how to convert pandas columns to rows dynamically - python

I have following dataframe in pandas
code tank noz_sale_cumsum noz_1_sub noz_2_sub noz_1_avg noz_2_avg noz_1_flag noz_2_flag
123 1 1234 12 23 23.23 32.45 short ok
123 2 1200 13 53 33.13 22.45 excess ok
columns such as noz_1_sub, noz_2_sub, noz_1_avg, noz_2_avg, noz_1_flag and noz_2_flag are generated dynamically.
My desired dataframe would be following.
code tank noz_no noz_sale_cumsum noz_sub noz_avg noz_flag
123 1 1 1234 12 23.23 short
123 1 2 1234 23 32.45 ok
123 2 1 1200 13 33.13 excess
123 2 2 1200 53 22.45 ok
I am doing following in pandas.
first I am getting all dynamic columns in different arrays
cols_sub = [cols for cols in df.columns if re.search('noz_\d+_sub', cols)]
cols_avg = [cols for cols in df.columns if re.search('noz_\d+_avg', cols)]
cols_flag = [cols for cols in df.columns if re.search('noz_\d+_flag', cols)]
final_df = df.pivot_table(index=['code', 'tank', 'noz_sale_cumsum'], columns=[cols_sub, cols_avg, cols_flag], values=[]).reset_index()
I am not sure about values column and how do I extract number from noz like columns and put it under noz_no column. Any help is appreciated.


You can use melt to convert everything to rows then use pivot_table to convert back some rows to columns.
a = df.melt(id_vars=['code', 'tank', 'noz_sale_cumsum'])
a['noz_no'] = a.variable.map(lambda x: x.split('_')[1])
a['kpi'] = a.variable.map(lambda x: 'noz_' + x.split('_')[2])
a.pivot_table(
values='value',
index=['code', 'tank', 'noz_sale_cumsum', 'noz_no'],
columns=['kpi'], aggfunc='first'
).reset_index()

Related

pandas average across dynamic number of columns

I have a dataframe like as shown below
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10
1 1234 1231 1256 1239
2 5678 3425 3255 2345
I would like to do the below
a) get average of revenue for each customer based on latest two columns (revenue_m9 and revenue_m10)
b) get average of revenue for each customer based on latest four columns (revenue_m7, revenue_m8, revenue_m9 and revenue_m10)
So, I tried the below
df['revenue_mean_2m'] = (df['revenue_m10']+df['revenue_m9'])/2
df['revenue_mean_4m'] = (df['revenue_m10']+df['revenue_m9']+df['revenue_m8']+df['revenue_m7'])/4
df['revenue_mean_4m'] = df.mean(axis=1) # i also tried this but how to do for only two columns (and not all columns)
But if I wish to compute average for past 12 months, then it may not be elegant to write this way. Is there any other better or efficient way to write this? I can just key in number of columns to look back and it can compute the average based on keyed in input
I expect my output to be like as below
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10 revenue_mean_2m revenue_mean_4m
1 1234 1231 1256 1239 1867 1240
2 5678 3425 3255 2345 2800 3675.75

Use filter and slicing:
# keep only the "revenue_" columns
df2 = df.filter(like='revenue_')
# or
# df2 = df.filter(regex=r'revenue_m\d+')
# get last 2/4 columns and aggregate as mean
df['revenue_mean_2m'] = df2.iloc[:, -2:].mean(axis=1)
df['revenue_mean_4m'] = df2.iloc[:, -4:].mean(axis=1)
Output:
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10 \
0 1 1234 1231 1256 1239
1 2 5678 3425 3255 2345
revenue_mean_2m revenue_mean_4m
0 1247.5 1240.00
1 2800.0 3675.75
if column order it not guaranteed
Sort them with natural sorting
# shuffle the DataFrame columns for demo
df = df.sample(frac=1, axis=1)
# filter and reorder the needed columns
from natsort import natsort_key
df2 = df.filter(regex=r'revenue_m\d+').sort_index(key=natsort_key, axis=1)

you could try something like this in reference to this post:
n_months = 4 # you could also do this in a loop for all months range(1, 12)
df[f'revenue_mean_{n_months}m'] = df.iloc[:, -n_months:-1].mean(axis=1)

