I am trying to find the unique number of Customers, O (Orders), Q (Quotations) and D (Drafts) our team has dealt with on a particular day from this sample dataset. Please note that there are repeated "Quote/Order #"s in the dataset. I need to figure out the unique numbers of Q/O/D on a given day.
I have figured out all the values except the fields highlighted in light orange color of my Expected output table. Can someone help me figure out the MS Excel formula for these four values as requested above?
Below is the given dataset. Please note that there can be empty values against a date. But those will always be found in the bottom few rows of the table:
Date
Job #
Job Type
Quote/Ordr #
Parts
Customer
man-hr
4-Apr-22
1
O
307585
1
FRU
0.35
4-Apr-22
2
D
307267
28
ATM
4.00
4-Apr-22
2
D
307267
25
ATM
3.75
4-Apr-22
2
D
307267
6
ATM
0.17
4-Apr-22
3
D
307438
3
ELCTRC
0.45
4-Apr-22
4
D
307515
7
ATM
0.60
4-Apr-22
4
D
307515
5
ATM
0.55
4-Apr-22
4
D
307515
4
ATM
0.35
4-Apr-22
5
O
307587
4
PULSE
0.30
4-Apr-22
6
O
307588
3
PULSE
0.40
5-Apr-22
1
O
307623
1
WST
0.45
5-Apr-22
2
O
307629
4
CG
0.50
5-Apr-22
3
O
307630
10
SUPER
1.50
5-Apr-22
4
O
307631
3
SUPER
0.60
5-Apr-22
5
O
307640
7
CAM
0.40
5-Apr-22
6
Q
307527
6
WG
0.55
5-Apr-22
6
Q
307527
3
WG
0.30
5-Apr-22
To figure out the unique "Number of Jobs" on Apr 4, I used the Excel formula:
=MAXIFS($K$3:$K$20,$J$3:$J$20,R3) Where, R3 ='4-Apr-22'
To figure out the unique "Number of D (Draft) Jobs" I used the Excel formula:
=SUMIFS($P$3:$P$20,$J$3:$J$20,R3,$L$3:$L$20,"D")
[1
[2
Related
I have a time series data given below:
date product price amount
11/01/2019 A 10 20
11/02/2019 A 10 20
11/03/2019 A 25 15
11/04/2019 C 40 50
11/05/2019 C 50 60
I have a high dimensional data, and I have just added the simplified version with two columns {price, amount}. I am trying to transform it relatively based on time index illustrated below:
date product price amount
11/01/2019 A NaN NaN
11/02/2019 A 0 0
11/03/2019 A 15 -5
11/04/2019 C NaN NaN
11/05/2019 C 10 10
I am trying to get relative changes of each product based on time indexes. If previous date does not exist for a specified product, I am adding "NaN".
Can you please tell me is there any function to do this?
Group by product and use .diff()
df[["price", "amount"]] = df.groupby("product")[["price", "amount"]].diff()
output :
date product price amount
0 2019-11-01 A NaN NaN
1 2019-11-02 A 0.0 0.0
2 2019-11-03 A 15.0 -5.0
3 2019-11-04 C NaN NaN
4 2019-11-05 C 10.0 10.0
I have a pandas dataframe as follows:
Name Age City Country percentage
a Jack 34 Sydney Australia 0.23
b Riti 30 Delhi India 0.45
c Vikas 31 Mumbai India 0.55
d Neelu 32 Bangalore India 0.73
e John 16 New York US 0.91
f Mike 17 las vegas US 0.78
I am planning to add one more column called bucket whose definition depends on the percentage column as follows:
less than 0.25 = 1
between 0.25 and 0.5 = 2
between 0.5 and 0.75 = 3
greater than 0.75 = 4
I tried the inbuilt conditions and choices properties of pandas follows:
conditions = [(df_obj['percentage'] < .25),
(df_obj['percentage'] >=.25 & df_obj['percentage'] < .5),
(df_obj['percentage'] >=.5 & df_obj['percentage'] < .75),
(df_obj['percentage'] >= .75)]
choices = [1,2,3,4]
df_obj['bucket'] = np.select(conditions, choices)
However, this gives me a random error as follows in the line where I create the conditions:
TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool]
A quick fix to your code is that you need more parentheses, for example:
((df_obj['percentage'] >=.25) & (df_obj['percentage'] < .5) )
^ ^ ^ ^
However, I think it's cleaner with pd.cut:
pd.cut(df['percentage'], bins=[0,0.25, 0.5, 0.75, 1],
include_lowest=True, right=False,
labels=[1,2,3,4])
Or since your buckets are linear:
df['bucket'] = (df['percentage']//0.25).add(1).astype(int)
Output
Name Age City Country percentage bucket
a Jack 34 Sydney Australia 0.23 1
b Riti 30 Delhi India 0.45 2
c Vikas 31 Mumbai India 0.55 3
d Neelu 32 Bangalore India 0.73 3
e John 16 New York US 0.91 4
f Mike 17 las vegas US 0.78 4
I think the easiest/most readable way to do this is to use the apply function:
def percentage_to_bucket(percentage):
if percentage < .25:
return 1
elif percentage >= .25 and percentage < .5:
return 2
elif percentage >= .5 and percentage < .75:
return 3
else:
return 4
df["bucket"] = df["percentage"].apply(percentage_to_bucket)
Pandas apply will take each value of a given column and apply the passed function to this value, returning a pandas series with the results, which you can then assign to your new column.
