getting dummy values acorss all columns - pandas

get dummies method does not seem to work as expected while using with more than one column.
For e.g. if I have this dataframe...
shopping_list = [
["Apple", "Bread", "Fridge"],
["Rice", "Bread", "Milk"],
["Apple", "Rice", "Bread", "Milk"],
["Rice", "Milk"],
["Apple", "Bread", "Milk"],
]
df = pd.DataFrame(shopping_list)
If I use get_dummmies method, the items are repeated across columns like this:
pd.get_dummies(df)
0_Apple 0_Rice 1_Bread 1_Milk 1_Rice 2_Bread 2_Fridge 2_Milk 3_Milk
0 1 0 1 0 0 0 1 0 0
1 0 1 1 0 0 0 0 1 0
2 1 0 0 0 1 1 0 0 1
3 0 1 0 1 0 0 0 0 0
4 1 0 1 0 0 0 0 1 0
While the expected result is:
Apple Bread Fridge Milk Rice
0 1 1 1 0 0
1 0 1 0 1 1
2 1 1 0 1 1
3 0 0 0 1 1
4 1 1 0 1 0


Add parameters prefix and prefix_sep to get_dummies and then add max for avoid duplicated columns names (it aggregate by max):
df = pd.get_dummies(df, prefix='', prefix_sep='').max(axis=1, level=0)
print(df)
Apple Rice Bread Milk Fridge
0 1 0 1 0 1
1 0 1 1 1 0
2 1 1 1 1 0
3 0 1 0 1 0
4 1 0 1 1 0

Related

Using If-else to change values in Pandas

I’ve a pd df consists three columns: ID, t, and ind1.
import pandas as pd
dat = {'ID': [1,1,1,1,2,2,2,3,3,3,3,4,4,4,5,5,6,6,6],
't': [0,1,2,3,0,1,2,0,1,2,3,0,1,2,0,1,0,1,2],
'ind1' : [1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0]
}
df = pd.DataFrame(dat, columns = ['ID', 't', 'ind1'])
print (df)
What I need to do is to create a new column (res) that
for all ID with ind1==0, then res is zero.
for all ID with
ind1==1 and if t==max(t) (group by ID), then res = 1, otherwise zero.
Here’s anticipated output

Check with groupby with idxmax , then where with transform all
df['res']=df.groupby('ID').t.transform('idxmax').where(df.groupby('ID').ind1.transform('all')).eq(df.index).astype(int)
df
Out[160]:
ID t ind1 res
0 1 0 1 0
1 1 1 1 0
2 1 2 1 0
3 1 3 1 1
4 2 0 0 0
5 2 1 0 0
6 2 2 0 0
7 3 0 0 0
8 3 1 0 0
9 3 2 0 0
10 3 3 0 0
11 4 0 1 0
12 4 1 1 0
13 4 2 1 1
14 5 0 1 0
15 5 1 1 1
16 6 0 0 0
17 6 1 0 0
18 6 2 0 0

This works on the knowledge that the ID column is sorted :
cond1 = df.ind1.eq(0)
cond2 = df.ind1.eq(1) & (df.t.eq(df.groupby("ID").t.transform("max")))
df["res"] = np.select([cond1, cond2], [0, 1], 0)
df
ID t ind1 res
0 1 0 1 0
1 1 1 1 0
2 1 2 1 0
3 1 3 1 1
4 2 0 0 0
5 2 1 0 0
6 2 2 0 0
7 3 0 0 0
8 3 1 0 0
9 3 2 0 0
10 3 3 0 0
11 4 0 1 0
12 4 1 1 0
13 4 2 1 1
14 5 0 1 0
15 5 1 1 1
16 6 0 0 0
17 6 1 0 0
18 6 2 0 0

Use groupby.apply:
df['res'] = (df.groupby('ID').apply(lambda x: x['ind1'].eq(1)&x['t'].eq(x['t'].max()))
.astype(int).reset_index(drop=True))
print(df)
ID t ind1 res
0 1 0 1 0
1 1 1 1 0
2 1 2 1 0
3 1 3 1 1
4 2 0 0 0
5 2 1 0 0
6 2 2 0 0
7 3 0 0 0
8 3 1 0 0
9 3 2 0 0
10 3 3 0 0
11 4 0 1 0
12 4 1 1 0
13 4 2 1 1
14 5 0 1 0
15 5 1 1 1
16 6 0 0 0
17 6 1 0 0
18 6 2 0 0

