
data.set_index('year', append=True, inplace =True)
data.set_index('month',append=True,inplace=True)
data.set_index('week',append=True,inplace=True)
data.set_index('day',append=True,inplace=True)

fig, ax = plt.subplots(dpi=300, figsize =(15,4))
plt.plot(data.index.get_level_values('Date'), data['Close'])

#--------------------------------------
#Feel free to try different options
#--------------------------------------
#locator = mdates.AutoDateLocator()
locator = mdates.MonthLocator()

formatter = mdates.ConciseDateFormatter(locator)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)

plt.show()

Here is the output.