Having a DataFrame structured as follows:
country A B C D
0 Albany 5.2 4.7 253.75 4
1 China 7.5 3.4 280.72 3
2 Portugal 4.6 7.5 320.00 6
3 France 8.4 3.6 144.00 3
4 Greece 2.1 10.0 331.00 6
I wanted to get something like this:
cost A B
country C D C D
Albany 2.05 4 1.85 4
China 2.67 3 1.21 3
Portugal 1.44 6 2.34 6
France 5.83 3 2.50 3
Greece 0.63 6 3.02 6
I mean, get the columns A and B as headers over C and D, keeping D the same with its constant value, and calculating in C the percentage resulting of the header over C. Example for Albany:
value C in A: (5.2/253.75)*100 = 2.05
value C in B: (4.7/253.75)*100 = 1.85
Is there any way to do it?
Thanks!
You can divide multiple columns, here A and B by DataFrame.div, then DataFrame.reindex by MultiIndex created by MultiIndex.from_product and last set D columns by original with MultiIndex slicers:
cols = ['A','B']
mux = pd.MultiIndex.from_product([cols, ['C', 'D']])
df1 = df[cols].div(df['C'], axis=0).mul(100).reindex(mux, axis=1, level=0)
idx = pd.IndexSlice
df1.loc[:, idx[:, 'D']] = df[['D'] * len(cols)].to_numpy()
#pandas bellow 0.24
#df1.loc[:, idx[:, 'D']] = df[['D'] * len(cols)].values
print (df1)
A B
C D C D
0 2.049261 4 1.852217 4
1 2.671701 3 1.211171 3
2 1.437500 6 2.343750 6
3 5.833333 3 2.500000 3
4 0.634441 6 3.021148 6
I have a lot of parameters on which I have to calculate the year on year growth.
Type 2006-Q1 2006-Q2 2006-Q3 2006-Q4 2007-Q1 2007-Q2 2007-Q3 2007-Q4 2008-Q1 2008-Q2 2008-Q3 2008-Q4
MonMkt_IntRt 3.44 3.60 3.99 4.40 4.61 4.73 5.11 4.97 4.92 4.89 5.29 4.51
RtlVol 97.08 97.94 98.25 99.15 99.63 100.29 100.71 101.18 102.04 101.56 101.05 99.49
IntRt 4.44 5.60 6.99 7.40 8.61 9.73 9.11 9.97 9.92 9.89 7.29 9.51
GMR 9.08 9.94 9.25 9.15 9.63 10.29 10.71 10.18 10.04 10.56 10.05 9.49
I need to calculate the growth, i.e in column 2007-Q1 i need to find the growth from 2006-Q1. The formula is (2007-Q1/2006-Q1) - 1
I have gone through the link below and tried to code
Calculating year over year growth by group in Pandas
df = pd.read_csv('c:/Econometric/EconoModel.csv')
df.set_index('Type',inplace=True)
df.sort_index(axis=1, inplace=True)
df_t = df.T
df_output=(df_cd_americas_t/df_cd_americas_t.shift(4)) -1
The output is as below
Type 2006-Q1 2006-Q2 2006-Q3 2006-Q4 2007-Q1 2007-Q2 2007-Q3 2007-Q4 2008-Q1 2008-Q2 2008-Q3 2008-Q4
MonMkt_IntRt 0.3398 0.3159 0.2806 0.1285 0.0661 0.0340 0.0363 -0.0912
RtlVol 0.0261 0.0240 0.0249 0.0204 0.0242 0.0126 0.0033 -0.0166
IntRt 0.6666 0.5375 0.3919 0.2310 0.1579 0.0195 0.0856 -0.2688
GMR 0.0077 -0.031 0.1124 0.1704 0.0571 -0.024 -0.014 -0.0127
Use iloc to shift data slices. See an example on test df.
