Traceback (most recent call last):
  File "<console>", line 1, in <module>
  File "/web/dashboard/asap_utils.py", line 107, in make_SWUBS_plots
    ax2.plot(dfm.index, dfm.mean_200.values, label='avg for 200s', color='#808080', linewidth=4)
  File "/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py", line 1667, in plot
    self.add_line(line)
  File "/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py", line 1902, in add_line
    self._update_line_limits(line)
  File "/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py", line 1924, in _update_line_limits
    path = line.get_path()
  File "/usr/local/lib/python3.7/site-packages/matplotlib/lines.py", line 1027, in get_path
    self.recache()
  File "/usr/local/lib/python3.7/site-packages/matplotlib/lines.py", line 670, in recache
    x = _to_unmasked_float_array(xconv).ravel()
  File "/usr/local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py", line 1390, in _to_unmasked_float_array
    return np.asarray(x, float)
  File "/usr/local/lib/python3.7/site-packages/numpy/core/_asarray.py", line 85, in asarray
    return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number, not 'Timestamp'
>>> import matplotlib
>>> matplotlib.__version__
'3.1.3'
>>> import pandas as pd
>>> pd.__version__
'1.0.0'
>>> import numpy as np
>>> np.__version__
'1.18.1'

from datetime import timedelta
from django_rq import get_scheduler
import sxptools
import pandas as pd
from django.db.models import Max
from .models import ASAPperformance
import datetime
from pytz import UTC
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import pandas as pd

def make_plots():
    df = pd.DataFrame(index=pd.date_range(start='2020-01-26 22:00:00+00:00', end='2020-02-03 14:00:00+00:00', freq='H'),
                 data=np.random.rand(185, 5), columns = ['code_200','code_201','code_500','mean_200','mean_201'] )
    fig, ax = plt.subplots( figsize=(15,6))
    dfd = df.resample('D')[['code_200','code_201','code_500']].sum().tail(10)
    plt.bar(dfd.index, dfd.code_200, color='#008c6e', label='sum of 200s')
    plt.bar(dfd.index, dfd.code_201, bottom=dfd.code_200, color='k', label='sum of 201s')
    plt.bar(dfd.index, dfd.code_500, bottom=dfd.code_200+dfd.code_201, color='#872d32',label='sum of 500s')

    ax.yaxis.grid('both',color='k', linestyle='-', alpha=0.2)
    ax.set_ylabel('# responses')
    ax.legend(loc='upper left')



    dfm = df.resample('D')[['mean_200', 'mean_201']].mean().tail(10)
    ax2 = ax.twinx() 
    ax2.plot(dfm.index, dfm.mean_200.values, label='avg for 200s', color='#808080', linewidth=4)
    ax2.plot(dfm.index, dfm.mean_201.values, label='avg for 201s', color='k', linewidth=4)

    ax2.legend(loc='lower left')
    ax2.set_ylabel('average response times [ms]')
    ax.xaxis.set_major_formatter( mdates.DateFormatter('%a, %d-%b'))
    ax.xaxis.set_tick_params(rotation=45)
    # i = 0
    # for val in  ['8.20%', '33.52%', '35.35%', '37.21%', '34.14%', '36.12%', '33.40%', '35.45%', '36.90%']:
    #     ax.text(dfd.index[i], dfd.code_200.mean(), val, horizontalalignment='center',color='w', fontsize=22)
    #     i +=1
    fig.savefig('dashboard/static/images/sum_plot.png', bbox_inches='tight', transparent=True)

>>> import matplotlib
>>> matplotlib.__version__
'3.1.0'
>>> import numpy as np; np.__version__
'1.18.1'
>>> import pandas as pd; pd.__version__
'1.0.0'
>>> import django
>>> django.__version__
'2.2.10'