Reshape multi-header table in python

I am new to python and trying to reshape table from excel file as I have multiple header I am trying to convert first header into 2 separate column. i am attaching my code output and data here.
Input Table
import pandas as pd
import numpy as nm
df = pd.read_excel(r'.\test.xlsx', header=[0, 1])
df = (df.stack(0, dropna=False)
.rename_axis(index=('Customer','Date'), columns=None)
.reset_index())
df.to_csv(r'.\testnew.csv',index=False)
print(df)
Printed Output -
Desired Output -
Customer
Date
Budget
Actual
Amount
John
Jan-20
100
50
0
John
Feb-20
John
Mar-20
Chris
Jan-20
120
80
0
Chris
Feb-20
50
10
20
Chris
Mar-20
50
45

I believe you need DataFrame.stack:
df = pd.read_excel(r'.\test.xlsx', header=[0, 1])
df = (df.stack(0, dropna=False)
.rename_axis(index=('Customer','Date'), columns=None)
.reset_index())

Traspose groupby sublevels into columns pandas/python

gp1.groupby(by=['ID', 'CD'])['BALANCE_AM'].sum()
ID CD
4332 5 0.0
58 0.0
123 22656.0
756423 47 645087.0
123 227655.0
I want to create columns for each type of CD, what is the sum of BALANCE_AM
Desired Output
ID 5 58 123 47
4332 0 0 22656.0 NaN
756423 NaN NaN 227655.0 645087.0

Add Series.unstack and DataFrame.reset_index if necessary ID in column:
df = gp1.groupby(by=['ID', 'CD'])['BALANCE_AM'].sum().unstack().reset_index()

Another way is to use pivot_table instead of groupby:
gp1.pivot_table(values='BALANCE_AM', index='ID', columns='CD', aggfunc='sum')

Pandas get the Month Ending Values from Series

I need to get the month-end balance from a series of entries.
Sample data:
date contrib totalShrs
0 2009-04-23 5220.00 10000.000
1 2009-04-24 10210.00 20000.000
2 2009-04-27 16710.00 30000.000
3 2009-04-30 22610.00 40000.000
4 2009-05-05 28909.00 50000.000
5 2009-05-20 38409.00 60000.000
6 2009-05-28 46508.00 70000.000
7 2009-05-29 56308.00 80000.000
8 2009-06-01 66108.00 90000.000
9 2009-06-02 78108.00 100000.000
10 2009-06-12 86606.00 110000.000
11 2009-08-03 95606.00 120000.000
The output would look something like this:
2009-04-30 40000
2009-05-31 80000
2009-06-30 110000
2009-07-31 110000
2009-08-31 120000
Is there a simple Pandas method?
I don't see how I can do this with something like a groupby?
Or would I have to do something like iterrows, find all the monthly entries, order them by date and pick the last one?
Thanks.

Use Grouper with GroupBy.last, forward filling missing values by ffill with Series.reset_index:
#if necessary
#df['date'] = pd.to_datetime(df['date'])
df = df.groupby(pd.Grouper(freq='m',key='date'))['totalShrs'].last().ffill().reset_index()
#alternative
#df = df.resample('m',on='date')['totalShrs'].last().ffill().reset_index()
print (df)
date totalShrs
0 2009-04-30 40000.0
1 2009-05-31 80000.0
2 2009-06-30 110000.0
3 2009-07-31 110000.0
4 2009-08-31 120000.0

Following gives you the information you want, i.e. end of month values, though the format is not exactly what you asked:
df['month'] = df['date'].str.split('-', expand = True)[1] # split date column to get month column
newdf = pd.DataFrame(columns=df.columns) # create a new dataframe for output
grouped = df.groupby('month') # get grouped values
for g in grouped: # for each group, get last row
gdf = pd.DataFrame(data=g[1])
newdf.loc[len(newdf),:] = gdf.iloc[-1,:] # fill new dataframe with last row obtained
newdf = newdf.drop('date', axis=1) # drop date column, since month column is there
print(newdf)
Output:
contrib totalShrs month
0 22610 40000 04
1 56308 80000 05
2 86606 110000 06
3 95606 120000 08

pandas how to use groupby to group columns by date in the label?

