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Python: Merge on 2 columns

I'm working with a large dataset. The following is an example, calculated with a smaller dataset.
In this example i got the measurements of the pollution of 3 rivers for different timespans. Each year, the amount pollution of a river is measured at a measuring station downstream ("pollution"). It has already been calculated, in which year the river water was polluted upstream ("year_of_upstream_pollution"). My goal ist to create a new column ["result_of_upstream_pollution"], which contains the amount of pollution connected to the "year_of_upstream_pollution". For this, the data from the "pollution"-column has to be reassigned.
ids = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3]
year = [2000,2001,2002,2003,2004,2005,1990,1991,1992,1993,1994,1995,2000,2001,2002,2003,2004,2005]
y1 = [2002,2002,2003,2005,2005,np.NaN,1991,1992,1993,1994,np.NaN,np.NaN,2012,2012,2013,2014,2015,np.NaN]
poll = [10,14,20,11,8,11,
20,22,20,25,18,21,
30,19,15,10,26,28]
dictr1 ={"river_id":ids,"year":year,"pollution": poll,"year_of_upstream_pollution":y1}
dfr1 = pd.DataFrame(dictr1)
print(dfr1)
river_id year pollution year_of_upstream_pollution
0 1 2000 10 2002.0
1 1 2001 14 2002.0
2 1 2002 20 2003.0
3 1 2003 11 2005.0
4 1 2004 8 2005.0
5 1 2005 11 NaN
6 2 1990 20 1991.0
7 2 1991 22 1992.0
8 2 1992 20 1993.0
9 2 1993 25 1994.0
10 2 1994 18 NaN
11 2 1995 21 NaN
12 3 2000 30 2002.0
13 3 2001 19 2002.0
14 3 2002 15 2003.0
15 3 2003 10 2004.0
16 3 2004 26 2005.0
17 3 2005 28 NaN
Example: river_id = 1, year = 2000, year_of_upstream_pollution = 2002
value of the pollution-column in year 2002 = 20
Therefore: result_of_upstream_pollution = 20
The resulting column should look like this:
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 NaN
6 22.0
7 20.0
8 25.0
9 18.0
10 NaN
11 NaN
12 15.0
13 15.0
14 10.0
15 26.0
16 28.0
17 NaN
My own approach:
### My approach
# Split dfr1 in two
dfr3 = pd.DataFrame(dfr1, columns = ["river_id","year","pollution"])
dfr4 = pd.DataFrame(dfr1, columns = ["river_id","year_of_upstream_pollution"])
# Merge the two dataframes on the "year" and "year_of_upstream_pollution"-column
arrayr= dfr4.merge(dfr3, left_on = "year_of_upstream_pollution", right_on = "year", how = "left").pollution.values
listr = arrayr.tolist()
dfr1["result_of_upstream_pollution"] = listr
print(dfr1)
len(listr) # = 28
This results in the following ValueError:
"Length of values does not match length of index"
My explanation for this is, that the values in the "year"-column of "dfr3" are not unique, which leads to several numbers being assigned to each year and explains why: len(listr) = 28
I haven't been able to find a way around this error yet. Please keep in mind that the real dataset is much larger than this one. Any help would be much appreciated!

As you said in the title, this is merge on two column:
dfr1['result_of_upstream_pollution'] = dfr1.merge(dfr1, left_on=['river_id','year'],
right_on=['river_id','year_of_upstream_pollution'],
how='right')['pollution_x']
print(df)
Output:
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 NaN
6 22.0
7 20.0
8 25.0
9 18.0
10 NaN
11 NaN
12 15.0
13 15.0
14 10.0
15 26.0
16 28.0
17 NaN

