Create a Program to Implement Data Preparation in Python Assignment Solution
- Instructions
- Objective
- Requirements and Specifications
Instructions
Objective
Write a program to implement data preparation in python language.
Requirements and Specifications
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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