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Add selected interactions as columns to pandas dataframe

I'm fairly new to pandas and python. I'm trying to return few selected interaction terms of all possible interactions in a data frame, and then return them as new features in the df.
My solution was to calculate the interactions of interest using sklearn's PolynomialFeature() and attach them to the df in a for loop. See example:
import numpy as np
import pandas as pd
from sklearn.preprocessing import PolynomialFeatures
np.random.seed(1111)
a1 = np.random.randint(2, size = (5,3))
a2 = np.round(np.random.random((5,3)),2)
df = pd.DataFrame(np.concatenate([a1, a2], axis = 1), columns = ['a','b','c','d','e','f'])
combinations = [['a', 'e'], ['a', 'f'], ['b', 'f']]
for comb in combinations:
polynomizer = PolynomialFeatures(interaction_only=True, include_bias=False).fit(df[comb])
newcol_nam = polynomizer.get_feature_names(comb)[2]
newcol_val = polynomizer.transform(df[comb])[:,2]
df[newcol_nam] = newcol_val
df
a b c d e f a e a f b f
0 0.0 1.0 1.0 0.51 0.45 0.10 0.00 0.00 0.10
1 1.0 0.0 0.0 0.67 0.36 0.23 0.36 0.23 0.00
2 0.0 0.0 0.0 0.97 0.79 0.02 0.00 0.00 0.00
3 0.0 1.0 0.0 0.44 0.37 0.52 0.00 0.00 0.52
4 0.0 0.0 0.0 0.16 0.02 0.94 0.00 0.00 0.00
Another solution would be to run
PolynomialFeatures(2, interaction_only=True, include_bias=False).fit_transform(df)
and then drop the interactions I'm not interested in.
However, neither option is ideal in terms of performance and I'm wondering if there is a better solution.

As commented, you can try:
df = df.join(pd.DataFrame({
f'{x} {y}': df[x]*df[y] for x,y in combinations
}))
Or simply:
for comb in combinations:
df[' '.join(comb)] = df[comb].prod(1)
Output:
a b c d e f a e a f b f
0 0.0 1.0 1.0 0.51 0.45 0.10 0.00 0.00 0.10
1 1.0 0.0 0.0 0.67 0.36 0.23 0.36 0.23 0.00
2 0.0 0.0 0.0 0.97 0.79 0.02 0.00 0.00 0.00
3 0.0 1.0 0.0 0.44 0.37 0.52 0.00 0.00 0.52
4 0.0 0.0 0.0 0.16 0.02 0.94 0.00 0.00 0.00

Get proportionate values of columns in a dataframe - Pandas

I have a dataframe like this,
ds 0 1 2 4 5 6
0 1991Q3 nan nan nan nan 1.0 nan
1 2014Q2 1.0 3.0 nan nan 1.0 nan
2 2014Q3 1.0 nan nan 1.0 4.0 nan
3 2014Q4 nan nan nan 2.0 3.0 nan
4 2015Q1 nan 1.0 2.0 4.0 4.0 nan
I would like the proportions for each column 0-6 like this,
ds 0 1 2 4 5 6
0 1991Q3 0.00 0.00 0.00 0.00 1.00 0.00
1 2014Q2 0.20 0.60 0.00 0.00 0.20 0.00
2 2014Q3 0.16 0.00 0.00 0.16 0.67 0.00
3 2014Q4 0.00 0.00 0.00 0.40 0.60 0.00
4 2015Q1 0.00 0.09 0.18 0.36 0.36 0.00
Is there a pandas way to this? Any suggestion would be great.

You can do this:
df = df.replace(np.nan, 0)
df = df.set_index('ds')
In [3194]: df.div(df.sum(1),0).reset_index()
Out[3194]:
ds 0 1 2 4 5 6
0 1991Q3 0.00 0.00 0.00 0.00 1.00 0.00
1 2014Q2 0.20 0.60 0.00 0.00 0.20 0.00
2 2014Q3 0.17 0.00 0.00 0.17 0.67 0.00
3 2014Q4 0.00 0.00 0.00 0.40 0.60 0.00
4 2015Q1 0.00 0.09 0.18 0.36 0.36 0.00
OR you can use df.apply:
In [3196]: df = df.replace(np.nan, 0)
In [3197]: df.iloc[:,1:] = df.iloc[:,1:].apply(lambda x: x/x.sum(), axis=1)
In [3198]: df
Out[3197]:
ds 0 1 2 4 5 6
0 1991Q3 0.00 0.00 0.00 0.00 1.00 0.00
1 2014Q2 0.20 0.60 0.00 0.00 0.20 0.00
2 2014Q3 0.17 0.00 0.00 0.17 0.67 0.00
3 2014Q4 0.00 0.00 0.00 0.40 0.60 0.00
4 2015Q1 0.00 0.09 0.18 0.36 0.36 0.00

