I have employee table, where employee id and supervisor is present. I want to find the hierarchy for the employee in five levels.
Example: Employee 1 is reported to 2, 2 reported to 4,4 reported to 17, 17 reported to 20. But we not able to find 20 supervisor so we kept the supervisor for 20 is 20 itself.
EmployeeID
SupervisiorID
1
2
2
4
8
6
9
5
6
3
5
10
4
17
3
15
10
20
15
20
17
20
16
21
15
13
14
12
13
11
Excepted output
EmployeeID
SupervisiorID_1
SupervisiorID_2
SupervisiorID_3
SupervisiorID_4
SupervisiorID_5
1
2
4
17
20
20
2
4
17
20
20
20
8
6
3
15
20
20
9
5
10
20
20
20
6
3
15
20
20
20
5
10
20
20
20
20
4
17
20
20
20
20
3
15
20
20
20
20
10
20
20
20
20
20
15
20
20
20
20
20
17
20
20
20
20
20
16
21
21
21
21
21
15
13
11
11
11
11
14
12
12
12
12
12
13
11
11
11
11
11
How can we achieve this in Spark using dataframe recursively.
Although this has been asked many times, someone here https://dwgeek.com/spark-sql-recursive-dataframe-pyspark-and-scala.html/ has solved this.
If you only have 5 levels, than it is better to use 4 joins to do the job.
In my point of view, spark doesn't support natively recursive solutions for such scenario. If you really want to do it in a recursive way, you may need to collect the data u need and do it on driver locally.
Related
I have a dataframe with multiple columns and 700+ rows and a series of 27 rows. I want to create a new column i.e. series in dataframe as per matching indexes with predefined column in df
data frame I have and need to add series which contains the same indexes of "Reason for absence"
ID Reason for absence Month of absence Day of the week Seasons
0 11 26 7 3 1
1 36 0 7 3 1
2 3 23 7 4 1
3 7 7 7 5 1
4 11 23 7 5 1
5 3 23 7 6 1
6 10 22 7 6 1
7 20 23 7 6 1
8 14 19 7 2 1
9 1 22 7 2 1
10 20 1 7 2 1
11 20 1 7 3 1
12 20 11 7 4 1
13 3 11 7 4 1
14 3 23 7 4 1
15 24 14 7 6 1
16 3 23 7 6 1
17 3 21 7 2 1
18 6 11 7 5 1
19 33 23 8 4 1
20 18 10 8 4 1
21 3 11 8 2 1
22 10 13 8 2 1
23 20 28 8 6 1
24 11 18 8 2 1
25 10 25 8 2 1
26 11 23 8 3 1
27 30 28 8 4 1
28 11 18 8 4 1
29 3 23 8 6 1
30 3 18 8 2 1
31 2 18 8 5 1
32 1 23 8 5 1
33 2 18 8 2 1
34 3 23 8 2 1
35 10 23 8 2 1
36 11 24 8 3 1
37 19 11 8 5 1
38 2 28 8 6 1
39 20 23 8 6 1
40 27 23 9 3 1
41 34 23 9 2 1
42 3 23 9 3 1
43 5 19 9 3 1
44 14 23 9 4 1
this is series table s_conditions
0 Not absent
1 Infectious and parasitic diseases
2 Neoplasms
3 Diseases of the blood
4 Endocrine, nutritional and metabolic diseases
5 Mental and behavioural disorders
6 Diseases of the nervous system
7 Diseases of the eye
8 Diseases of the ear
9 Diseases of the circulatory system
10 Diseases of the respiratory system
11 Diseases of the digestive system
12 Diseases of the skin
13 Diseases of the musculoskeletal system
14 Diseases of the genitourinary system
15 Pregnancy and childbirth
16 Conditions from perinatal period
17 Congenital malformations
18 Symptoms not elsewhere classified
19 Injury
20 External causes
21 Factors influencing health status
22 Patient follow-up
23 Medical consultation
24 Blood donation
25 Laboratory examination
26 Unjustified absence
27 Physiotherapy
28 Dental consultation
dtype: object
I tried this
df1.insert(loc=0, column="Reason_for_absence", value=s_conditons)
out- this is wrong because i need the reason_for_absence colum according to the index of reason for absence and s_conditions
Reason_for_absence ID Reason for absence \
0 Not absent 11 26
1 Infectious and parasitic diseases 36 0
2 Neoplasms 3 23
3 Diseases of the blood 7 7
4 Endocrine, nutritional and metabolic diseases 11 23
5 Mental and behavioural disorders 3 23
6 Diseases of the nervous system 10 22
7 Diseases of the eye 20 23
