python - Modifying Code to work for Month and Week instead of Year -

i making stacked bar plot on year time span x-axis company names, y-axis number of calls, , stacks months.

i want able make plot run time span of month, stacks days, , time span of week, stacks days. having trouble doing since code built around year time span.

my input original input csv file. pulling 2 rows this:

companyname     recvd_dttm company1        6/5/2015 18:28:50 pm company2        6/5/2015 14:25:43 pm company3        9/10/2015 21:45:12 pm company4        6/5/2015 14:30:43 pm company5        6/5/2015 14:32:33 pm 

then make datatable looks this

pivot_table.head(3) out[12]:  month       1   2   3   4   5   6   7   8   9   10  11   12  companyname                                                                      customer1   17  30  29  39  15  26  24  12  36  21  18   15   customer2   4   11  13  22  35  29  15  18  29  31  17   14 customer3   11   8  25  24   7  15  20   0  21  12  12   17 

and code far.

first grab years worth of data (i change month or week question)

# filter countries @ least 1 medal , sort df['recvd_dttm'] = pd.to_datetime(df['recvd_dttm'])  #only retrieve data before (ignore typos future dates) mask = df['recvd_dttm'] <= datetime.datetime.now() df = df.loc[mask] # first , last datetime final week of data  range_max = df['recvd_dttm'].max() range_min = range_max - pd.dateoffset(years=1)  # take slice final week of data df = df[(df['recvd_dttm'] >= range_min) &                 (df['recvd_dttm'] <= range_max)] 

then create pivot_table shown above.

########################################################### #create dataframe ###########################################################  df = df.set_index('recvd_dttm') df.index = pd.to_datetime(df.index, format='%m/%d/%y %h:%m')  result = df.groupby([lambda idx: idx.month, 'companyname']).agg(len).reset_index() result.columns = ['month', 'companyname', 'numbercalls'] pivot_table = result.pivot(index='month', columns='companyname', values='numbercalls').fillna(0) s = pivot_table.sum().sort(ascending=false,inplace=false) pivot_table = pivot_table.ix[:,s.index[:30]] pivot_table = pivot_table.transpose() pivot_table = pivot_table.reset_index() pivot_table['companyname'] = [str(x) x in pivot_table['companyname']] companies = list(pivot_table['companyname']) pivot_table = pivot_table.set_index('companyname') pivot_table.to_csv('pivot_table.csv') 

then use pivot table create ordereddict plotting

########################################################### #create ordereddict plotting ###########################################################   months = [pivot_table[(m)].astype(float).values m in range(1, 13)] names = ["jan", "feb", "mar","apr","may","jun","jul","aug","sep","oct","nov", "dec"] months_dict = ordereddict(list(zip(names, months)))  ########################################################### #plot! ###########################################################   palette = brewer["rdylgn"][8]  hover = hovertool(     tooltips = [         ("month", "@months"),         ("number of calls", "@numbercalls"),         ] ) output_file("stacked_bar.html") bar = bar(months_dict, companies, title="number of calls each month", palette = palette, legend = "top_right", width = 1200, height=900, stacked=true) bar.add_tools(hover)   show(bar) 

does have ideas on how approach modifying code can work shorter time spans? thinking modification in ordereddict section. possibly making len(recvd_dttm) iterate over?

for days in month ('2015-07' say) change

result = df.groupby([lambda idx: idx.month, 'companyname']).agg(len).reset_index() 

to like

month = '2015-07' result = df.loc[month].groupby([lambda idx: idx.day, 'companyname']).agg(len).reset_index() 

and replace 'month' 'day' below. wouldn't have bother ordereddict etc. in case ints. week

start, end = '2015-07-06', '2015-07-12' result = df.loc[start: end].groupby(             [lambda idx: idx.dayofweek, 'companyname']).agg(len).reset_index()