ID            Date            Item
22788928    1/1/14 12:00    Stock A
28388931    1/2/14 14:00    Stock B
23288957    1/2/14 14:00    Stock C
28788888    1/2/14 14:00    Stock A
28788946    1/2/14 14:00    Stock B
23239020    1/2/14 14:00    Stock C
28789025    1/2/14 14:00    Stock A
28783465    1/2/14 14:00    Stock B
28789089    1/2/14 14:00    Stock C
28784595    1/2/14 14:00    Stock A
28788656    1/2/14 14:00    Stock B
28790573    1/2/14 14:00    Stock C
28790574    1/2/14 14:00    Stock A
28790585    1/2/14 14:00    Stock B
28790586    1/2/14 14:00    Stock C
28790616    1/2/14 14:00    Stock A
28790617    1/2/14 14:00    Stock B
28790628    1/3/14 0:00     Stock C
28790630    1/3/14 0:00     Stock A
28790662    1/3/14 0:00     Stock B
28791947    1/3/14 0:00     Stock C
28791952    1/3/14 0:00     Stock A
28789524    1/3/14 0:00     Stock B
28789625    1/3/14 0:00     Stock C
28789842    1/3/14 0:00     Stock A
28789917    1/4/14 0:00     Stock B
28789975    1/5/14 0:00     Stock C
28789986    1/6/14 0:00     Stock A
28790058    1/7/14 0:00     Stock B
28790060    1/8/14 0:00     Stock B
28790063    1/9/14 0:00     Stock B
28790081    1/10/14 0:00    Stock B
28790085    1/11/14 0:00    Stock B
28790087    1/12/14 0:00    Stock B
28790994    1/13/14 0:00    Stock B
28791020    1/14/14 0:00    Stock A
28791082    1/15/14 0:00    Stock A
28791087    1/15/14 0:00    Stock A
28791096    1/15/14 0:00    Stock A
28791131    1/18/14 0:00    Stock A
28791086    1/19/14 0:00    Stock A
28791092    1/19/14 0:00    Stock A
28791102    1/19/14 0:00    Stock A
28791118    1/19/14 0:00    Stock A
28791140    1/19/14 0:00    Stock A
28791303    1/19/14 0:00    Stock A
28790057    1/25/14 0:00    Stock A
28790086    1/26/14 0:00    Stock A
28790098    1/27/14 0:00    Stock A
28789547    1/28/14 0:00    Stock A
28789216    1/29/14 0:00    Stock A
28788886    1/30/14 0:00    Stock A
28788556    1/31/14 0:00    Stock A
28788226    2/1/14 0:00     Stock A
28787896    2/2/14 0:00     Stock A
28787566    2/3/14 0:00     Stock C
28787236    2/4/14 0:00     Stock C
28786906    2/5/14 0:00     Stock C
28786576    2/6/14 0:00     Stock C
28786246    2/7/14 0:00     Stock C
28785915    2/8/14 0:00     Stock C
28785585    2/9/14 0:00     Stock B
28785255    2/10/14 0:00    Stock B
28784925    2/11/14 0:00    Stock B
28784595    2/12/14 0:00    Stock B
28784265    2/13/14 0:00    Stock A
28783935    2/13/14 0:00    Stock A
28783605    2/13/14 0:00    Stock A

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
pd.set_option('display.mpl_style', 'default')

data = pd.read_csv('.../data.csv',
                         parse_dates='Date',
                         infer_datetime_format=True
                         )

item_A = data[data['Item'] == 'Stock A']
count_A = item_A['Date'].value_counts(sort=False)
print count_A
count_A.plot()

plt.show()
pylab.show(block=True)

1/28/14 0:00    1
1/26/14 0:00    1
1/27/14 0:00    1
1/14/14 0:00    1
1/3/14 0:00     3
2/13/14 0:00    3
1/25/14 0:00    1
1/2/14 14:00    5
1/6/14 0:00     1
1/15/14 0:00    3
1/1/14 12:00    1
1/19/14 0:00    6
1/30/14 0:00    1
2/2/14 0:00     1
1/29/14 0:00    1
1/18/14 0:00    1
2/1/14 0:00     1
1/31/14 0:00    1
dtype: int64