9 17學習筆記（重複值處理 資料清洗）

pandas的duplicated()判斷重複值記錄

pandas的drop_duplicates()刪除資料記錄，可指定特定列或全部

numpy中unique()返回所有不同的值，且按照從小到大的順序

set()，python自帶內建函式，也能返回唯一元素的集合

示例：重複值處理

import pandas as pd

data1=['a',1]
data2=['a',1]
data3=['b',2]
data4=['b',2]
data=pd.dataframe([data1,data2,data3,data4],columns=['col1','col2'])
print(data)
#判斷isduplicated=data.duplicated()
print(isduplicated)
#刪除new_1=data.drop_duplicates()
new_2=data.drop_duplicates(['col1'])
new_3=data.drop_duplicates(['col1','col2'])
print(new_1)
print(new_2)
print(new_3)

col1 col2

0 a 1

1 a 1

2 b 2

3 b 2

0 false

1 true

2 false

3 true

dtype: bool

col1 col2

0 a 1

2 b 2

col1 col2

0 a 1

2 b 2

col1 col2

0 a 1

2 b 2

示例：資料清洗

import re

#載入正規表示式庫
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn import model_selection
from sklearn.preprocessing import labelencoder
from sklearn.ensemble import randomforestregressor
from sklearn.ensemble import gradientboostingregressor
#特徵工程處理
train_df_org=pd.read_csv('train.csv')
test_df_org=pd.read_csv('test.csv')
test_df_org['survived']=0
#---pclass欄位---建立pcalss fare category
def pclass_fare_category(df,pclass1_mean_fare,pclass2_mean_fare,pclass3_mean_fare):
if df['pclass']==1:
if df['fare']<=pclass1_mean_fare:
return 'pclass1_low'
else:
return 'pclass1_high'
elif df['pclass']==2:
if df['fare']<=pclass2_mean_fare:
return 'pclass2_low'
else:
return 'pclass2_high'
elif df['pclass']==3:
if df['fare']<=pclass3_mean_fare:
return 'pclass3_low'
else:
return 'pclass3_high'
pclass1_mean_fare=combined_train_test['fare'].groupby(by=combined_train_test['pclass']).mean().get([1]).values[0] //取pclass=1的艙的平均票價
pclass2_mean_fare=combined_train_test['fare'].groupby(by=combined_train_test['pclass']).mean().get([2]).values[0]
pclass3_mean_fare=combined_train_test['fare'].groupby(by=combined_train_test['pclass']).mean().get([3]).values[0]
print('# pclass_fare_category...')
print(combined_train_test.groupby(['pclass_fare_category','survived'])['survived'].count())

#/ pclass_fare_category…

pclass_fare_category survived

pclass1_high 0 49

1 48

pclass1_low 0 138

1 88

pclass2_high 0 68

1 43

pclass2_low 0 122

1 44

pclass3_high 0 174

1 42

pclass3_low 0 416

1 77

name: survived, dtype: int64

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