I just realized that this solution doesn't seem to be working for me.
When i execute the code, this is what happens:
dfr1['result_of_upstream_pollution'] = dfr1.merge(dfr1, left_on=['river_id','year'],
right_on=['river_id','year_of_upstream_pollution'],
how='right')['pollution_x']
print(dfr1)
river_id year pollution year_of_upstream_pollution \
0 1 2000 10 2002.0
1 1 2001 14 2002.0
2 1 2002 20 2003.0
3 1 2003 11 2005.0
4 1 2004 8 2005.0
5 1 2005 11 NaN
6 2 1990 20 1991.0
7 2 1991 22 1992.0
8 2 1992 20 1993.0
9 2 1993 25 1994.0
10 2 1994 18 NaN
11 2 1995 21 NaN
12 3 2000 30 2002.0
13 3 2001 19 2002.0
14 3 2002 15 2003.0
15 3 2003 10 2004.0
16 3 2004 26 2005.0
17 3 2005 28 NaN
result_of_upstream_pollution
0 20.0
1 20.0
2 11.0
3 11.0
4 11.0
5 22.0
6 20.0
7 25.0
8 18.0
9 15.0
10 15.0
11 10.0
12 26.0
13 28.0
14 NaN
15 NaN
16 NaN
17 NaN
For some reason, this code doesn't seem to be handling the "NaN" values in the right way.
If there is an "NaN"-value (in the column: "year_of_upstream_pollution"), there shouldnt be a value in "result_of_upstream_pollution".
Equally, the ids 14,15 and 16 all have values for the "year_of_upstream_pollution" which has matching data in the "pollution-column" and therefore should also have values in the result-column.
On top of that, it seems that all values after the first "NaN" (at id = 5) are assigned the wrong values.
#Quang Hoang Thank you very much for trying to solve my problem! Could you maybe explain why my results differ from yours?
Does anyone know how i can get this code to work?

Replace last value(s) of group with NaN

My goal is to replace the last value (or the last several values) of each id with NaN. My real dataset is quite large and has groups of different sizes.
Example:
import pandas as pd
ids = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3]
year = [2000,2001,2002,2003,2004,2005,1990,1991,1992,1993,1994,1995,2010,2011,2012,2013,2014,2015]
percent = [120,70,37,40,50,110,140,100,90,5,52,80,60,40,70,60,50,110]
dictex ={"id":ids,"year":year,"percent [%]": percent}
dfex = pd.DataFrame(dictex)
print(dfex)
id year percent [%]
0 1 2000 120
1 1 2001 70
2 1 2002 37
3 1 2003 40
4 1 2004 50
5 1 2005 110
6 2 1990 140
7 2 1991 100
8 2 1992 90
9 2 1993 5
10 2 1994 52
11 2 1995 80
12 3 2010 60
13 3 2011 40
14 3 2012 70
15 3 2013 60
16 3 2014 50
17 3 2015 110
My goal is to replace the last 1 / or 2 / or 3 values of the "percent [%]" column for each id (group) with NaN.
The result should look like this: (here: replace the last 2 values of each id)
id year percent [%]
0 1 2000 120
1 1 2001 70
2 1 2002 37
3 1 2003 40
4 1 2004 NaN
5 1 2005 NaN
6 2 1990 140
7 2 1991 100
8 2 1992 90
9 2 1993 5
10 2 1994 NaN
11 2 1995 NaN
12 3 2010 60
13 3 2011 40
14 3 2012 70
15 3 2013 60
16 3 2014 NaN
17 3 2015 NaN
I know there should be a relatively easy solution for this, but i'm new to python and simply haven't been able to figure out an elegant way.
Thanks for the help!

try using groupby, tail and index to find the index of those rows that will be modified and use loc to change the values
nrows = 2
idx = df.groupby('id').tail(nrows).index
df.loc[idx, 'percent [%]'] = np.nan
#output
id year percent [%]
0 1 2000 120.0
1 1 2001 70.0
2 1 2002 37.0
3 1 2003 40.0
4 1 2004 NaN
5 1 2005 NaN
6 2 1990 140.0
7 2 1991 100.0
8 2 1992 90.0
9 2 1993 5.0
10 2 1994 NaN
11 2 1995 NaN
12 3 2010 60.0
13 3 2011 40.0
14 3 2012 70.0
15 3 2013 60.0
16 3 2014 NaN
17 3 2015 NaN

Pandas removing duplicate range of data

Hi all I have the following dataframe:
df1
WL WM WH WP
1 low medium high premium
2 26 26 15 14
3 32 32 18 29
4 41 41 19 42
5 apple dog fur napkins
6 orange cat tesla earphone
7 NaN rat tobias controller
8 NaN NaN phone
9 low medium high
10 1 3 5
11 2 4 6
12 low medium high
13 4 8 10
14 5 9 11
Is there a way to remove low + 2 rows such that the output is such:
df1
WL WM WH WP
1 low medium high premium
2 26 26 15 14
3 32 32 18 29
4 41 41 19 42
5 apple dog fur napkins
6 orange cat tesla earphone
7 NaN rat tobias controller
8 NaN NaN phone
Unfortunately the code must be dynamic because I have multiple dataframes and the placement for 'low' is different in each. My initial attempt:
df1 = df1[~df1.iloc[:,0].isin(['LOW'])+2].reset_index(drop=True)
however this is not quite what I am looking for. Any help is appreciated

