Example Analysis of adaboost algorithm in python Machine Learning Sklearn
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Pandas Batch Processing Physical Test Scores import numpy as npimport pandas as pdfrom pandas import Series, Dataimport Framematplotlib.pyplot as pltdata = pd.read_excel("/Users/zhucan/Desktop/18th Grade High Physical Test Scores Summary.xls")cond = data["Class"] != "class"data = data[cond] data.fillna(0,inplace=True)data.isnull().any() There is no empty data
Results:
Class False
Gender False
Name False
1000 m False
50 m False
Long jump False
Forward Bend False
False
Vital capacity False
Height False
Weight False
dtype: bool
data.head()
#1000 m score has string with intdef convert(x): if isinstance(x,str): minute,second = x.split("'") int(minute) minute = int(minute) second = int(second) return minute + second/100.0 else: return xdata["1000m"] = data["1000m"].map(convert)
score = pd.read_excel("/Users/zhucan/Desktop/Body Side Scores.xls",header=[0,1])score
def convert(item): m,s = item.strip('"').split("'") m,s =int(m),int(s) return m+s/100.0score.iloc[:,-4] = score.iloc[:,-4].map(convert) def convert(item): m,s = item.strip('"').split("'") m,s =int(m),int(s) return m+s/100.0 score.iloc[:,-2] = score.iloc[:,-2].map(convert)score
data.columns = ['class',' gender','name',' male 1000','male 50m run',' long jump','forward flex',' pull-up','vital capacity',' height','weight'] data["male 50m run"] = data["male 50m run"].astype(np.float)for col in ["male 1000","male 50m run"]: #Criteria for obtaining results s = score[col] def convert(x): for i in range(len(s)): if xs["score"].iloc[-1]: return 0 #Running too slow elif (x>s["score"].iloc[i-1]) and (xs["score"].iloc[i]: return s["fraction"].iloc[i] return 0 data[col+"score"] = data[col].map(convert)
data.columns
Results:
Index(['class',' gender','name',' male 1000','male 50m run',' long jump','forward flex',' pull-up','vital capacity',' height', 'weight',' men's 1000 performance','men's 50-meter running performance',' long jump performance','body forward bend performance',' pull up performance','vital capacity performance'], dtype='object')#According to the index order, go to data values cols = [' class','gender',' name','male 1000',' male 1000','male 50m run',' male 50m run','long jump',' long jump','body flexion','body flexion','pull-up',' vital capacity ',' vital capacity ',' height','weight'] data[cols]
#Calculate BMIdata["BMI"] = data["Weight"]/data["Height"]def convert(x): if x>100: return x/100 else: return xdata["Height"] = data["Height"].map(convert)data["BMI"] = data["Weight"]/(data["Height"])**2def convert_bmi(x): if x >= 26.4: return 60 elif (x 23.3 and x = 16.5 and x
