Pandas Dataframe: change columns, index and plot - pandas

Question

Hi, I generated the table above using Counter from collections for counting the combinations of 3 variables from a dataframe: Jessica, Mike, and Dog. I got the combination and their counts.
Any help to make that table a bit more prettier? I would like to rename the index as grp1, grp2, etc and the column as well with something else than 0.
Also, what would be the best plot to use for plotting the different groups?
Thanks for your help!!
I used this command to produce the table here:
df= np.random.choice(["Mike", "Jessica", "Dog"], size=(20, 3))
Z= pd.DataFrame(df,columns=['a', 'b', 'c'])
import collections
from collections import Counter
LL= Z.apply (Counter, axis= "columns").value_counts()
H= pd.DataFrame(LL)
print(H)

Answer 1

quite an unusual technique....
you can change the dict index to a multi-index
then plot() as barh and labels make sense
df= np.random.choice(["Mike", "Jessica", "Dog"], size=(20, 3))
Z= pd.DataFrame(df,columns=['a', 'b', 'c'])
import collections
from collections import Counter
LL= Z.apply (Counter, axis= "columns").value_counts()
H= pd.DataFrame(LL)
I = pd.Series(H.index).apply(pd.Series)
H = H.set_index(pd.MultiIndex.from_arrays(I.T.values, names=I.columns))
H.plot(kind="barh")
H after setting as multi-index
0
Mike Dog Jessica
2.0 1.0 NaN 5
NaN 1.0 4
NaN 1.0 2.0 3
1.0 NaN 2.0 3
1.0 1.0 2
NaN NaN 3.0 1
2.0 1.0 1
3.0 NaN NaN 1

Answer 2

Instead of using counter, you can apply value_counts directly to each row:
import pandas as pd
from matplotlib import pyplot as plt
# Hard Coded For Reproducibility
df = pd.DataFrame({'a': {0: 'Dog', 1: 'Jessica', 2: 'Mike',
3: 'Dog', 4: 'Dog', 5: 'Dog',
6: 'Jessica', 7: 'Jessica',
8: 'Dog', 9: 'Dog', 10: 'Jessica',
11: 'Mike', 12: 'Dog',
13: 'Jessica', 14: 'Mike',
15: 'Mike',
16: 'Mike', 17: 'Dog',
18: 'Jessica', 19: 'Mike'},
'b': {0: 'Mike', 1: 'Mike', 2: 'Jessica',
3: 'Jessica', 4: 'Dog', 5: 'Jessica',
6: 'Mike', 7: 'Dog', 8: 'Mike',
9: 'Dog', 10: 'Dog', 11: 'Dog',
12: 'Dog', 13: 'Jessica',
14: 'Jessica', 15: 'Dog',
16: 'Dog', 17: 'Dog', 18: 'Jessica', 19: 'Jessica'},
'c': {0: 'Mike', 1: 'Dog', 2: 'Jessica',
3: 'Dog', 4: 'Dog', 5: 'Dog', 6: 'Dog',
7: 'Jessica', 8: 'Mike', 9: 'Dog',
10: 'Dog', 11: 'Mike', 12: 'Jessica',
13: 'Jessica', 14: 'Jessica',
15: 'Jessica', 16: 'Jessica',
17: 'Dog', 18: 'Mike', 19: 'Dog'}})
# Apply value_counts across each row
df = df.apply(pd.value_counts, axis=1) \
.fillna(0)
# Group By All Columns and
# Get Duplicate Count From Group Size
df = pd.DataFrame(df
.groupby(df.columns.values.tolist())
.size()
.sort_values())
# Plot
plt.figure()
df.plot(kind="barh")
plt.tight_layout()
plt.show()
df after groupby, size, and sort:
0
Dog Jessica Mike
0.0 3.0 0.0 1
1.0 2.0 0.0 1
0.0 2.0 1.0 3
1.0 0.0 2.0 3
3.0 0.0 0.0 3
2.0 1.0 0.0 4
1.0 1.0 1.0 5
Plt: