Create a Program to Implement Screening Method in Python Assignment Solution.

#**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()