df= pd.DataFrame({i:[0+i,1+i,2+i] for i in range(0,12)})
print(df)
0 1 2 3 4 5 6 7 8 9 10 11
0 0 1 2 3 4 5 6 7 8 9 10 11
1 1 2 3 4 5 6 7 8 9 10 11 12
2 2 3 4 5 6 7 8 9 10 11 12 13
df.iloc[:,list(range(3,12))] = df.iloc[:,list(range(3,12))].values/ df.iloc[:,list(range(0,9))].values - 1
print(df)
0 1 2 3 4 5 6 7 8 9 10
0 0 1 2 inf 3.0 1.50 1.00 0.75 0.600000 0.500000 0.428571
1 1 2 3 3.0 1.5 1.00 0.75 0.60 0.500000 0.428571 0.375000
2 2 3 4 1.5 1.0 0.75 0.60 0.50 0.428571 0.375000 0.333333
11
0 0.375000
1 0.333333
2 0.300000
I could not find any issue with your code.
Simply added axis=1 to the dataframe.shift() method as you are trying to do the column comparison
I have executed the following code it is giving the result you expected.
def getSampleDataframe():
df_economy_model = pd.DataFrame(
{
'Type':['MonMkt_IntRt', 'RtlVol', 'IntRt', 'GMR'],
'2006-Q1':[3.44, 97.08, 4.44, 9.08],
'2006-Q2':[3.6, 97.94, 5.6, 9.94],
'2006-Q3':[3.99, 98.25, 6.99, 9.25],
'2006-Q4':[4.4, 99.15, 7.4, 9.15],
'2007-Q1':[4.61, 99.63, 8.61, 9.63],
'2007-Q2':[4.73, 100.29, 9.73, 10.29],
'2007-Q3':[5.11, 100.71, 9.11, 10.71],
'2007-Q4':[4.97, 101.18, 9.97, 10.18],
'2008-Q1':[4.92, 102.04, 9.92, 10.04],
'2008-Q2':[4.89, 101.56, 9.89, 10.56],
'2008-Q3':[5.29, 101.05, 7.29, 10.05],
'2008-Q4':[4.51, 99.49, 9.51, 9.49]
}) # Your data
return df_economy_model>
df_cd_americas = getSampleDataframe()
df_cd_americas.set_index('Type', inplace=True)
df_yearly_growth = (df/df.shift(4, axis=1))-1
print (df_cd_americas)
print (df_yearly_growth)
I am struggling to find the sum of distinct id's value. Example given below.
Week TID Ano Points
1 111 ANo1 1
1 112 ANo1 1
2 221 ANo2 0.25
2 222 ANo2 0.25
2 223 ANo2 0.25
2 331 ANo3 1
2 332 ANo3 1
2 333 ANo3 1
2 999 Ano9 0.25
2 998 Ano9 0.25
3 421 ANo4 0.25
3 422 ANo4 0.25
3 423 ANo4 0.25
3 531 ANo5 0.5
3 532 ANo5 0.5
3 533 ANo5 0.5
From the above data i need to bring the below result. Could anyone help please using some excel formula?
Week Points_Sum
1 1
2 1.50
3 0.75
You say "sum of distinct id's value"? All the IDs are different so I'm assuming you want to sum for each different "Ano" within the week?
=SUM(IF(FREQUENCY(IF(A$2:A$17=F2,MATCH(C$2:C$17,C$2:C$17,0)),ROW(A$2:A$17)-ROW(A$2)+1),D$2:D$17))
confirmed with CTRL+SHIFT+ENTER
where F2 contains a specific week number
Assumes that each "Ano" will always have the same points value
Probably not the most efficient solution... but this array formula works:
= SUMPRODUCT(IF($A$2:$A$15=$F2,$D$2:$D$15),1/MMULT((IF($A$2:$A$15=$F2,$D$2:$D$15)=
TRANSPOSE(IF($A$2:$A$15=$F2,$D$2:$D$15)))+0,(ROW($A$2:$A$15)>0)+0))
Note this is an array formula, so you have to press Ctrl+Shift+Enter after typing this formula instead of just Enter.
See working example below. This formula is in cell G2 and dragged down.