I have a CSV file with employee information. There are multiple records for an employee describing his monthly information. I want to create a consolidated dataframe where there are columns for each month and the number of leave days availed for each month is stored in the appropriate column
Emp. Code Month Year leave Days
1 2-2022 10
2 3-2022 15
1 3-2022 20
2 2-2022 2
1 4-2022 3
1 5-2022 2
2 6-2022 4
expected output
empcode leavedays-Feb leavedays-march leavedays-april
1 10 15 3
2 2 15 nil
Related
I am making a table up that will sum all matches of a company found within a specific time period. I need to also exclude certain months if they are inserted into a cell as mm/yy. Excluding one month is fine but when i type 10/22, 11/22, it will sum everthing. THe below code is what i am using with U$4 being the end of a month minus the tracking period which is 90 days. Note that the Raw Data that it is reading from only goes to end of November.
=IF([#[Company Name]]="","",SUM(IF(ISNUMBER(SEARCH([#[Company Name]],RawData[Description]))=TRUE,IF(RawData[Home]=XLOOKUP($D$1,HomeList[Home Code],HomeList[Home]),IF(RawData[Source]="Spend Money",IF(RawData[Date]<=U$4,IF(RawData[Date]>=U$4-[#[Tracking period (Days)]],1,0)))))))
With one date inserted which is correct:
28/Feb 31/Mar 30/Apr 31/May 30/Jun 31/Jul 31/Aug 30/Sep 31/Oct 30/Nov 31/Dec 31/Jan
Exclude Company Name Tracking period (Days) Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12
11/22 CLH 90 0 0 0 0 0 0 0 1 2 2 1 0
With multiple months inserted which is incorrect:
28/Feb 31/Mar 30/Apr 31/May 30/Jun 31/Jul 31/Aug 30/Sep 31/Oct 30/Nov 31/Dec 31/Jan
Exclude Company Name Tracking period (Days) Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12
10/22,11/22 CLH 90 0 0 0 0 0 0 0 2 3 8 6 5
Expected if multiple months as it has found one match for September so counts it
28/Feb 31/Mar 30/Apr 31/May 30/Jun 31/Jul 31/Aug 30/Sep 31/Oct 30/Nov 31/Dec 31/Jan
Exclude Company Name Tracking period (Days) Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12
10/22,11/22 CLH 90 0 0 0 0 0 0 0 1 1 1 0 0
Had to use MATCH with the TEXTSPLIT for it to work
=IF([#[Company Name]]="","",SUM(IF(ISNUMBER(SEARCH([#[Company Name]],RawData[Description]))=TRUE,IF(RawData[Home]=XLOOKUP($D$1,HomeList[Home Code],HomeList[Home]),IF(RawData[Source]="Spend Money",IF(RawData[Date]<=S$4,IF(RawData[Date]>=S$4-[#[Tracking period (Days)]],IF(ISNUMBER(MATCH(RawData[Find Date],TEXTSPLIT([#Exclude],","),)),0,1))))))))
I created a dataframe form a csv file containing data on number of deaths by year (running from 1946 to 2021) and month (within year):
dataD = pd.read_csv('MY_FILE.csv', sep=',')
First rows (out of 902...) of output are :
dataD
Year Month Deaths
0 2021 2 55500
1 2021 1 65400
2 2020 12 62800
3 2020 11 64700
4 2020 10 56900
As expected, the dataframe contains an index numbered 0,1,2, ... and so on.