Set the first column as the index, get the sum of each row, and divide the main dataframe by the sums, and filling the null entries with 0
res = df.set_index("ds")
res.fillna(0).div(res.sum(1),axis=0)

How to concat two pivot tables without losing column name

I am trying to concat two pivot tables but after join the two tables, the columns lost.
Pivot1:
SATISFIED_CHECKOUT 1.0 2.0 3.0 4.0 5.0
SEGMENT
BOTH_TX_SPEND_GROWN 0.01 0.03 0.04 0.14 0.80
BOTH_TX_SPEND_NO_GROWTH 0.01 0.03 0.04 0.14 0.78
ONLY_SHOPPED_2018 NaN 0.03 0.04 0.15 0.78
ONLY_SHOPPED_2019 0.01 0.02 0.05 0.13 0.78
ONLY_SPEND_GROWN 0.01 0.02 0.03 0.12 0.82
ONLY_TX_GROWN 0.01 0.03 0.03 0.14 0.79
SHOPPED_NEITHER NaN 0.04 0.02 0.15 0.79
Pivot2:
SATISFIED_FOOD 1.0 2.0 3.0 4.0 5.0
SEGMENT
BOTH_TX_SPEND_GROWN 0.00 0.01 0.07 0.20 0.71
BOTH_TX_SPEND_NO_GROWTH 0.00 0.01 0.08 0.19 0.71
ONLY_SHOPPED_2018 0.01 0.01 0.07 0.19 0.71
ONLY_SHOPPED_2019 0.00 0.01 0.10 0.19 0.69
ONLY_SPEND_GROWN 0.00 0.01 0.08 0.18 0.72
ONLY_TX_GROWN 0.00 0.02 0.07 0.19 0.72
SHOPPED_NEITHER NaN NaN 0.10 0.20 0.70
The original df looks like below:
SATISFIED_CHECKOUT SATISFIED_FOOD Persona
1 1 BOTH_TX_SPEND_GROWN
2 3 BOTH_TX_SPEND_NO_GROWTH
3 2 ONLY_SHOPPED_2019
.... .... ............
5 3 ONLY_SHOPPED_2019
I am using the code:
a = pd.pivot_table(df,index=["SEGMENT"], columns=["SATISFIED_FOOD"], aggfunc='size').apply(lambda x: x / x.sum(), axis=1).round(2)
b = pd.pivot_table(df,index=["SEGMENT"], columns=["SATISFIED_CHECKOUT"], aggfunc='size').apply(lambda x: x / x.sum(), axis=1).round(2)
pd.concat([a, b],axis=1)
The result like below:
1.0 2.0 3.0 4.0 ... 2.0 3.0 4.0 5.0
SEGMENT ...
BOTH_TX_SPEND_GROWN 0.01 0.03 0.07 0.23 ... 0.03 0.04 0.14 0.80
BOTH_TX_SPEND_NO_GROWTH 0.01 0.03 0.06 0.22 ... 0.03 0.04 0.14 0.78
ONLY_SHOPPED_2018 0.01 0.04 0.08 0.24 ... 0.03 0.04 0.15 0.78
ONLY_SHOPPED_2019 0.01 0.03 0.08 0.25 ... 0.02 0.05 0.13 0.78
ONLY_SPEND_GROWN 0.00 0.03 0.07 0.22 ... 0.02 0.03 0.12 0.82
ONLY_TX_GROWN 0.01 0.02 0.05 0.22 ... 0.03 0.03 0.14 0.79
SHOPPED_NEITHER NaN 0.01 0.07 0.28 ... 0.04 0.02 0.15 0.79
[7 rows x 15 columns]
But what I want to see this the result like below:
SATISFIED_CHECKOUT SATISFIED_FOOD
1.0 2.0 3.0 4.0 ... 2.0 3.0 4.0 5.0
SEGMENT ...
BOTH_TX_SPEND_GROWN 0.01 0.03 0.07 0.23 ... 0.03 0.04 0.14 0.80
BOTH_TX_SPEND_NO_GROWTH 0.01 0.03 0.06 0.22 ... 0.03 0.04 0.14 0.78
ONLY_SHOPPED_2018 0.01 0.04 0.08 0.24 ... 0.03 0.04 0.15 0.78
ONLY_SHOPPED_2019 0.01 0.03 0.08 0.25 ... 0.02 0.05 0.13 0.78
ONLY_SPEND_GROWN 0.00 0.03 0.07 0.22 ... 0.02 0.03 0.12 0.82
ONLY_TX_GROWN 0.01 0.02 0.05 0.22 ... 0.03 0.03 0.14 0.79
SHOPPED_NEITHER NaN 0.01 0.07 0.28 ... 0.04 0.02 0.15 0.79
[7 rows x 15 columns]