You can use:
#get index values where low
a = df.index[df.iloc[:,0] == 'low']
size = 2
#all index values (without first [1:])
#min is for last rows of df for avoid select non existed values
arr = [np.arange(i, min(i+size+1,len(df)+1)) for i in a[1:]]
idx = np.unique(np.concatenate(arr))
print (idx)
[ 9 10 11 12 13 14]
#remove rows
df = df.drop(idx)
print (df)
WL WM WH WP
1 low medium high premium
2 26 26 15 14
3 32 32 18 29
4 41 41 19 42
5 apple dog fur napkins
6 orange cat tesla earphone
7 NaN rat tobias controller
8 NaN NaN phone NaN

Pandas compare 2 dataframes by specific rows in all columns

I have the following Pandas dataframe of some raw numbers:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 10000)
col_raw_headers = ['07_08_19 #1','07_08_19 #2','07_08_19 #2.1','11_31_19 #1','11_31_19 #1.1','11_31_19 #1.3','12_15_20 #1','12_15_20 #2','12_15_20 #2.1','12_15_20 #2.2']
col_raw_trial_info = ['Quantity1','Quantity2','Quantity3','Quantity4','Quantity5','Quantity6','TimeStamp',np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]
cols_raw = [[1,75,9,7,-4,0.4,'07/08/2019 05:11'],[1,11,20,-17,12,0.8,'07/08/2019 10:54'],[2,0.9,17,102,56,0.6,'07/08/2019 21:04'],[1,70,4,75,0.8,0.4,'11/31/2019 11:15'],[2,60,74,41,-36,0.3,'11/31/2019 16:50'],[3,17,12,-89,30,0.1,'11/31/2019 21:33'],[1,6,34,496,-84,0.5,'12/15/2020 01:36'],[1,3,43,12,-23,0.5,'12/15/2020 07:01'],[2,5,92,17,64,0.5,'12/15/2020 11:15'],[3,7,11,62,-11,0.5,'12/15/2020 21:45']]
both_values = [[1,2,3,4,8,4,3,8,7],[6,5,3,7,3,23,27,3,11],[65,3,6,78,9,2,45,6,7],[4,3,6,8,3,5,66,32,84],[2,3,11,55,3,7,33,65,34],[22,1,6,32,5,6,4,3,898],[1,6,3,2,6,55,22,6,23],[34,37,46,918,0,37,91,12,68],[51,20,1,34,12,59,78,6,101],[12,71,34,94,1,73,46,51,21]]
processed_cols = ['c_1trial','14_1','14_2','8_1','8_2','8_3','28_1','24_1','24_2','24_3']
df_raw = pd.DataFrame(zip(*cols_raw))
df_temp = pd.DataFrame(zip(*both_values))
df_raw = pd.concat([df_raw,df_temp])
df_raw.columns=col_raw_headers
df_raw.insert(0,'Tr_id',col_raw_trial_info)
df_raw.reset_index(drop=True,inplace=True)
It looks like this:
Tr_id 07_08_19 #1 07_08_19 #2 07_08_19 #2.1 11_31_19 #1 11_31_19 #1.1 11_31_19 #1.3 12_15_20 #1 12_15_20 #2 12_15_20 #2.1 12_15_20 #2.2
0 Quantity1 1 1 2 1 2 3 1 1 2 3
1 Quantity2 75 11 0.9 70 60 17 6 3 5 7
2 Quantity3 9 20 17 4 74 12 34 43 92 11
3 Quantity4 7 -17 102 75 41 -89 496 12 17 62
4 Quantity5 -4 12 56 0.8 -36 30 -84 -23 64 -11
5 Quantity6 0.4 0.8 0.6 0.4 0.3 0.1 0.5 0.5 0.5 0.5
6 TimeStamp 07/08/2019 05:11 07/08/2019 10:54 07/08/2019 21:04 11/31/2019 11:15 11/31/2019 16:50 11/31/2019 21:33 12/15/2020 01:36 12/15/2020 07:01 12/15/2020 11:15 12/15/2020 21:45
7 NaN 1 6 65 4 2 22 1 34 51 12
8 NaN 2 5 3 3 3 1 6 37 20 71
9 NaN 3 3 6 6 11 6 3 46 1 34
10 NaN 4 7 78 8 55 32 2 918 34 94