How to create dummy variables on Ordinal columns in Python

I am new to Python. I have created dummy columns on categorical column using pandas get_dummies. How to create dummy columns on ordinal column (say column Rating has values 1,2,3...,10)

Consider the dataframe df
df = pd.DataFrame(dict(Cats=list('abcdcba'), Ords=[3, 2, 1, 0, 1, 2, 3]))
df
Cats Ords
0 a 3
1 b 2
2 c 1
3 d 0
4 c 1
5 b 2
6 a 3
pd.get_dummies
works the same on either column
with df.Cats
pd.get_dummies(df.Cats)
a b c d
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1
4 0 0 1 0
5 0 1 0 0
6 1 0 0 0
with df.Ords
0 1 2 3
0 0 0 0 1
1 0 0 1 0
2 0 1 0 0
3 1 0 0 0
4 0 1 0 0
5 0 0 1 0
6 0 0 0 1
with both
pd.get_dummies(df)
Ords Cats_a Cats_b Cats_c Cats_d
0 3 1 0 0 0
1 2 0 1 0 0
2 1 0 0 1 0
3 0 0 0 0 1
4 1 0 0 1 0
5 2 0 1 0 0
6 3 1 0 0 0
Notice that it split out Cats but not Ords
Let's expand on this by adding another Cats2 column and calling pd.get_dummies
pd.get_dummies(df.assign(Cats2=df.Cats)))
Ords Cats_a Cats_b Cats_c Cats_d Cats2_a Cats2_b Cats2_c Cats2_d
0 3 1 0 0 0 1 0 0 0
1 2 0 1 0 0 0 1 0 0
2 1 0 0 1 0 0 0 1 0
3 0 0 0 0 1 0 0 0 1
4 1 0 0 1 0 0 0 1 0
5 2 0 1 0 0 0 1 0 0
6 3 1 0 0 0 1 0 0 0
Interesting, it splits both object columns but not the numeric one.

Truth table with 5 inputs and 3 outputs

I have to make a truth table with 5 inputs and 3 outputs, something like this:
A B C D E red green blue
0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1
0 0 0 1 0 0 0 1
.
.
.
.
1 1 0 1 0 0 1 1
.
.
.
1 1 1 1 1 1 0 1
etc. (in total 32 rows, the numbers in the rgb table represents the number of 1's in each row in binary i.e in row 1 1 0 1 0 there are three 1's, so three in binary is 0 1 1).
I would like to present the result of it in the Atanua (http://sol.gfxile.net/atanua/index.html) tool (so fore example when I press button E, the blue light will shine, when pressing A B D the green and blue light will shine and so on). But there is a requirement that I can only use AND, OR, NOT operands, and each operand can only have two inputs. Although I'm using Karnaugh map to minimize it, still for so many records the results for each output are very long (especially for the last one).
I tried to simplify it more by adding all of the three output boolean functions into one, and the minimization process ended pretty well:
A + B + C + D
It seems to work fine (but as there is only one output light, it works only in red green blue column separately). My concern is the fact that I would like to have three outputs (three lights, not one), and is that even possible after this kind of minimization? Is there a good solution to do it in Atanua? Or do I have to make 3 separate boolean functions, no matter how long they will be (and there is a lot of them even after minimization)?
EDIT: the whole truth table :)
A B C D E R G B
0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 1
0 0 0 1 0 0 0 1
0 0 0 1 1 0 1 0
0 0 1 0 0 0 0 1
0 0 1 0 1 0 1 0
0 0 1 1 0 0 1 0
0 0 1 1 1 0 1 1
0 1 0 0 0 0 0 1
0 1 0 0 1 0 1 0
0 1 0 1 0 0 1 0
0 1 0 1 1 0 1 1
0 1 1 0 0 0 1 0
0 1 1 0 1 0 1 1
0 1 1 1 0 0 1 1
0 1 1 1 1 1 0 0
1 0 0 0 0 0 0 1
1 0 0 0 1 0 1 0
1 0 0 1 0 0 1 0
1 0 0 1 1 0 1 1
1 0 1 0 0 0 1 0
1 0 1 0 1 0 1 1
1 0 1 1 0 0 1 1
1 0 1 1 1 1 0 0
1 1 0 0 0 0 1 0
1 1 0 0 1 0 1 1
1 1 0 1 0 0 1 1
1 1 0 1 1 1 0 0
1 1 1 0 0 0 1 1
1 1 1 0 1 1 0 0
1 1 1 1 0 1 0 0
1 1 1 1 1 1 0 1
And the karnaugh map for each color (~is the gate NOT, * is AND, + OR):
RED:
BCDE+ACDE+ABDE+ABCE+ABCD
GREEN:
~A~BDE+~AC~DE+~ACD~E+~BCD~E+~AB~CE+B~CD~E+BC~D~E+A~B~CE+A~B~CD+A~BC~D+AB~C~D
BLUE:
~A~B~C~DE+~A~B~CD~E+~A~BC~D~E+~A~BCDE+~AB~C~D~E+~AB~CDE+~ABC~DE+~ABCD~E+A~B~C~D~E+A~B~CDE+A~BC~DE+A~BCD~E+AB~C~DE+AB~CD~E+ABC~D~E+ABCDE

