【数据处理】：（二）文本类_读取arff文件-优快云博客

一、读取arff文件

1、使用scipy.io中arff读取文件

import pandas as pd
from scipy.io import arff

data,meta = arff.loadarff("../Dry_Bean_Dataset/Dry_Bean_Dataset.arff")

2、转化为DataFrame

df =pd.DataFrame(data)
print(df.shape)
print(df.head())
print(df.isnull().sum())
print(df.describe().T)
print(df.Class.unique())

3、使用LabelEncoder对类别Class进行编码

from sklearn.preprocessing import  LabelEncoder
lb_encoder = LabelEncoder()
for col in df.columns:
    df.Class = lb_encoder.fit_transform(df.Class)

print(df.Class.value_counts())

4、使用sns与plt绘制不同类别的数量

import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(x=df.Class)
plt.title('Class')
plt.show()

二、针对空值填补

1、查找空值

DataDF.isnull().sum().sort_values(ascending=False)

2、删除有缺失值的行或者列

'''移除行或列"'''
drop_row_data = train_data.dropna()   # 移除缺值的行
drop_col_data = train_data.dropna(axis=1)  # 移除至少缺一个值的列

3、常规填补法

均值

all_features.Embarked = all_features.Embarked.fillna(all_features.Embarked.mean())

最近邻,前后，在时间序列分析中比较常见

print(DataDF.UnitPrice.fillna(method='ffill')) # 前向后填充
print(DataDF.UnitPrice.fillna(method='bfill')) # 后向前填充

4、使用与之相关的属性，相近的求均值填补

sns.boxplot(x = train_data["Pclass"], y = train_data["Age"])plt.show()

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'''
通过数据可视化，发现Age与Pclass相关性较大
'''
def add_age(cols):
    Age=cols[0]
    Pclass=cols[1]
    if pd.isnull(Age):
        if Pclass==1:
            return train_data[train_data['Pclass']==1]['Age'].mean()
        elif Pclass==2:
            return train_data[train_data['Pclass']==2]['Age'].mean()
        elif Pclass==3:
            return train_data[train_data['Pclass']==3]['Age'].mean()
    else:
        return Age
train_data['Age']=train_data[['Age','Pclass']].apply(add_age,axis=1)

5、具体分析

df_data['Title'] = df_data.Name.str.extract(' ([A-Za-z]+)\.', expand=False)

pd.crosstab(df_data['Title'], df_data['Sex']).transpose()

Title	Capt	Col	Countess	Don	Dona	Dr	Jonkheer	Lady	Major	Master	Miss	Mlle	Mme	Mr	Mrs	Ms	Rev	Sir
Sex
female	0	0	1	0	1	1	0	1	0	0	260	2	1	0	187	2	0	0
male	1	4	0	1	0	7	1	0	2	61	0	0	0	757	0	0	8	1

"""
根据统计结果填补Age
"""

train_data['Initial']=0
for i in train_data:
    train_data['Initial']=train_data.Name.str.extract('([A-Za-z]+)\.')

pd.crosstab(train_data.Initial,train_data.Sex).T.style.background_gradient()
train_data['Initial'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','Countess','Jonkheer','Col','Rev','Capt','Sir','Don'],['Miss','Miss','Miss','Mr','Mr','Mrs','Mrs','Other','Other','Other','Mr','Mr','Mr'],inplace=True)

train_data.loc[(train_data.Age.isnull())&(train_data.Initial=='Mr'),'Age']=33
train_data.loc[(train_data.Age.isnull())&(train_data.Initial=='Mrs'),'Age']=36
train_data.loc[(train_data.Age.isnull())&(train_data.Initial=='Master'),'Age']=5
train_data.loc[(train_data.Age.isnull())&(train_data.Initial=='Miss'),'Age']=22
train_data.loc[(train_data.Age.isnull())&(train_data.Initial=='Other'),'Age']=46

三、文字类转化为数字

1、使用LabelEncoder对特征进行编码

from sklearn.preprocessing import  LabelEncoder
lb_encoder = LabelEncoder()
for col in df.columns:
    df.Class = lb_encoder.fit_transform(df.Class)
print(df.Class.value_counts())

2、直接替换

若该属性只有几类，可直接替换

train_data.Embarked=train_data.Embarked.map({'S':0, 'C':1, 'Q':2})

或

all_features.Sex.replace('male', 1, inplace=True)
all_features.Sex.replace('female', 0, inplace=True)
all_features.Embarked.replace('S', 0, inplace=True)
all_features.Embarked.replace('C', 1, inplace=True)
all_features.Embarked.replace('Q', 2, inplace=True)

3、one-hot编码

四、缩放和规范化

在缩放中更改数据的范围，

而在规范化方面，更改数据分布的形状。

in scaling, you’re changing the range of your data, while
in normalization, you’re changing the shape of the distribution of your data.

缩放：

original_goal_data = pd.DataFrame(kickstarters_2017.goal)
scaled_goal_data = minmax_scaling(original_goal_data,columns=["goal"])

规范化：

original_pledged = pd.DataFrame(kickstarters_2017.pledged)
index_positive_pledges = kickstarters_2017.pledged > 0

positive_pledges_only = kickstarters_2017.pledged.loc[index_positive_pledges]

normalized_values = pd.Series(stats.boxcox(positive_pledges_only)[0], 
                              name='pledged', index=positive_pledges_only.index)

五、日期处理

1、将日期规范化后存入dete_parsed列中

landslides['date_parsed'] = pd.to_datetime(landslides['date'], format="%m/%d/%Y")

注意！format中%Y尽量用大写

2、从时间属性值获取日期/月份/年份

day_of_month_earthquakes = earthquakes['date_parsed'].dt.day

六、数据统一

对于同一个属性的同一个值，由于输入时空格，大小写等原因，造成不一致的问题

1、提取出属性中出现的值，并使用unique()获取互不相同的值

countries = professors['Country'].unique()

2、删除头尾空格，把所有大写字符转化为小写

professors['Country'] = professors['Country'].str.lower()

professors['Country'] = professors['Country'].str.strip()

Python strip() 方法用于移除字符串头尾指定的字符（默认为空格或换行符）或字符序列。

Python lower() 方法转换字符串中所有大写字符为小写。

3、模糊处理

matches = fuzzywuzzy.process.extract("usa", countries, limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)
replace_matches_in_column(df=professors, column='Country', string_to_match="usa", min_ratio=70)

def replace_matches_in_column(df, column, string_to_match, min_ratio = 47):
    strings = df[column].unique()
    
    matches = fuzzywuzzy.process.extract(string_to_match, strings, limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)

    close_matches = [matches[0] for matches in matches if matches[1] >= min_ratio]

    rows_with_matches = df[column].isin(close_matches)

    df.loc[rows_with_matches, column] = string_to_match