11 NaN 8 3 9 3 3 5 6 0 12 1
12 NaN 4 23 2 5 7 6 55 37 59 73
13 NaN 3 27 45 66 33 4 22 91 78 46
14 NaN 8 3 6 32 65 3 6 12 6 51
15 NaN 7 11 7 84 34 898 23 68 101 21
I have a separate dataframe of a processed version of these numbers where:
some of the header rows from above have been deleted,
the column names have been changed
Here is the second dataframe:
df_processed = pd.DataFrame(zip(*both_values),columns=processed_cols)
df_processed = df_processed[[3,4,9,7,0,2,1,6,8,5]]
8_1 8_2 24_3 24_1 c_1trial 14_2 14_1 28_1 24_2 8_3
0 4 2 12 34 1 65 6 1 51 22
1 3 3 71 37 2 3 5 6 20 1
2 6 11 34 46 3 6 3 3 1 6
3 8 55 94 918 4 78 7 2 34 32
4 3 3 1 0 8 9 3 6 12 5
5 5 7 73 37 4 2 23 55 59 6
6 66 33 46 91 3 45 27 22 78 4
7 32 65 51 12 8 6 3 6 6 3
8 84 34 21 68 7 7 11 23 101 898
Common parts of each dataframe:
For each column, rows 8 onwards of the raw dataframe are the same as row 1 onwards from the processed dataframe. The order of columns in both dataframes is not the same.
Output combination:
I am looking to compare rows 8-16 in columns 1-10 of the raw dataframe dr_raw to the processed dataframe df_processed. If the columns match each other, then I would like to extract rows 1-7 of the df_raw and the column header from df_processed.
Example:
the values in column c_1trial only matches values in rows 8-16 from the column 07_08_19 #1. I would 2 steps: (1) I would like to find some way to determine that these 2 columns are matching each other, (2) if 2 columns do match eachother, then in the sample output, I would like to select rows from the matching columns.
Here is the output I am looking to get:
Tr_id 07_08_19 #1 07_08_19 #2 07_08_19 #2.1 11_31_19 #1 11_31_19 #1.1 11_31_19 #1.3 12_15_20 #1 12_15_20 #2 12_15_20 #2.1 12_15_20 #2.2
Quantity1 1 1 2 1 2 3 1 1 2 3
Quantity2 75 11 0.9 70 60 17 6 3 5 7
Quantity3 9 20 17 4 74 12 34 43 92 11
Proc_Name c_1trial 14_1 14_2 8_1 8_2 8_3 28_1 24_1 24_2 24_3
Quantity4 7 -17 102 75 41 -89 496 12 17 62
Quantity5 -4 12 56 0.8 -36 30 -84 -23 64 -11
Quantity6 0.4 0.8 0.6 0.4 0.3 0.1 0.5 0.5 0.5 0.5
TimeStamp 07/08/2019 05:11 07/08/2019 10:54 07/08/2019 21:04 11/31/2019 11:15 11/31/2019 16:50 11/31/2019 21:33 12/15/2020 01:36 12/15/2020 07:01 12/15/2020 11:15 12/15/2020 21:45
My attempts are giving trouble:
print (df_raw.iloc[7:,1:] == df_processed).all(axis=1)
gives
ValueError: Can only compare identically-labeled DataFrame objects
and
print (df_raw.ix[7:].values == df_processed.values) #gives False
gives
False
The problem with my second attempt is that I am not selecting .all(axis=1). When I make a comparison I want to do this across all rows of every column, not just one row.
Question:
Is there a way to select out the output I showed above from these 2 dataframes?

