Sorting in a Pandas pivot_table
Question
I have been looking all over trying to figure out how to sort my pivot table correctly and I haven't had any luck.
client unit task hours month
0 A DVADA Account Management 6.50 January
1 A DVADA Buying 1.25 January
2 A DVADA Meeting / Call 0.50 January
3 A DVADA Account Management 3.00 January
4 A DVADA Billing 2.50 February
5 A DVADA Account Management 6.50 February
6 A DVADA Buying 1.25 February
7 A DVADA Meeting / Call 0.50 February
8 A DVADA Account Management 3.00 February
9 A DVADA Billing 2.50 February
10 A DVADA Billing 2.50 December
11 A DVADA Account Management 6.50 December
12 A DVADA Buying 1.25 December
13 A DVADA Meeting / Call 0.50 December
14 A DVADA Account Management 3.00 December
15 A DVADA Billing 2.50 December
16 A DVADA Account Management 6.50 August
17 A DVADA Buying 1.25 August
18 A DVADA Meeting / Call 0.50 August
19 A DVADA Account Management 3.00 August
20 A DVADA Account Management 6.50 April
21 A DVADA Buying 1.25 April
22 A DVADA Meeting / Call 0.50 April
23 A DVADA Account Management 3.00 April
24 B DVADA Account Management 6.50 January
25 B DVADA Buying 1.25 January
26 B DVADA Meeting / Call 0.50 January
27 B DVADA Account Management 3.00 January
28 B DVADA Billing 2.50 February
29 B DVADA Account Management 6.50 February
30 B DVADA Buying 1.25 February
31 B DVADA Meeting / Call 0.50 February
32 B DVADA Account Management 3.00 February
33 B DVADA Billing 2.50 February
34 B DVADA Billing 2.50 December
35 B DVADA Account Management 6.50 December
36 B DVADA Buying 1.25 December
37 B DVADA Meeting / Call 0.50 December
38 B DVADA Account Management 3.00 December
39 B DVADA Billing 2.50 December
40 B DVADA Account Management 6.50 August
41 B DVADA Buying 1.25 August
42 B DVADA Meeting / Call 0.50 August
43 B DVADA Account Management 3.00 August
44 B DVADA Account Management 6.50 April
45 B DVADA Buying 1.25 April
46 B DVADA Meeting / Call 0.50 April
47 C DVADA Account Management 3.00 April
48 C DVADA Account Management 6.50 January
49 C DVADA Buying 1.25 January
50 C DVADA Meeting / Call 0.50 January
51 C DVADA Account Management 3.00 January
52 C DVADA Billing 2.50 February
53 C DVADA Account Management 6.50 February
54 C DVADA Buying 1.25 February
55 C DVADA Meeting / Call 0.50 February
56 C DVADA Account Management 3.00 February
57 C DVADA Billing 2.50 February
58 C DVADA Billing 2.50 December
59 C DVADA Account Management 6.50 December
60 C DVADA Buying 1.25 December
61 C DVADA Meeting / Call 0.50 December
62 C DVADA Account Management 3.00 December
63 C DVADA Billing 2.50 December
64 C DVADA Account Management 6.50 August
65 C DVADA Buying 1.25 August
66 C DVADA Meeting / Call 0.50 August
67 C DVADA Account Management 3.00 August
68 C DVADA Account Management 6.50 April
69 C DVADA Buying 1.25 April
70 C DVADA Meeting / Call 0.50 April
71 C DVADA Account Management 3.00 April
df = pd.pivot_table(vp_clients, values='hours', index=['client', 'month'], aggfunc=sum)
Which returns a pivot table with three columns (client, month, hours). Each client has 12 months (Jan-Dec) and each of those months has a hours for that month.
hours
client month
A April 203.50
August 227.75
December 159.75
February 203.25
January 199.25
B April 203.50
August 227.75
December 159.75
February 203.25
January 199.25
C April 203.50
August 227.75
December 159.75
February 203.25
January 199.25
I want to sort this pivot table by the months but keep the client column in tacked.
hours
client month
A January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
B January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
C January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
The sorting issue is fixed with the answer below from Scott. Now I want to add a row to each client with the total hours used.
hours
client month
A January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
Total 1000.34
B January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
Total 1000.34
C January 203.50
February 227.75
March 159.75
April 203.25
May 199.90
Total 1000.34
Any help will be greatly appreciated
2 answers
solution1
4 ACCPTED 2018-02-21 21:55:08
Update to add Total at end of each client
vp_clients['month'] = pd.Categorical(vp_clients['month'],
ordered=True,
categories=['January','February','March',
'April','May','June','July',
'August','September','October',
'November','December','Total'])
df = pd.pivot_table(vp_clients, values='hours', index=['client', 'month'], aggfunc=sum)
df = df.dropna()
pd.concat([df,df.sum(level=0).assign(month='Total').set_index('month', append=True)]).sort_index()
Output:
hours
client month
A January 11.25
February 16.25
April 11.25
August 11.25
December 16.25
Total 66.25
B January 11.25
February 16.25
April 8.25
August 11.25
December 16.25
Total 63.25
C January 11.25
February 16.25
April 14.25
August 11.25
December 16.25
Total 69.25
Let's use pd.Categorical :
vp_clients['month'] = pd.Categorical(vp_clients['month'],
ordered=True,
categories=['January','February','March',
'April','May','June','July',
'August','September','October',
'November','December'])
df = pd.pivot_table(vp_clients, values='hours', index=['client', 'month'], aggfunc=sum)
df.dropna()
Output:
hours
client month
A January 11.25
February 16.25
April 11.25
August 11.25
December 16.25
B January 11.25
February 16.25
April 8.25
August 11.25
December 16.25
C January 11.25
February 16.25
April 14.25
August 11.25
December 16.25
solution2
1 2018-02-21 22:37:37
Additionally, as commented, since you are not pivoting values to new columns in a wide format, consider simply using groupby() . And re-consider reindex() for custom January-December order, specifying the level and interfacing with python's built-in calendar module.
import calendar
...
grp_df = df.groupby(['client', 'month']).agg({'hours': 'sum'})\
.reindex(level=1, labels=calendar.month_name)
# hours
# client month
# A January 11.25
# February 16.25
# April 11.25
# August 11.25
# December 16.25
# B January 11.25
# February 16.25
# April 8.25
# August 11.25
# December 16.25
# C January 11.25
# February 16.25
# April 14.25
# August 11.25
# December 16.25
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