import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import seaborn as sns
from datetime import datetime
%matplotlib inline
import warnings; warnings.filterwarnings(action='once')

df = pd.read_excel("CDS Detail - 2019 05 22 - sample.xlsx")

df.head()
Date                Status  Method  Volume
2018-05-10 20:45:28 F   Discretionary   1
2018-05-03 21:09:10 F   Discretionary   1
2018-05-17 14:19:47 F   Discretionary   1
2018-05-17 14:21:17 F   Discretionary   1
2018-05-17 14:19:47 F   Discretionary   1

df = df[df['Date'].notnull()]

df.columns
Index(['Status', 'Method', 'Volume', 'Date'], dtype='object')
df.dtypes
Status            object
Method            object
Volume             int64
Date      datetime64[ns]
dtype: object

df.head()
Status  Method  Volume  Date
0   F   Discretionary   1   2018-05-10 20:45:28
1   F   Discretionary   1   2018-05-03 21:09:10
2   F   Discretionary   1   2018-05-17 14:19:47
3   F   Discretionary   1   2018-05-17 14:21:17
4   F   Discretionary   1   2018-05-17 14:19:47

df = df.set_index('Date')

df.head()
Status  Method  Volume
Date
2018-05-10 20:45:28 F   Discretionary   1
2018-05-03 21:09:10 F   Discretionary   1
2018-05-17 14:19:47 F   Discretionary   1
2018-05-17 14:21:17 F   Discretionary   1
2018-05-17 14:19:47 F   Discretionary   1

weekly = df.resample(rule='W').sum()

weekly
Date        Volume
2018-04-08  7
2018-04-15  10
2018-04-22  40
2018-04-29  69
2018-05-06  128
2018-05-13  380
2018-05-20  464
2018-05-27  6052
2018-06-03  6095
2018-06-10  6224
2018-06-17  3084
2018-06-24  5

sns.lineplot(data = weekly, hue = 'Method')