Does this look like the output you're looking for?
Raw dataframe df:
Tr_id 07_08_19 07_08_19.1 07_08_19.2 11_31_19 11_31_19.1
0 Quantity1 1 1 2 1 2
1 Quantity2 75 11 0.9 70 60
2 Quantity3 9 20 17 4 74
3 Quantity4 7 -17 102 75 41
4 Quantity5 -4 12 56 0.8 -36
5 Quantity6 0.4 0.8 0.6 0.4 0.3
6 TimeStamp 07/08/2019 07/08/2019 07/08/2019 11/31/2019 11/31/2019
7 NaN 1 6 65 4 2
8 NaN 2 5 3 3 3
9 NaN 3 3 6 6 11
10 NaN 4 7 78 8 55
11 NaN 8 3 9 3 3
12 NaN 4 23 2 5 7
13 NaN 3 27 45 66 33
14 NaN 8 3 6 32 65
15 NaN 7 11 7 84 34
11_31_19.2 12_15_20 12_15_20.1 12_15_20.2 12_15_20.3
0 3 1 1 2 3
1 17 6 3 5 7
2 12 34 43 92 11
3 -89 496 12 17 62
4 30 -84 -23 64 -11
5 0.1 0.5 0.5 0.5 0.5
6 11/31/2019 12/15/2020 12/15/2020 12/15/2020 12/15/2020
7 22 1 34 51 12
8 1 6 37 20 71
9 6 3 46 1 34
10 32 2 918 34 94
11 5 6 0 12 1
12 6 55 37 59 73
13 4 22 91 78 46
14 3 6 12 6 51
15 898 23 68 101 21
Processed dataframe dfp:
8_1 8_2 24_3 24_1 c_1trial 14_2 14_1 28_1 24_2 8_3
0 4 2 12 34 1 65 6 1 51 22
1 3 3 71 37 2 3 5 6 20 1
2 6 11 34 46 3 6 3 3 1 6
3 8 55 94 918 4 78 7 2 34 32
4 3 3 1 0 8 9 3 6 12 5
5 5 7 73 37 4 2 23 55 59 6
6 66 33 46 91 3 45 27 22 78 4
7 32 65 51 12 8 6 3 6 6 3
8 84 34 21 68 7 7 11 23 101 898
Code:
df = pd.read_csv('raw_df.csv') # raw dataframe
dfp = pd.read_csv('processed_df.csv') # processed dataframe
dfr = df.drop('Tr_id', axis=1)
x = pd.DataFrame()
for col_raw in dfr.columns:
for col_p in dfp.columns:
if (dfr.tail(9).astype(int)[col_raw] == dfp[col_p]).all():
series = dfr[col_raw].head(7).tolist()
series.append(col_raw)
x[col_p] = series
x = pd.concat([df['Tr_id'].head(7), x], axis=1)
Output:
Tr_id c_1trial 14_1 14_2 8_1 8_2
0 Quantity1 1 1 2 1 2
1 Quantity2 75 11 0.9 70 60
2 Quantity3 9 20 17 4 74
3 Quantity4 7 -17 102 75 41
4 Quantity5 -4 12 56 0.8 -36
5 Quantity6 0.4 0.8 0.6 0.4 0.3
6 TimeStamp 07/08/2019 07/08/2019 07/08/2019 11/31/2019 11/31/2019
7 NaN 07_08_19 07_08_19.1 07_08_19.2 11_31_19 11_31_19.1
8_3 28_1 24_1 24_2 24_3
0 3 1 1 2 3
1 17 6 3 5 7
2 12 34 43 92 11
3 -89 496 12 17 62
4 30 -84 -23 64 -11
5 0.1 0.5 0.5 0.5 0.5
6 11/31/2019 12/15/2020 12/15/2020 12/15/2020 12/15/2020
7 11_31_19.2 12_15_20 12_15_20.1 12_15_20.2 12_15_20.3
I think the code could be more concise but maybe this does the job.

alternative solution, using DataFrame.isin() method:
In [171]: df1
Out[171]:
a b c
0 1 1 3
1 0 2 4
2 4 2 2
3 0 3 3
4 0 4 4
In [172]: df2
Out[172]:
a b c
0 0 3 3
1 1 1 1
2 0 3 4
3 4 2 3
4 0 4 4
In [173]: common = pd.merge(df1, df2)
In [174]: common
Out[174]:
a b c
0 0 3 3
1 0 4 4
In [175]: df1[df1.isin(common.to_dict('list')).all(axis=1)]
Out[175]:
a b c
3 0 3 3
4 0 4 4
Or if you want to subtract second data set from the first one. I.e. Pandas equivalent for SQL's:
select col1, .., colN from tableA
minus
select col1, .., colN from tableB
in Pandas:
In [176]: df1[~df1.isin(common.to_dict('list')).all(axis=1)]
Out[176]:
a b c
0 1 1 3
1 0 2 4
2 4 2 2

