CODEWITHSUNDEEP
pythonpandas
Blueboots
Blueboots

Reputation: 59

Conditional Probability in Python

I have a dataframe where I have six columns that are coded 1 for yes and 0 for no. There is also a column for year. The output I need is finding the conditional probability between each column being coded 1 according to year. I tried incorporating some suggestions from this post: Pandas - Conditional Probability of a given specific b but with no luck. Other things I came up with are inefficient. I am really struggling to find the best way to go about this.

Current dataframe:

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Output I am seeking:

enter image description here

Upvotes: 0

Views: 517

Answers (1)

Parfait
Parfait

Reputation: 107767

To get your wide-formatted data into the long format of linked post, consider running melt and then run a self merge by year for all pairwise combinations (avoiding same keys and reverse duplicates). Then calculate as linked post shows:

long_df = current_df.melt(
    id_vars = "Year",
    var_name = "Key",
    value_name = "Value"
)

pairwise_df = (
    long_df.merge(
        long_df,
        on = "Year",
        suffixes = ["1", "2"]
    ).query("Key1 < Key2")
    .assign(
        Both_Occur = lambda x: np.where(
            (x["Value1"] == 1) & (x["Value2"] == 1),
            1,
            0
        )
    )
)

prob_df = (
    (pairwise_df.groupby(["Year", "Key1", "Key2"])["Both_Occur"].value_counts() / 
         pairwise_df.groupby(["Year", "Key1", "Key2"])["Both_Occur"].count()
    ).to_frame(name = "Prob")
    .reset_index()
    .query("Both_Occur == 1")
    .drop(["Both_Occur"], axis = "columns")
)

To demonstrate with reproducible data

import numpy as np
import pandas as pd

np.random.seed(112621)
random_df = pd.DataFrame({
    'At least one tree': np.random.randint(0, 2, 100),
    'At least two trees': np.random.randint(0, 2, 100),
    'Clouds': np.random.randint(0, 2, 100),
    'Grass': np.random.randint(0, 2, 100),
    'At least one mounain': np.random.randint(0, 2, 100),
    'Lake': np.random.randint(0, 2, 100),
    'Year': np.random.randint(1983, 1995, 100)
})

# ...same code as above...

prob_df
     Year                  Key1                Key2      Prob
0    1983  At least one mounain   At least one tree  0.555556
2    1983  At least one mounain  At least two trees  0.555556
5    1983  At least one mounain              Clouds  0.416667
6    1983  At least one mounain               Grass  0.555556
8    1983  At least one mounain                Lake  0.555556
..    ...                   ...                 ...       ...
351  1994    At least two trees               Grass  0.490000
353  1994    At least two trees                Lake  0.420000
355  1994                Clouds               Grass  0.280000
357  1994                Clouds                Lake  0.240000
359  1994                 Grass                Lake  0.420000

Upvotes: 1

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