I have a dataframe 10730 rows × 249 columns, i have columns:
Index(['RegionID', 'Metro', 'CountyName', 'SizeRank', '1996-04', '1996-05',
'1996-06', '1996-07', '1996-08', '1996-09',
...
'2015-11', '2015-12', '2016-01', '2016-02', '2016-03', '2016-04',
'2016-05', '2016-06', '2016-07', '2016-08'],
dtype='object', length=249)
so what i need to do is group the columns by the quarter, jan to march Q1, and so on till Q4(using mean for the values). i know how to group 3 columns for example, but how do i group all the columns since i cannot specify the name of the column one by one.
This is the dataframe head in csv to use for testing:
'State,RegionName,RegionID,Metro,CountyName,SizeRank,1996-04,1996-05,1996-06,1996-07,1996-08,1996-09,1996-10,1996-11,1996-12,1997-01,1997-02,1997-03,1997-04,1997-05,1997-06,1997-07,1997-08,1997-09,1997-10,1997-11,1997-12,1998-01,1998-02,1998-03,1998-04,1998-05,1998-06,1998-07,1998-08,1998-09,1998-10,1998-11,1998-12,1999-01,1999-02,1999-03,1999-04,1999-05,1999-06,1999-07,1999-08,1999-09,1999-10,1999-11,1999-12,2000-01,2000-02,2000-03,2000-04,2000-05,2000-06,2000-07,2000-08,2000-09,2000-10,2000-11,2000-12,2001-01,2001-02,2001-03,2001-04,2001-05,2001-06,2001-07,2001-08,2001-09,2001-10,2001-11,2001-12,2002-01,2002-02,2002-03,2002-04,2002-05,2002-06,2002-07,2002-08,2002-09,2002-10,2002-11,2002-12,2003-01,2003-02,2003-03,2003-04,2003-05,2003-06,2003-07,2003-08,2003-09,2003-10,2003-11,2003-12,2004-01,2004-02,2004-03,2004-04,2004-05,2004-06,2004-07,2004-08,2004-09,2004-10,2004-11,2004-12,2005-01,2005-02,2005-03,2005-04,2005-05,2005-06,2005-07,2005-08,2005-09,2005-10,2005-11,2005-12,2006-01,2006-02,2006-03,2006-04,2006-05,2006-06,2006-07,2006-08,2006-09,2006-10,2006-11,2006-12,2007-01,2007-02,2007-03,2007-04,2007-05,2007-06,2007-07,2007-08,2007-09,2007-10,2007-11,2007-12,2008-01,2008-02,2008-03,2008-04,2008-05,2008-06,2008-07,2008-08,2008-09,2008-10,2008-11,2008-12,2009-01,2009-02,2009-03,2009-04,2009-05,2009-06,2009-07,2009-08,2009-09,2009-10,2009-11,2009-12,2010-01,2010-02,2010-03,2010-04,2010-05,2010-06,2010-07,2010-08,2010-09,2010-10,2010-11,2010-12,2011-01,2011-02,2011-03,2011-04,2011-05,2011-06,2011-07,2011-08,2011-09,2011-10,2011-11,2011-12,2012-01,2012-02,2012-03,2012-04,2012-05,2012-06,2012-07,2012-08,2012-09,2012-10,2012-11,2012-12,2013-01,2013-02,2013-03,2013-04,2013-05,2013-06,2013-07,2013-08,2013-09,2013-10,2013-11,2013-12,2014-01,2014-02,2014-03,2014-04,2014-05,2014-06,2014-07,2014-08,2014-09,2014-10,2014-11,2014-12,2015-01,2015-02,2015-03,2015-04,2015-05,2015-06,2015-07,2015-08,2015-09,2015-10,2015-11,2015-12,2016-01,2016-02,2016-03,2016-04,2016-05,2016-06,2016-07,2016-08\nNY,New York,6181,New York,Queens,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,432600.0,438700.0,440500.0,433900.0,422000.0,415700.0,421200.0,431100.0,435100.0,431900.0,428400.0,430700.0,438800.0,446800.0,455400.0,465500.0,472600.0,478200.0,487600.0,498600.0,508800.0,515300.0,517000.0,517800.0,520800.0,521500.0,523000.0,526300.0,524800.0,519100.0,516200.0,516400.0,516300.0,515500.0,512200.0,509200.0,509800.0,511600.0,512700.0,514000.0,513400.0,510700.0,508100.0,506700.0,505200.0,503700.0,502900.0,502400.0,500500.0,496400.0,491900.0,487500.0,484400.0,481700.0,477900.0,473600.0,469700.0,466100.0,461700.0,457700.0,455300.0,454800.0,456000.0,457800.0,461300.0,466100.0,470200.0,472800.0,475300.0,477100.0,478400.0,479100.0,478900.0,477700.0,476700.0,477100.0,478000.0,478000.0,476800.0,475300.0,473800.0,472000.0,470600.0,469900.0,469500.0,468200.0,465800.0,463500.0,461800.0,460100.0,459700.0,460800.0,461700.0,462500.0,463900.0,466000.0,467500.0,468200.0,468700.0,469400.0,469400,469100.0,468700,469300,470300,472100,474300,477600,481400,485100,488800,492600,495900,499500,503500,506400,509900,515700,520800,522200,522400,523800,526200,528400,529600,530800,532200,533800,536200,540600,545600,551400,557200,563000,568700,573600,576200,578400,582200,588000,592200,592500,590200,588000,586400\nCA,Los Angeles,12447,Los Angeles-Long Beach-Anaheim,Los