Now, I pivot this dataframe in order to have only 1 row by year and months in column, using the following code:
dataDW = dataD.pivot(index='Year', columns='Month', values='Deaths')
The first rows of the result are now:
Month 1 2 3 4 5 6 7 8 9 10 11 12
Year
1946 70900.0 53958.0 57287.0 45376.0 42591.0 37721.0 37587.0 34880.0 35188.0 37842.0 42954.0 49596.0
1947 60453.0 56891.0 56442.0 45121.0 42605.0 37894.0 38364.0 36763.0 35768.0 40488.0 41361.0 46007.0
1948 46161.0 45412.0 51983.0 43829.0 42003.0 37084.0 39069.0 35272.0 35314.0 39588.0 43596.0 53899.0
1949 87861.0 58592.0 52772.0 44154.0 41896.0 39141.0 40042.0 37372.0 36267.0 40534.0 47049.0 47918.0
1950 51927.0 47749.0 50439.0 47248.0 45515.0 40095.0 39798.0 38124.0 37075.0 42232.0 44418.0 49860.0
My question is:
What do I have to change in the previous pivoting code in order to find again the index 0,1,2,..etc. when I output the pivoted file? I think I need to specify index=*** in order to make the pivot instruction run. But afterwards, I would like to recover an index "as usual" (if I can say), exactly like in my first file dataD.
Any possibility?
You can reset_index() after pivoting:
dataDW = dataD.pivot(index='Year', columns='Month', values='Deaths').reset_index()
This would give you the following:
Month Year 1 2 3 4 5 6 7 8 9 10 11 12
0 1946 70900.0 53958.0 57287.0 45376.0 42591.0 37721.0 37587.0 34880.0 35188.0 37842.0 42954.0 49596.0
1 1947 60453.0 56891.0 56442.0 45121.0 42605.0 37894.0 38364.0 36763.0 35768.0 40488.0 41361.0 46007.0
2 1948 46161.0 45412.0 51983.0 43829.0 42003.0 37084.0 39069.0 35272.0 35314.0 39588.0 43596.0 53899.0
3 1949 87861.0 58592.0 52772.0 44154.0 41896.0 39141.0 40042.0 37372.0 36267.0 40534.0 47049.0 47918.0
4 1950 51927.0 47749.0 50439.0 47248.0 45515.0 40095.0 39798.0 38124.0 37075.0 42232.0 44418.0 49860.0
Note that the "Month" here might look like the index name but is actually df.columns.name. You can unset it if preferred:
df.columns.name = None
Which then gives you:
Year 1 2 3 4 5 6 7 8 9 10 11 12
0 1946 70900.0 53958.0 57287.0 45376.0 42591.0 37721.0 37587.0 34880.0 35188.0 37842.0 42954.0 49596.0
1 1947 60453.0 56891.0 56442.0 45121.0 42605.0 37894.0 38364.0 36763.0 35768.0 40488.0 41361.0 46007.0
2 1948 46161.0 45412.0 51983.0 43829.0 42003.0 37084.0 39069.0 35272.0 35314.0 39588.0 43596.0 53899.0
3 1949 87861.0 58592.0 52772.0 44154.0 41896.0 39141.0 40042.0 37372.0 36267.0 40534.0 47049.0 47918.0
4 1950 51927.0 47749.0 50439.0 47248.0 45515.0 40095.0 39798.0 38124.0 37075.0 42232.0 44418.0 49860.0
i have data frame which contains fields casenumber , count and credated date .here created date is months which are in numerical i want to make dataframe as arrenge the ranges to the count acoording to createddate column
Here i used below code but i didnot match my requirement.i have data frame which contains fields casenumber , count and credated date .here created date is months which are in numerical i want to make dataframe as arrenge the ranges to the count acoording to createddate column
i have data frame as below
casenumber count CREATEDDATE
3820516 1 jan
3820547 1 jan
3820554 2 feb
3820562 1 feb
3820584 1 march
4226616 1 april
4226618 2 may
4226621 2 may
4226655 1 june
4226663 1 june
Here i used below code but i didnot match my requirement.i have data frame which contains fields casenumber , count and credated date .here created date is months which are in numerical i want to make dataframe as arrenge the ranges to the count acoording to createddate column
import pandas as pd
import numpy as np
df = pd.read_excel(r"")
bins = [0, 1 ,4,8,15, np.inf]
names = ['0-1','1-4','4-8','8-15','15+']
df1 = df.groupby(pd.cut(df['CREATEDDATE'],bins,labels=names))['casenumber'].size().reset_index(name='No_of_times_statuschanged')
CREATEDDATE No_of_times_statuschanged
0 0-1 2092
1 1-4 9062
2 4-8 12578
3 8-15 3858
4 15+ 0
I got the above data as out put but my expected should be range for month on month based on the cases per month .
