CODEWITHSUNDEEP
pythonpandasdataframedictionarypandas-groupby
Girish Kumar Chandora
Girish Kumar Chandora

Reputation: 188

Create a grouped DataFrame from a nested dictionary

What is the efficient way to buid a grouped DataFrame from the nested dictionary.
Code snippet

# Endcoding Description
encoding_dict = {"Age":{"Middle": 0,
                        "Senior": 1,
                        "Young": 2},
                 "Sex":{"F": 0,
                        "M": 1},
                 "BP":{"High": 0,
                       "Low": 1,
                       "Normal": 2},
                 "Cholesterol":{"High": 0,
                                "Normal": 1}}

# Step 1 : Create DataFrame
df_1 = pd.DataFrame({"Features": ["Age"]*3 + ["Sex"]*2 + ["BP"]*3 + ["Cholesterol"]*2,
                     "Categories":["Middle", "Senior", "Young", "F", "M", "High", "Low","Normal", "High", "Normal"],
                     "Encoding":[0, 1, 2, 0, 1, 0, 1, 2, 0, 1]})

# Step 2 : Grouped DataFrame
grouped  = df_1.groupby(["Features","Categories"]).sum()
print(grouped)

Output

                           Encoding
Features    Categories          
Age         Middle             0
            Senior             1
            Young              2
BP          High               0
            Low                1
            Normal             2
Cholesterol High               0
            Normal             1
Sex         F                  0
            M                  1

What's the efficeint way to create a desired grouped dataframe of the nested dictionary without performing the step(1) manually?

Upvotes: 0

Views: 55

Answers (1)

Henry Ecker
Henry Ecker

Reputation: 35626

A dictionary comprehension to build a frame constructor then add the axis names could work:

df = pd.DataFrame(
    {'encoding': {(k, sub_k): v
                  for k, sub_d in encoding_dict.items()
                  for sub_k, v in sub_d.items()}}
).rename_axis(index=['Features', 'Categories'])

df:

                        encoding
Features    Categories          
Age         Middle             0
            Senior             1
            Young              2
BP          High               0
            Low                1
            Normal             2
Cholesterol High               0
            Normal             1
Sex         F                  0
            M                  1

Upvotes: 1

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