Angeles,2,155000.0,154600.0,154400.0,154200.0,154100.0,154300.0,154300.0,154200.0,154800.0,155900.0,157000.0,157700.0,158200.0,158600.0,158800.0,158900.0,159100.0,159800.0,160700.0,161900.0,163400.0,165400.0,167000.0,168500.0,169900.0,171400.0,172900.0,174300.0,175800.0,177800.0,180100.0,182600.0,184400.0,185600.0,186900.0,188200.0,189600.0,191300.0,193100.0,194700.0,196300.0,197700.0,199100.0,200700.0,202300.0,204400.0,207000.0,209800.0,212300.0,214500.0,216600.0,219000.0,221100.0,222800.0,224300.0,226100.0,228100.0,230600.0,233000.0,235400.0,237300.0,239100.0,240900.0,242900.0,245000.0,247300.0,250100.0,253100.0,255900.0,258800.0,261900.0,265200.0,268600.0,272600.0,276900.0,281800.0,287000.0,292200.0,297000.0,302100.0,307600.0,313400.0,319000.0,324300.0,329600.0,334600.0,339300.0,344500.0,350600.0,356800.0,363400.0,370700.0,378400.0,386500.0,394900.0,404300.0,414600.0,425500.0,436600.0,447400.0,456700.0,464400.0,471200.0,477400.0,483500.0,489100.0,494700.0,501400.0,509700.0,518300.0,527200.0,536100.0,545400.0,555200.0,564500.0,571900.0,576800.0,579700.0,581800.0,583800.0,585300.0,587300.0,589900.0,592200.0,593300.0,593400.0,593100.0,592900.0,591600.0,590900.0,591800.0,592600.0,592100.0,590200.0,586200.0,581600.0,577500.0,572800.0,567600.0,562100.0,554400.0,545000.0,535500.0,525400.0,513600.0,502000.0,491200.0,480200.0,469000.0,459300.0,451200.0,443900.0,436800.0,430900.0,426100.0,421800.0,417800.0,413700.0,410200.0,407900.0,406300.0,404900.0,404200.0,402900.0,405900.0,412000.0,415000.0,413100.0,412100.0,411300.0,410100.0,408400.0,406800.0,405100.0,403300.0,401900.0,401000.0,399200.0,397100.0,395000.0,392700.0,390200.0,387400.0,384700.0,382100.0,379500.0,377200.0,375700.0,373800.0,371500.0,370000.0,370300.0,372100.0,375300.0,378600.0,382100.0,385600.0,389000.0,391800.0,396400.0,401500,405700.0,410700,418200,425500,432700,440400,448100,455200,461900,467800,472300,475700,479400,484000,489400,494200,498100,501800,505600,509000,512600,516000,518900,521700,525100,528900,532400,535300,538200,541000,544000,547200,550600,554200,558200,560800,562800,565600,569700,574000,577800,580600,583000,585100\nIL,Chicago,17426,Chicago,Cook,3,109700.0,109400.0,109300.0,109300.0,109100.0,109000.0,109000.0,109600.0,110200.0,110800.0,111300.0,111700.0,112200.0,112300.0,112100.0,112200.0,113000.0,113700.0,114200.0,114800.0,115500.0,116200.0,117100.0,117600.0,117800.0,118300.0,119200.0,120000.0,120600.0,121500.0,122300.0,122700.0,122900.0,123300.0,123700.0,124500.0,125700.0,127300.0,128800.0,130200.0,131400.0,132600.0,133700.0,134600.0,135500.0,136800.0,138300.0,140100.0,141900.0,143700.0,145300.0,146700.0,147900.0,149000.0,150400.0,152000.0,154000.0,155600.0,157000.0,158200.0,159900.0,161800.0,163700.0,165300.0,166400.0,167500.0,168800.0,170400.0,172100.0,173900.0,175600.0,177000.0,177800.0,177600.0,177300.0,177700.0,178800.0,180400.0,182300.0,183800.0,185000.0,185600.0,186800.0,188900.0,191300.0,194100.0,197500.0,200200.0,202300.0,203700.0,204000.0,204000.0,204400.0,205300.0,206300.0,207000.0,207600.0,208600.0,209600.0,210900.0,212800.0,214600.0,216400.0,218300.0,220300.0,222300.0,224000.0,225400.0,226900.0,228600.0,230100.0,231800.0,233200.0,234500.0,236000.0,237500.0,239000.0,240800.0,242500.0,243900.0,244900.0,245300.0,245400.0,245800.0,245800.0,245500.0,245900.0,246900.0,247300.0,247400.0,247300.0,247000.0,246700.0,246400.0,246100.0,246100.0,246300.0,246400.0,246700.0,247100.0,246700.0,245300.0,243900.0,242000.0,239800.0,237900.0,236000.0,233500.0,231800.0,230700.0,229200.0,226700.0,225200.0,224500.0,223800.0,223000.0,221900.0,219700.0,217500.0,215600.0,213800.0,212900.0,212300.0,211900.0,210800.0,209300.0,207300.0,205300.0,204200.0,204100.0,203100.0,201100.0,199000.0,196700.0,193800.0,191100.0,189200.0,188100.0,187600.0,186500.0,184400.0,181700.0,178700.0,175900.0,174100.0,172800.0,171400.0,170100.0,169100.0,167900.0,166700.0,166200.0,166400.0,166800.0,167900.0,168900.0,168400.0,167100.0,166900.0,167300.0,167500,167700.0,168300,169100,170400,172400,175100,178200,181000,183200,184600,185800,187200,189100,191100,192500,192600,192400,192900,193900,195600,197800,200100,201700,202000,201200,200500,201500,204000,206500,207600,207700,208100,209100,209000,207800,206900,206200,205800,206200,207300,208200,209100,211000,213000\nPA,Philadelphia,13271,Philadelphia,Philadelphia,4,50000.0,49900.0,49600.0,49400.0,49400.0,49300.0,49300.0,49400.0,49700.0,49600.0,49500.0,49700.0,49800.0,49700.0,49700.0,49800.0,49700.0,49700.0,49800.0,49900.0,49900.0,50000.0,50300.0,50600.0,50800.0,50800.0,50800.0,50800.0,50700.0,50500.0,50500.0,50700.0,50700.0,50800.0,50900.0,51100.0,51200.0,51400.0,51500.0,51400.0,