expected output should be like
CREATEDDATE jan feb march april may june
0-1 1 2 3 4 5 6
1-4 3 0 6 7 8 9
4-8 4 6 3 0 9 2
8-15 0 3 4 5 8 9
I got the above data as out put but my expected should be range for month on month based on the cases per month .
expected output should be like
Use crosstab with change CREATEDDATE to count for pd.cut and change order of column by subset by list of columns names:
#add another months if necessary
months = ["jan", "feb", "march", "april", "may", "june"]
bins = [0, 1 ,4,8,15, np.inf]
names = ['0-1','1-4','4-8','8-15','15+']
df1 = pd.crosstab(pd.cut(df['count'],bins,labels=names), df['CREATEDDATE'])[months]
print (df1)
CREATEDDATE jan feb march april may june
count
0-1 2 1 1 1 0 2
1-4 0 1 0 0 2 0
Another idea is use ordered categoricals:
df1 = pd.crosstab(pd.cut(df['count'],bins,labels=names),
pd.Categorical(df['CREATEDDATE'], ordered=True, categories=months))
print (df1)
col_0 jan feb march april may june
count
0-1 2 1 1 1 0 2
1-4 0 1 0 0 2 0
I have a DataFrame like:
Code Date sales
1 2/2013 10
1 3/2013 11
2 3/2013 12
2 4/2013 14
...
I want to convert it into a DataFrame with a timeline, code, and sales of each type of item:
Date Code Sales1 Code Sales2
2/2013 1 10 NA NA
3/2013 1 11 2 12
4/2013 NA NA 2 14
....
or into a simpler way:
Date Code Sales1 Date Code Sales2 .....
2/2013 1 10 3/2013 2 12
3/2013 1 11 4/2013 2 14
or even into the simplest way, splitting into many small DataFrames
IIUC using concatwith the groupby result
df.index=df.groupby('Code').cumcount()# create the key for concat
pd.concat([x for _,x in df.groupby('Code')],1)
Out[392]:
Code Date sales Code Date sales
0 1 2/2013 10 2 3/2013 12
1 1 3/2013 11 2 4/2013 14
Actually, I was stupid to split the data that way, I rethink and solve the problem with the pivot_table
pd.pivot_table(df, values = ['sales'], index = ['code'], columns = ['date'])
and the result should be like.
sum
date 2/2013 3/2013 4/2013 ....
code
1 10 11 NaN
2 NaN 12 14
...
I am working on an algorithm, which requires grouping by two columns. Pandas supports grouping by two columns by using:
df.groupby([col1, col2])
But the resulting dataframe is not the required dataframe
Work Setup:
Python : v3.5
Pandas : v0.18.1
Pandas Dataframe - Input Data:
Type Segment
id
1 Domestic 1
2 Salary 3
3 NRI 1
4 Salary 4
5 Salary 3
6 NRI 4
7 Salary 4
8 Salary 3
9 Salary 4
10 NRI 4
Required Dataframe:
Count of [Domestic, Salary, NRI] in each Segment
Domestic Salary NRI
Segment
1 1 3 1
3 0 0 0
4 0 3 2
Experiments:
group = df.groupby(['Segment', 'Type'])
group.size()
Segment Type Count
1 Domestic 1
NRI 1
3 Salary 3
4 Salary 3
NRI 2
I am able to achieve the required dataframe using MS Excel Pivot Table feature. Is there any way, where I can achieve similar results using pandas?
After the Groupby.size operation, a multi-index(2 level index) series object gets created that needs to be converted into a dataframe, which could be done by unstacking the 2nd level index and optionally filling NaNs obtained with 0.
df.groupby(['Segment', 'Type']).size().unstack(level=1, fill_value=0)