Annoting date on chart

I am trying to add a straight line down which would have date printed vertically on the line. I have added a picture of how i am trying to accomplish this below. I have also included the code which i am trying to annotate with.

My code:

import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import numpy as np

start = datetime.date(2015,1,1)
end = datetime.date.today()
start1 = datetime.date(2019,1,1)

data = web.DataReader("^GSPC", 'yahoo',start, end)
data1 = web.DataReader("^GSPC", 'yahoo', start1, end)

data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data1.index = pd.to_datetime(data1.index, format ='%Y-%m-%d')

full_dates = pd.date_range(start, end)
data = data.reindex(full_dates)
data1 = data1.reindex(full_dates)

data.set_index('month',append=True,inplace=True)
data1.set_index('month',append=True,inplace=True)
data.set_index('week',append=True,inplace=True)
data1.set_index('week',append=True,inplace=True)
data.set_index('day',append=True,inplace=True)
data1.set_index('day',append=True,inplace=True)

data['pct_day']= data['Adj Close'].pct_change()
data1['pct_day']= data1['Adj Close'].pct_change()

df = data.groupby(['month', 'day']).mean()
df2 = data1.groupby(['month', 'day']).mean()

df['cumsum_pct_day']=df['pct_day'].cumsum(axis = 0)
df2['cumsum_pct_day']=df2['pct_day'].cumsum(axis = 0)

ax = df.plot(y='cumsum_pct_day', grid = True, label='df')
df2.plot(y='cumsum_pct_day', grid= True, ax=ax, label='df2')

ylims = ax.get_ylim()
ax.vlines(end, ylims[0], data1.Close[0], linestyles='--')
ax.text(end, data1.Close[0], end, ha='right', va='top', rotation=90)
ax.set_ylim(ylims)


plt.show()

For some reason i am getting warning :

But the line is not plotting. Could you advise why it is not doing so?

Solution

Instead of vlines that plots multiple lines, you can use the method axvline that adds a single vertical line:

from datetime import timedelta

def gen_df(size):
    arr1 = pd.date_range(start='1/1/2018', periods=size)
    arr2 = np.random.exponential(size=size).cumsum()
    return pd.DataFrame({'col1': arr1, 'col2': arr2})

df1 = gen_df(60)
df2 = gen_df(50)

ax = df1.plot(x='col1', y='col2', label='df1')
df2.plot(x='col1', y='col2', ax=ax, label='df2')
ax.axvline(x=df2['col1'].max(), color='red')
ax.annotate(s=df2['col1'].max().date(), xy=(df2['col1'].max()-timedelta(days=2), 35), rotation=90)

Let's reproduce your data set:

def gen_df(size):
    arr1 = pd.date_range(start='1/1/2018', periods=size)
    arr2 = np.random.standard_exponential(size)
    return pd.DataFrame({'Adj Close': arr2}, index=arr1)

df1 = gen_df(150)
df2 = gen_df(130)

print(df1.head())

Output:

            Adj Close
2018-01-01   0.061166
2018-01-02   0.669330
2018-01-03   0.123332
2018-01-04   0.029007
2018-01-05   1.024210

for df in [df1, df2]:
    df['year'] = df.index.year
    df['month'] = df.index.month
    df['week'] = df.index.week
    df['day'] = df.index.day
    df.set_index('month', append=True, inplace=True)
    df.set_index('week', append=True, inplace=True)
    df.set_index('day', append=True, inplace=True)
    df['pct_day']= df['Adj Close'].pct_change()

print(df1.head())

Output:

                           Adj Close  year    pct_day
           month week day                            
2018-01-01 1     1    1     0.061166  2018        NaN
2018-01-02 1     1    2     0.669330  2018   9.942917
2018-01-03 1     1    3     0.123332  2018  -0.815739
2018-01-04 1     1    4     0.029007  2018  -0.764804
2018-01-05 1     1    5     1.024210  2018  34.308892

df1 = df1.groupby(['month', 'day']).mean()
df1['cumsum_pct_day'] = df1['pct_day'].cumsum(axis = 0)

df2 = df2.groupby(['month', 'day']).mean()
df2['cumsum_pct_day'] = df2['pct_day'].cumsum(axis = 0)

print(df1.head())

Output:

           Adj Close  year    pct_day  cumsum_pct_day
month day                                            
1     1     0.061166  2018        NaN             NaN
      2     0.669330  2018   9.942917        9.942917
      3     0.123332  2018  -0.815739        9.127178
      4     0.029007  2018  -0.764804        8.362375
      5     1.024210  2018  34.308892       42.671267

Add a single line with axvline:

ax = df1.plot(y ='cumsum_pct_day', label='df1')
df2.plot(y ='cumsum_pct_day', ax=ax, label='df2')

df = df1 if len(df1) < len(df2) else df2 # get a smaller DataFrame
ax.axvline(x=len(df), color='red')

y_min, y_max = ax.get_ylim()
middle = (y_max - y_min) / 2
ax.annotate(s=df.index.max(), xy=(len(df) - 5, middle), rotation=90)

Add multiple lines with vlines:

ax = df1.plot(y ='cumsum_pct_day', label='df1')
df2.plot(y ='cumsum_pct_day', ax=ax, label='df2')

y_min, y_max = ax.get_ylim()
ax.vlines(x=np.arange(len(df2), len(df1), step=.1), ymin=y_min, ymax=y_max, color='red')

middle = (y_max - y_min) / 2
ax.annotate(s=df.index.max(), xy=(len(df) - 5, middle), rotation=90)