Have to admit that the formulas are somewhat ugly, but it's not too complicated to implement with logic gatters, because you can reuse parts.
A -----+------+------------- - - -
NOT |
+------|--AND- ~AB
| | |
AND-----|---|-- ~A~B
+--AND-+ |
| +--|---|-- A~B
NOT AND--|-- AB
B -----+------+---+---------- - - -
Here as an example I created all combinations of [not]A and [not]B. You can do the same for C and D. So you can get any combination of [not]A and [not]B and [not]C and [not]D by combining a wire from each "box" with an and gatter (e.g. for ABCD we would take the AB wire AND the CD wire).

Extract columns from row values in Python

I am using this dataframe:
dfPredET.head(5)
id Class
1 Class_2
2 Class_1
3 Class_6
4 Class_2
5 Class_1
and I would like to transforms it indicating if one instance belongs to a class (1) or not (0):
id Class_1 Class_2 Class_3 Class_4 Class_5 Class_6 Class_7 Class_8 Class_9
1 0 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0 0 0
3 0 0 0 0 0 1 0 0 0
4 0 1 0 0 0 0 0 0 0
5 1 0 0 0 0 0 0 0 0
Can I do that using pivot() function? And how?

Use get_dummies:
In [7]:
pd.get_dummies(df)
Out[7]:
id Class_Class_1 Class_Class_2 Class_Class_6
0 1 0 1 0
1 2 1 0 0
2 3 0 0 1
3 4 0 1 0
4 5 1 0 0

Matplotlib pcolor not plotting correctly

I am trying to create a heat map from a DataFrame (df) of IDs (rows) and Positions (columns) at which a motif is possible. If the motif is present the value of the table is 1 and 0 if it is not present. Such as:
ID Position 1 2 3 4 5 6 7 8 9 10 ...etc
A 0 1 0 0 0 1 0 0 0 1
B 1 0 1 0 1 0 0 1 0 0
C 0 0 0 1 0 0 1 0 1 0
D 1 0 1 0 0 0 1 0 1 0
I then multiply this matrix by itself to find the number of times the motifs present co-occur with motifs at other positions using the code:
df.T.dot(df)
To obtain the Data Frame:
POS 1 2 3 4 5 6 7 8 9 10 ...
1 2 0 2 0 1 0 1 1 1 0
2 0 1 0 0 0 1 0 0 0 1
3 2 0 2 0 1 0 1 1 1 0
4 0 0 0 1 0 0 1 0 1 0
5 1 0 1 0 1 0 0 1 0 0
6 0 1 0 0 0 1 0 0 0 1
7 1 0 1 1 0 0 2 0 2 0
8 1 0 1 0 1 0 0 1 0 0
9 1 0 1 1 0 0 2 0 2 0
10 0 1 0 0 0 1 0 0 0 1
...
Which is symmetrical with the diagonal, however when I try to create the Heat Map using
pylab.pcolor(df)
It gives me an asymmetrical map that does not seem to be representing the dotted matrix. I don't have enough reputation to post an image though.
Does anyone know why this might be occurring? Thanks