error in dataframe loc when applying these two dataframes

I'm trying to put these two dataframes(data2 and trades) together tto make it look like this https://i.sstatic.net/pR8bW.png:

data2:

            Close
2015-08-28  113.290001
2015-08-31  112.760002
2015-09-01  107.720001
2015-09-02  112.339996
2015-09-03  110.370003
2015-09-04  109.269997
2015-09-08  112.309998
2015-09-09  110.150002
2015-09-10  112.570000
2015-09-11  114.209999

trades:

               Trades
2015-08-28     3.0
2015-08-31     3.0
2015-09-01     3.0
2015-09-02     3.0
2015-09-03     2.0

code:

import matplotlib.pyplot as plt

fig = plt.figure()

ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')

data2["Close"].plot(ax=ax1, lw=2.)

ax1.plot(data2.loc[trades.Trades == 2.0].index, data2.total[trades.Trades == 2.0],
         '^', markersize=10, color='m')
ax1.plot(data2.loc[trades.Trades == 3.0].index, 
         data2.total[trades.Trades == 3.0],
         'v', markersize=10, color='k')

plt.show()

But this gives the following error:

---------------------------------------------------------------------------
IndexingError                             Traceback (most recent call last)
<ipython-input-38-9cde686354a8> in <module>()
      7 data2["Close"].plot(ax=ax1, lw=2.)
      8 
----> 9 ax1.plot(data2.loc[trades.Trades == 2.0].index, data2.total[trades.Trades == 2.0],
     10          '^', markersize=10, color='m')
     11 ax1.plot(data2.loc[trades.Trades == 3.0].index, 

3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in check_bool_indexer(index, key)
   2316         if mask.any():
   2317             raise IndexingError(
-> 2318                 "Unalignable boolean Series provided as "
   2319                 "indexer (index of the boolean Series and of "
   2320                 "the indexed object do not match)."

IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).

Solution

The indexes of the two data frames are different. I've taken the approach of define masks for data2 dataframe that are based of values in trades dataframe and it works.

Additionally your sample code referred to total which does not exist. Updates to use Close

import pandas as pd
import io
import matplotlib.pyplot as plt

data2 = pd.read_csv(io.StringIO("""            Close
2015-08-28  113.290001
2015-08-31  112.760002
2015-09-01  107.720001
2015-09-02  112.339996
2015-09-03  110.370003
2015-09-04  109.269997
2015-09-08  112.309998
2015-09-09  110.150002
2015-09-10  112.570000
2015-09-11  114.209999"""), sep="\s+")

trades = pd.read_csv(io.StringIO("""               Trades
2015-08-28     3.0
2015-08-31     3.0
2015-09-01     3.0
2015-09-02     3.0
2015-09-03     2.0"""), sep="\s+")

# make sure it's dates
data2 = data2.reset_index().assign(index=lambda x: pd.to_datetime(x["index"])).set_index("index")
trades = trades.reset_index().assign(index=lambda x: pd.to_datetime(x["index"])).set_index("index")

fig = plt.figure()
ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')

data2["Close"].plot(ax=ax1, lw=2.)

mask2 = data2.index.isin((trades.Trades == 2.0).index)
mask3 = data2.index.isin((trades.Trades == 3.0).index)

ax1.plot(data2.loc[mask2].index, data2.Close[mask2],
         '^', markersize=10, color='m')
ax1.plot(data2.loc[mask3].index, 
         data2.Close[mask3],
         'v', markersize=10, color='k')


plt.show()

output