51500.0,51800.0,52100.0,52100.0,52300.0,52700.0,53100.0,53200.0,53400.0,53700.0,53800.0,53800.0,54100.0,54500.0,54700.0,54600.0,54800.0,55100.0,55400.0,55500.0,55400.0,55500.0,55700.0,55900.0,56300.0,56600.0,57000.0,57500.0,58100.0,58600.0,59100.0,59700.0,60300.0,60700.0,61200.0,61800.0,62200.0,62500.0,63000.0,63600.0,63900.0,64200.0,64700.0,65300.0,65700.0,66100.0,66800.0,67700.0,68500.0,69200.0,69800.0,70700.0,71700.0,72800.0,73700.0,74700.0,75700.0,76700.0,77800.0,79100.0,80500.0,82100.0,84000.0,85600.0,87000.0,88200.0,89600.0,91300.0,93000.0,94900.0,96700.0,98400.0,100200.0,101900.0,103400.0,104900.0,106400.0,107500.0,108200.0,109300.0,110800.0,112500.0,113800.0,114800.0,115600.0,116000.0,116400.0,116700.0,116800.0,116900.0,117300.0,117800.0,118200.0,118600.0,119300.0,120200.0,120900.0,121400.0,121300.0,120900.0,120200.0,119600.0,119600.0,119500.0,118800.0,118100.0,117500.0,117100.0,117000.0,116700.0,116300.0,115800.0,115500.0,115900.0,116300.0,116400.0,116400.0,116100.0,116000.0,116200.0,116700.0,117300.0,118000.0,118200.0,119500.0,120900.0,121300.0,121300.0,122100.0,123000.0,123300.0,122300.0,120000.0,118200.0,117600.0,117900.0,117800.0,117400.0,117000.0,116900.0,116700.0,116500.0,115700.0,115300.0,115500.0,115600.0,115200.0,114800.0,114100.0,113500.0,112900.0,111800.0,110800.0,110400.0,110400.0,110200.0,109900.0,109700.0,110000.0,110700.0,111800,112100.0,111900,112000,112200,111800,111200,111000,110900,111100,111800,112700,112900,113100,113900,114200,113600,113500,114100,114900,115500,115500,115400,115600,116000,116100,116100,116400,117000,117900,119000,120100,121300,122300,122700,122300,121600,121800,123300,125200,126400,127000,127400,128300,129100\nAZ,Phoenix,40326,Phoenix,Maricopa,5,87200.0,87700.0,88200.0,88400.0,88500.0,88900.0,89400.0,89700.0,90100.0,90700.0,91400.0,91700.0,91800.0,92000.0,92300.0,92600.0,93000.0,93400.0,94000.0,94600.0,95300.0,96100.0,96800.0,97300.0,97700.0,98400.0,99200.0,100100.0,100500.0,100700.0,100900.0,101700.0,102600.0,103400.0,103900.0,104400.0,105100.0,105900.0,106200.0,106600.0,107400.0,108300.0,109000.0,109700.0,110400.0,111000.0,111700.0,112800.0,113700.0,114300.0,115100.0,115600.0,115900.0,116500.0,117200.0,117400.0,117600.0,118400.0,119700.0,120700.0,121200.0,121500.0,122000.0,122400.0,122700.0,123000.0,123600.0,124300.0,125000.0,125800.0,126600.0,127200.0,127900.0,128400.0,128800.0,129500.0,130500.0,131600.0,132500.0,133200.0,134000.0,134900.0,135700.0,136500.0,137200.0,138000.0,138600.0,138900.0,139200.0,139400.0,139600.0,140300.0,141400.0,142500.0,143700.0,144900.0,145900.0,147100.0,148400.0,150300.0,153100.0,156200.0,159400.0,162900.0,166500.0,170000.0,173900.0,178800.0,185000.0,192300.0,200700.0,209400.0,217000.0,223600.0,229800.0,234900.0,238600.0,241300.0,243000.0,244100.0,244800.0,245400.0,245600.0,245600.0,245300.0,244600.0,243800.0,243400.0,243400.0,243600.0,243200.0,242200.0,241300.0,240200.0,238400.0,236400.0,234700.0,233300.0,231600.0,229100.0,226100.0,222800.0,218800.0,214300.0,209500.0,205200.0,201100.0,197300.0,193700.0,190300.0,186700.0,182800.0,180500.0,179600.0,178000.0,175100.0,172100.0,168400.0,164200.0,160000.0,156000.0,151800.0,147600.0,143900.0,138900.0,133400.0,130200.0,129200.0,127700.0,126200.0,124800.0,123100.0,120700.0,118500.0,117000.0,115800.0,114800.0,114100.0,113200.0,111800.0,110100.0,108000.0,105900.0,104100.0,102900.0,102300.0,102400.0,103000.0,104100.0,105800.0,107600.0,109100.0,111200.0,114000.0,117200.0,120400.0,123300.0,125800.0,128300.0,130500.0,132500,134400.0,136200,138400,141600,144700,147400,150500,153600,156100,158100,160000,161600,162700,163300,163700,164100,164200,164500,164700,165200,166200,167200,168400,169900,171000,171500,172100,172900,174100,175500,177100,179100,181000,182400,183800,185300,186600,188000,189100,190200,191300,192800,194500,195900\n'
I changed the column index to date by dropping the non dates from the df quarter = df.drop(['RegionID','Metro','CountyName','SizeRank'],axis=1)
then change the columns to date quarter.columns = pd.to_datetime(quarter.columns) then i would like to do something likequarter = quarter.groupby(pd.TimeGrouper(freq='3M'),axis=1) but it's not working, then i would merge it back to the non-date columns. Also with this approach i wouldnt know how to put the right label for it like [2015Q4,2016Q1,2016Q2,2016Q3,2016Q4]