I came up with this using loops. It is very disappointing:
holder = []
for randm,pp in enumerate(list(df_processed)):
list1 = df_processed[pp].tolist()
for car,rr in enumerate(list(df_raw)):
list2 = df_raw.loc[7:,rr].tolist()
if list1==list2:
holder.append([rr,pp])
df_intermediate = pd.DataFrame(holder,columns=['A','B'])
df_c = df_raw.loc[:6,df_intermediate.iloc[:,0].tolist()]
df_c.loc[df_c.shape[0]] = df_intermediate.iloc[:,1].tolist()
df_c.insert(0,list(df_raw)[0],df_raw[list(df_raw)[0]])
df_c.iloc[-1,0]='Proc_Name'
df_c = df_c.reindex([0,1,2]+[7]+[3,4,5,6]).reset_index(drop=True)
Output:
Tr_id 11_31_19 #1 11_31_19 #1.1 12_15_20 #2.2 12_15_20 #2 07_08_19 #1 07_08_19 #2.1 07_08_19 #2 12_15_20 #1 12_15_20 #2.1 11_31_19 #1.3
0 Quantity1 1 2 3 1 1 2 1 1 2 3
1 Quantity2 70 60 7 3 75 0.9 11 6 5 17
2 Quantity3 4 74 11 43 9 17 20 34 92 12
3 Proc_Name 8_1 8_2 24_3 24_1 c_1trial 14_2 14_1 28_1 24_2 8_3
4 Quantity4 75 41 62 12 7 102 -17 496 17 -89
5 Quantity5 0.8 -36 -11 -23 -4 56 12 -84 64 30
6 Quantity6 0.4 0.3 0.5 0.5 0.4 0.6 0.8 0.5 0.5 0.1
7 TimeStamp 11/31/2019 11:15 11/31/2019 16:50 12/15/2020 21:45 12/15/2020 07:01 07/08/2019 05:11 07/08/2019 21:04 07/08/2019 10:54 12/15/2020 01:36 12/15/2020 11:15 11/31/2019 21:33
The order of the columns is different than what I required, but that is a minor problem.
The real problem with this approach is using loops.
I wish there was a better way to do this using some built-in Pandas functionality. If you have a better solution, please post it. thank you.

pandas drop row below each row containing an 'na'

i have a dataframe with, say, 4 columns [['a','b','c','d']], to which I add another column ['total'] containing the sum of all the other columns for each row. I then add another column ['growth of total'] with the growth rate of the total.
some of the values in [['a','b','c','d']] are blank, rendering the ['total'] column invalid for these rows. I can easily get rid of these rows with df.dropna(how='any').
However, my growth rate will be invalid not only for rows with missing values in [['a','b','c','d']], but also for the following row. How do I drop all these rows?

IIUC correctly you can use notnull with all to mask off any rows with NaN and any rows that follow NaN rows:
In [43]:
df = pd.DataFrame({'a':[0,np.NaN, 2, 3,np.NaN], 'b':[np.NaN, 1,2,3,4], 'c':[0, np.NaN,2,3,4]})
df
Out[43]:
a b c
0 0 NaN 0
1 NaN 1 NaN
2 2 2 2
3 3 3 3
4 NaN 4 4
In [44]:
df[df.notnull().all(axis=1) & df.shift().notnull().all(axis=1)]
Out[44]:
a b c
3 3 3 3

Here's one option that I think does what you're looking for:
In [76]: df = pd.DataFrame(np.arange(40).reshape(10,4))
In [77]: df.ix[1,2] = np.nan
In [78]: df.ix[6,1] = np.nan
In [79]: df['total'] = df.sum(axis=1, skipna=False)
In [80]: df
Out[80]:
0 1 2 3 total
0 0 1 2 3 6
1 4 5 NaN 7 NaN
2 8 9 10 11 38
3 12 13 14 15 54
4 16 17 18 19 70
5 20 21 22 23 86
6 24 NaN 26 27 NaN
7 28 29 30 31 118
8 32 33 34 35 134
9 36 37 38 39 150
In [81]: df['growth'] = df['total'].iloc[1:] - df['total'].values[:-1]
In [82]: df
Out[82]:
0 1 2 3 total growth
0 0 1 2 3 6 NaN
1 4 5 NaN 7 NaN NaN
2 8 9 10 11 38 NaN
3 12 13 14 15 54 16
4 16 17 18 19 70 16
5 20 21 22 23 86 16
6 24 NaN 26 27 NaN NaN
7 28 29 30 31 118 NaN
8 32 33 34 35 134 16
9 36 37 38 39 150 16