Lernen Summarizing Case Outcomes | Analyzing Legal Case Data

When working with legal case data, you often encounter a variety of outcomes such as "Settled," "Dismissed," "Won," and "Lost." Understanding how frequently each outcome occurs is crucial for legal professionals. These summary statistics help you quickly assess patterns in your cases, evaluate the effectiveness of different legal strategies, and communicate results to clients or colleagues. Summarizing case outcomes also supports data-driven decision-making by highlighting trends and areas for improvement.


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import pandas as pd
from io import StringIO

data = """CaseID,LawFirm,Outcome
1,Firm A,Won
2,Firm B,Lost
3,Firm A,Settled
4,Firm C,Won
5,Firm A,Lost
6,Firm B,Settled
7,Firm A,Won
8,Firm B,Won
9,Firm C,Lost
10,Firm A,Settled
"""

df = pd.read_csv(StringIO(data))
outcome_counts = df["Outcome"].value_counts()
print(outcome_counts)

The value_counts() method in pandas is a powerful tool for summarizing legal data. By applying this method to a column, you can quickly see how many times each unique value appears. For legal case data, this means you can instantly count how many cases were "Won", "Lost", "Settled", or "Dismissed". This summary provides a clear overview of your case results, making it easier to identify patterns and present findings.


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# Calculate the percentage of cases won by 'Firm A'
total_cases_firm_a = df[df["LawFirm"] == "Firm A"].shape[0]
won_cases_firm_a = df[(df["LawFirm"] == "Firm A") & (df["Outcome"] == "Won")].shape[0]

if total_cases_firm_a > 0:
    percent_won = (won_cases_firm_a / total_cases_firm_a) * 100
    print(f"Firm A won {percent_won:.1f}% of their cases.")
else:
    print("Firm A has no cases.")

1. What pandas method is used to count occurrences of each unique value in a column?

2. Fill in the blank: To calculate the percentage of cases with a specific outcome, divide the count by _______.

3. How can summary statistics support legal decision-making?

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