Here is a vectorized solution which uses pd.PeriodIndex and groupby(..., axis=1):
Data:
In [69]: x
Out[69]:
2016-01 2016-02 2016-03 2016-04 2016-05 2016-06
0 1 0 1 0 0 0
1 2 0 1 0 0 0
2 1 1 2 0 1 0
Solution:
In [70]: x.groupby(pd.PeriodIndex(x.columns, freq='Q'), axis=1).mean()
Out[70]:
2016Q1 2016Q2
0 0.666667 0.000000
1 1.000000 0.000000
2 1.333333 0.333333
Explanation:
In [71]: pd.PeriodIndex(x.columns, freq='Q')
Out[71]: PeriodIndex(['2016Q1', '2016Q1', '2016Q1', '2016Q2', '2016Q2', '2016Q2'], dtype='period[Q-DEC]', freq='Q-DEC')

It's not pretty, but this is the first thing I thought of. It sounds like the date columns can be manipulated separately. Break those out into a separate dataframe of the form below. If the other fields are kept, the conversion to datetime will throw an error.
import numpy as np
import pandas as pd
csv_df = pd.DataFrame({'2016-01':[1,2,1], '2016-02':[0,0,1], '2016-03':[1,1,2], '2016-04':[0,0,0], '2016-05':[0,0,1], '2016-06':[0,0,0]})
# convert columns into datetime format
csv_df.rename(columns=lambda x: pd.to_datetime(x, format='%Y-%m'), inplace=True)
# now strip out the year and the quarter
csv_df.rename(columns=lambda x: str(x.year) + 'Q' + str(x.quarter), inplace=True)
# #lucarlig improved my suggestion by using groupby as follows
csv_df = csv_df.groupby(csv_df.columns, axis=1).mean()

