Massage csv dataframe into dictionary style - python-3.x

Question

I have a pandas dataframe named tshirt_orders from an API call looking like this:
Alice, small, red
Alice, small, green
Bob, small, blue
Bob, small, orange
Cesar, medium, yellow
David, large, purple
How can I get this into a dictionary style format where I first go by size and have sub keys under for name and another sublist for color so that I can address it when iterating over by using tshirt_orders?
Like this:
size:
small:
Name:
Alice:
Color:
red
green
Bob:
Color:
blue
orange
medium:
Name:
Cesar:
Color:
yellow
large:
Name:
David:
Color:
purple
What would be the best solution to change this? It is in a pandas dataframe but changing that isn't a problem should there be better solutions.

Answer 1

The close is write DataFrame to yaml.
First create nested dictionaries in dict comprehension:
print (df)
A B C
0 Alice small red
1 Alice small green
2 Bob small blue
3 Bob small orange
4 Cesar medium yellow
5 David large purple
d = {k:v.groupby('A', sort=False)['C'].apply(list).to_dict()
for k, v in df.groupby('B', sort=False)}
print (d)
{'small': {'Alice': ['red', 'green'],
'Bob': ['blue', 'orange']},
'medium': {'Cesar': ['yellow']},
'large': {'David': ['purple']}}
Add size to dict for key and then write to yaml file:
import yaml
with open('result.yml', 'w') as yaml_file:
yaml.dump({'size': d}, yaml_file, default_flow_style=False, sort_keys=False)
size:
small:
Alice:
- red
- green
Bob:
- blue
- orange
medium:
Cesar:
- yellow
large:
David:
- purple
Or create json:
import json
with open("result.json", "w") as twitter_data_file:
json.dump({'size': d}, twitter_data_file, indent=4)
{
"size": {
"small": {
"Alice": [
"red",
"green"
],
"Bob": [
"blue",
"orange"
]
},
"medium": {
"Cesar": [
"yellow"
]
},
"large": {
"David": [
"purple"
]
}
}
}
EDIT:
df = df.assign(A1='Name', B1='size', C1='Color')
df1 = df.groupby(['B1','B','A1','A','C1'], sort=False)['C'].apply(list).reset_index()
#https://stackoverflow.com/a/19900276
def recur_dictify(frame):
if len(frame.columns) == 1:
if frame.values.size == 1: return frame.values[0][0]
return frame.values.squeeze()
grouped = frame.groupby(frame.columns[0], sort=False)
d = {k: recur_dictify(g.iloc[:,1:]) for k,g in grouped}
return d
d = recur_dictify(df1)
print (d)
{'size': {'small': {'Name': {'Alice': {'Color': ['red', 'green']},
'Bob': {'Color': ['blue', 'orange']}}},
'medium': {'Name': {'Cesar': {'Color': ['yellow']}}},
'large': {'Name': {'David': {'Color': ['purple']}}}}}
import yaml
with open('result.yml', 'w') as yaml_file:
yaml.dump(d, yaml_file, default_flow_style=False, sort_keys=False)