8 Diseases of the ear 14 19
9 Diseases of the circulatory system 1 22
10 Diseases of the respiratory system 20 1
11 Diseases of the digestive system 20 1
12 Diseases of the skin 20 11
13 Diseases of the musculoskeletal system 3 11
14 Diseases of the genitourinary system 3 23
15 Pregnancy and childbirth 24 14
16 Conditions from perinatal period 3 23
17 Congenital malformations 3 21
18 Symptoms not elsewhere classified 6 11
19 Injury 33 23
20 External causes 18 10
21 Factors influencing health status 3 11
22 Patient follow-up 10 13
23 Medical consultation 20 28
24 Blood donation 11 18
25 Laboratory examination 10 25
26 Unjustified absence 11 23
27 Physiotherapy 30 28
28 Dental consultation 11 18
29 NaN 3 23
30 NaN 3 18
31 NaN 2 18
32 NaN 1 23
i am getting output upto 28 rows and NaN values after that. Instead, I need correct order of series according to indexes for all the rows
While this question is a bit confusing, it seems the desire is to match the series index with the dataframe "Reason for Absence" column. If this is correct, below is a small example of how to accomplish. Keep in mind, the resulting dataframe will be sorted based on the 'Reason for Absence Numerical' column. If my understanding is incorrect, please clarify this question so we can better assist you.
d = {'ID': [11,36,3], 'Reason for Absence Numerical': [3,2,1], 'Day of the Week': [4,2,6]}
dataframe = pd.DataFrame(data=d)
s = {0: 'Not absent', 1:'Neoplasms', 2:'Injury', 3:'Diseases of the eye'}
disease_series = pd.Series(data=s)
def add_series_to_df(df, series, index_val):
df_filtered = df[df['Reason for Absence Numerical'] == index_val].copy()
series_filtered = series[series.index == index_val]
if not df_filtered.empty:
df_filtered['Reason for Absence Text'] = series_filtered.item()
return df_filtered
x = [add_series_to_df(dataframe, disease_series, index_val) for index_val in range(len(disease_series.index))]
new_df = pd.concat(x)
print(new_df)
I have a time series as a dataframe. The first column is the week number, the second are values for that week. The first week (22) and the last week (48), are the lower and upper bounds of the time series. Some weeks are missing, for example, there is no week 27 and 28. I would like to resample this series such that there are no missing weeks. Where a week was inserted, I would like the corresponding value to be zero. This is my data:
week value
0 22 1
1 23 2
2 24 2
3 25 3
4 26 2
5 29 3
6 30 3
7 31 3
8 32 7
9 33 4
10 34 5
11 35 4
12 36 2
13 37 3
14 38 10
15 39 5
16 40 7
17 41 10
18 42 11
19 43 15
20 44 9
21 45 13
22 46 5
23 47 6
24 48 2
I am wondering if this can be achieved in Pandas without creating a loop from scratch. I have looked into pd.resample, but can't achieve the results I am looking for.
I would set week as index, reindex with fill_value option:
start, end = df['week'].agg(['min','max'])
df.set_index('week').reindex(np.arange(start, end+1), fill_value=0).reset_index()
Output (head):
week value
0 22 1
1 23 2
2 24 2
3 25 3
4 26 2
5 27 0
6 28 0
7 29 3
8 30 3
I have two dirs base and to_move. There are 10 files in base, which are named
0 1 2 3 4 5 6 7 8 9, and 3 files, 0 1 2, in to_move. What I want is to move the 3 files in to_move to base, with their names changed to 10 11 12.
Inside the dir to_move, I run the command
tmp=$(ls);for item in ${tmp[#]};do dst=$((item+10));echo $dst $item;done
what I got is
10 0
11 1
12 2
11 1
20 10
21 11
22 12
23 13
24 14
25 15
26 16
27 17
28 18
29 19
12 2
30 20
31 21
32 22
33 23
34 24
35 25
36 26
37 27
38 28
13 3
14 4
15 5
16 6
17 7
18 8
19 9
This makes no sense to me, it seems $(($item+10)) has some weird effects on $item.
Why this happens? And how can I modify the command to get this output?
10 0
11 1
12 2
I downloaded a dataset in .csv format from kaggle which is about lego. There's a "Ages" column like this:
df['Ages'].unique()
array(['6-12', '12+', '7-12', '10+', '5-12', '8-12', '4-7', '4-99', '4+',
'9-12', '16+', '14+', '9-14', '7-14', '8-14', '6+', '2-5', '1½-3',
'1½-5', '9+', '5-8', '10-21', '8+', '6-14', '5+', '10-16', '10-14',
'11-16', '12-16', '9-16', '7+'], dtype=object)
These categories are the suggested ages for using and playing with the legos.
I'm intended to do some statistical analysis with these age bins. For example, I want to check the mean of these suggested ages.
However, since the type of each of them is string:
type(lego_dataset.loc[0]['Ages'])
str
I don't know how to work on the data.
I've already check How to categorize a range of values in Pandas DataFrame
But imagine there are 100 unique bins. It's not reasonable to prepare a list of 100 labels for each category. There should be a better way.
Not entirely sure what output you are looking for. See if the below code & output helps you.
df['Lage'] = df['Ages'].str.split('[-+]').str[0]
df['Uage'] = df['Ages'].str.split('[-+]').str[-1]
or
df['Lage'] = df['Ages'].str.extract('(\d+)', expand=True) #you don't get the fractions for row 17 & 18
df['Uage'] = df['Ages'].str.split('[-+]').str[-1]
Input
Ages
0 6-12
1 12+
2 7-12
3 10+
4 5-12
5 8-12
6 4-7
7 4-99
8 4+
9 9-12
10 16+
11 14+
12 9-14
13 7-14
14 8-14
15 6+
16 2-5
17 1½-3
18 1½-5
19 9+
20 5-8
21 10-21
22 8+
23 6-14
24 5+
25 10-16
26 10-14
27 11-16
28 12-16
29 9-16
30 7+
Output1
Ages Lage Uage
0 6-12 6 12
1 12+ 12
2 7-12 7 12
3 10+ 10
4 5-12 5 12
5 8-12 8 12
6 4-7 4 7
7 4-99 4 99
8 4+ 4
9 9-12 9 12
10 16+ 16
11 14+ 14
12 9-14 9 14
13 7-14 7 14
14 8-14 8 14
15 6+ 6
16 2-5 2 5
17 1½-3 1½ 3
18 1½-5 1½ 5
19 9+ 9
20 5-8 5 8
21 10-21 10 21
22 8+ 8
23 6-14 6 14
24 5+ 5
25 10-16 10 16
26 10-14 10 14
27 11-16 11 16
28 12-16 12 16
29 9-16 9 16
30 7+ 7
Output2
Ages Lage Uage
0 6-12 6 12
1 12+ 12
2 7-12 7 12
3 10+ 10
4 5-12 5 12
5 8-12 8 12
6 4-7 4 7
7 4-99 4 99
8 4+ 4
9 9-12 9 12
10 16+ 16
11 14+ 14
12 9-14 9 14
13 7-14 7 14
14 8-14 8 14
15 6+ 6
16 2-5 2 5
17 1½-3 1 3
18 1½-5 1 5
19 9+ 9
20 5-8 5 8
21 10-21 10 21
22 8+ 8
23 6-14 6 14
24 5+ 5
25 10-16 10 16
26 10-14 10 14
27 11-16 11 16
28 12-16 12 16
29 9-16 9 16
30 7+ 7
I do calculations on 64 elements (for p=1:64 function end) and pull out the result values in an Excel file.
Is there any way to arrange the result values for each element row by row (the values of the first element should appear on the first row, the values of the second element should appear on the second row and so on)?
I used P=reshape(A,[],16) but Matlab pushes the values from right to the left mixing them.
For example,
If I set the loop for the calculation p=1:1 and use P=reshape(A,[],16) the result is:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
If I set p=1:2 the result becomes:
for element 1: 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
for element 2: 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
(the values of element 2 are: 17 18 19 20 21 22 23 24 25 ... 32)
The result for p=1:2 should be:
for element 1: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
for element 2: 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
for element 3: 33 34 35 ,etc...
Try this:
P=reshape(A,16,[])'
Is this what you need?