Consider melting the dataframe from the wide format to long format, parse out the quarter and year using datetime and run a pivot_table() to transform back from long to wide aggregating the values with mean:
import pandas as pd
import datetime as dt
import numpy as np
...
# MELT DATAFRAME
meltdf = pd.melt(df, id_vars = ['State','RegionName','RegionID',
'Metro','CountyName','SizeRank'],
var_name = 'Date', value_name = 'Data')
# EXTRACT QUARTER
meltdf['Date'] = pd.to_datetime(meltdf['Date'] + '-01')
meltdf['YearQuarter'] = meltdf['Date'].dt.year.astype(str) + 'Q' + \
meltdf['Date'].dt.quarter.astype(str)
# PIVOT DATAFRAME
pivotdf = pd.pivot_table(meltdf, index=['State','RegionName','RegionID',
'Metro','CountyName','SizeRank'],
columns=['YearQuarter'], values='Data', aggfunc=np.mean)
Output
print(pivotdf.head())
# State RegionName RegionID Metro CountyName SizeRank 1996Q2 1996Q3 1996Q4 1997Q1 1997Q2 1997Q3 ...
# AZ Phoenix 40326 Phoenix Maricopa 5 87700 88600 89733.33333 91266.66667 92033.33333 93000
# CA Los Angeles 12447 Los Angeles...Los Angeles 2 154666.6667 154200 154433.3333 156866.6667 158533.3333 159266.6667
# IL Chicago 17426 Chicago Cook 3 109466.6667 109133.3333 109600 111266.6667 112200 112966.6667
# NY New York 6181 New York Queens 1
# PA Philadelphia 13271 Philadelphia Philadelphia 4 49833.33333 49366.66667 49466.66667 49600 49733.33333 49733.33333

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