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Create a Program to Implement Screening Method in Python Assignment Solution

July 10, 2024
Dr. Matthew Hernandez
Dr. Matthew
🇨🇭 Switzerland
Python
Dr. Matthew Hernandez, an esteemed Computer Science researcher, obtained his PhD from ETH Zurich, Switzerland. With 6 years of experience under his belt, he has successfully completed over 400 Python assignments, demonstrating his proficiency and commitment to excellence.
Key Topics
  • Instructions
    • Objective
  • Requirements and Specifications
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Instructions

Objective

Write a python assignment program to implement screening method

Requirements and Specifications

program-to-implement-screening-method-in-python

Source Code

#**4.** import pandas as pd # reading and converting the data ito pandas Dataframe data = pd.read_csv("spahn.csv") # applaying pandas method .describe() data.describe().T import pandas as pd # reading and converting the data ito pandas Dataframe data = pd.read_csv("spahn.csv") # applaying pandas method .describe() data[['ERA+']].describe() data[['SO']].boxplot() data[['ERA']].boxplot() data[['ERA+']].boxplot() #**5.** data = pd.read_csv('d5000.csv') data.head() data.describe() data.plot.scatter(x = 'HR', y = 'SO') #**6.** data = pd.read_csv('hofbatting.csv') data.head() data.describe().T import numpy as np data = pd.read_csv('hofbatting.csv') mid_career_keys = ['19 th Century', 'Dead Ball', 'Lively Ball', 'Integration', 'Expansion', 'Free Agency', 'Long Ball'] mid_career_values = [] for row in data[['From', 'To']].values: From, To = row[0], row[1] #up to the 1900 Season if To <= 1900: mid_career_values.append(mid_career_keys[0]) #1901 through 1919 elif From > 1900 and To <= 1919: mid_career_values.append(mid_career_keys[1]) #1920 through 1941 elif From > 1920 and To <= 1941: mid_career_values.append(mid_career_keys[2]) #1942 through 1960 elif From > 1942 and To <= 1960: mid_career_values.append(mid_career_keys[3]) #1961 through 1976 elif From > 1961 and To <= 1976: mid_career_values.append(mid_career_keys[4]) #1977 through 1993 elif From > 1977 and To <= 1993: mid_career_values.append(mid_career_keys[5]) #after 1993 elif From > 1993: mid_career_values.append(mid_career_keys[6]) else: mid_career_values.append('not-labled') data['mid-career'] = mid_career_values data.head() data.groupby('mid-career')['mid-career'].value_counts() data.groupby('mid-career').sum() hist = data['mid-career'].hist() data.plot.scatter(x = 'OBP', y = 'SLG') OPS_values = [] for row in data[['OBP', 'SLG']].values: OPS_values.append(row[0] + row[1]) data['OPS'] = OPS_values data.columns data[['OBP', 'SLG', 'OPS']].head() data['OPS'] = (data['OPS'] - data['OPS'].mean())/data['OPS'].std(ddof=0) data['OPS'].head() data.plot.scatter(x = 'OPS', y = 'mid-career') HR_AB_values = [] for row in data[['HR', 'AB']].values: HR_AB_values.append(row[0] + row[1]) data['HR/AB'] = HR_AB_values data['HR/AB'] df=data.groupby('mid-career')['HR/AB'] df.describe() data.boxplot()

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