Python多个DataFrames到字典

Question

I have several 2 column data frames with varying row counts. Essentially 1 column is the categorical string value and the second column is the label encoded numeric value. Now when I evaluate feature values from the model output, I only see the numeric value and I want to see the categorical value.

Is the best way to do this to create a dictionary of lists/dictionaries and then loop through the dictionary where value equals and replace?

I'm open to a different approach.

df1 = df1[['A1','A2']].drop_duplicates().sort_values(by=['A2'])
df2 = df2[['B1','B2']].drop_duplicates().sort_values(by=['B2'])
df3 = df3[['C1','C2']].drop_duplicates().sort_values(by=['C2'])

So...

df1
A1    A2
cat   1

df2
B1    B2
dog   2

df3
C1    C2
fish  3
bird  4

dict= {df1: {cat:1}, df2: {dog:2}, df3: {fish:3}}

Answer 1

Assuming your omission of 'bird' was an oversight:

Code:

import pandas as pd
df1 = pd.DataFrame(data = [['cat', 1]], columns=['A1', 'A2'])
df2 = pd.DataFrame(data=[['dog', 2]], columns=['B1', 'B2'])
df3 = pd.DataFrame(data=[['fish', 3], ['bird', 4]], columns=['C1', 'C2'])
result = {"df{}".format(i): dict(df.values.tolist()) for i, df in enumerate([df1, df2, df3], start=1)}
print(result)

Output:

{'df1': {'cat': 1}, 'df2': {'dog': 2}, 'df3': {'fish': 3, 'bird': 4}}

Alternatively, you can create a flat dictionary as follows. (DataFrame information is lost.)

Alternative:

alt = {k: v for df in [df1, df2, df3] for k, v in df.values.tolist()}
print(alt)

Output:

{'cat': 1, 'dog': 2, 'fish': 3, 'bird': 4}