Merging two pandas data frame with common columns

I have a lower triangular matrix and then I transpose it and I have the transpose of it.
I am trying to merge them together
lower triangular:
Data :
0 1 2 3
0 1 0 0 0
1 0.21 0 0 0
2 0.31 0.32 0 0
3 0.41 0.42 0.43 0
4 0.51 0.52 0.53 0.54
transpose triangular:
Data :
0 1 2 3
0 1 0.21 0.31 0.41
1 0 0 0.32 0.52
2 0 0 0 0.53
3 0 0 0 0.54
4 0 0 0 0
Merged matrix:
Data :
0 1 2 3 4
0 1 0.21 0.31 0.41 0.51
1 0.21 0 0.32 0.42 0.52
2 0.31 0.32 0 0.43 0.53
3 0.41 0.42 0.43 0 0.54
4 0.51 0.52 0.53 0.54 0
I tried using pd.merge but I couldn't get it to work

Let us using combine_first after mask
df.mask(df==0).T.combine_first(df).fillna(0)
Out[1202]:
0 1 2 3 4
0 1.00 0.21 0.31 0.41 0.51
1 0.21 0.00 0.32 0.42 0.52
2 0.31 0.32 0.00 0.43 0.53
3 0.41 0.42 0.43 0.00 0.54
4 0.51 0.52 0.53 0.54 0.00

How about just adding the two dataframes?
df3 = df1.add(df2, fill_value=0)
BR

Row based chart plot (Seaborn or Matplotlib)

Given that my data is a pandas dataframe and looks like this:
Ref +1 +2 +3 +4 +5 +6 +7
2013-05-28 1 -0.44 0.03 0.06 -0.31 0.13 0.56 0.81
2013-07-05 2 0.84 1.03 0.96 0.90 1.09 0.59 1.15
2013-08-21 3 0.09 0.25 0.06 0.09 -0.09 -0.16 0.56
2014-10-15 4 0.35 1.16 1.91 3.44 2.75 1.97 2.16
2015-02-09 5 0.09 -0.10 -0.38 -0.69 -0.25 -0.85 -0.47
How can I plot a chart of the 5 lines (1 for each ref), where the X axis are the columns (+1, +2...), and starts from 0? If is in seaborn, even better. But matplotlib solutions are also welcome.

Plotting a dataframe in pandas is generally all about reshaping the table so that the individual lines you want are in separate columns, and the x-values are in the index. Some of these reshape operations are a bit ugly, but you can do:
df = pd.read_clipboard()
plot_table = pd.melt(df.reset_index(), id_vars=['index', 'Ref'])
plot_table = plot_table.pivot(index='variable', columns='Ref', values='value')
# Add extra row to have all lines start from 0:
plot_table.loc['+0', :] = 0
plot_table = plot_table.sort_index()
plot_table
Ref 1 2 3 4 5
variable
+0 0.00 0.00 0.00 0.00 0.00
+1 -0.44 0.84 0.09 0.35 0.09
+2 0.03 1.03 0.25 1.16 -0.10
+3 0.06 0.96 0.06 1.91 -0.38
+4 -0.31 0.90 0.09 3.44 -0.69
+5 0.13 1.09 -0.09 2.75 -0.25
+6 0.56 0.59 -0.16 1.97 -0.85
+7 0.81 1.15 0.56 2.16 -0.47
Now that you have a table with the right shape, plotting is pretty automatic:
plot_table.plot()