对pandas进行数据预处理的实例讲解

import pandas as pd
import numpy as np
train_df =pd.read_csv('../datas/train.csv') # train set
test_df = pd.read_csv('../datas/test.csv') # test set
combine = [train_df, test_df]

#查看前五条数据
print train_df.head(5) 
#查看每列数据类型以及nan情况
print train_df.info() 
# 获得所有object属性
print train_data.describe(include=['O']).columns 

#查看连续数值属性基本统计情况
print train_df.describe() 
#查看object属性数据统计情况
print train_df.describe(include=['O']) 
# 统计Title单列各个元素对应的个数
print train_df['Title'].value_counts() 
# 属性列删除
train_df = train_df.drop(['Name', 'PassengerId'], axis=1) 

# 直接丢弃缺失数据列的行
print df4.dropna(axis=0,subset=['col1']) # 丢弃nan的行,subset指定查看哪几列 
print df4.dropna(axis=1) # 丢弃nan的列
# 采用其他值填充
dataset['Cabin'] = dataset['Cabin'].fillna('U') 
dataset['Title'] = dataset['Title'].fillna(0) 
# 采用出现最频繁的值填充
freq_port = train_df.Embarked.dropna().mode()[0]
dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)
# 采用中位数或者平均数填充
test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)
test_df['Fare'].fillna(test_df['Fare'].dropna().mean(), inplace=True)

# 创造一个新列，FareBand，将连续属性Fare切分成四份
train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)
# 查看切分后的属性与target属性Survive的关系
train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)
# 建立object属性映射字典 
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Royalty":5, "Officer": 6}
dataset['Title'] = dataset['Title'].map(title_mapping)

print train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)

”' 
创建df对象 
””' 
s1 = pd.Series([1,2,3,np.nan,4,5]) 
s2 = pd.Series([np.nan,1,2,3,4,5]) 
print s1 
dates = pd.date_range(“20130101”,periods=6) 
print dates 
df = pd.DataFrame(np.random.rand(6,4),index=dates,columns=list(“ABCD”)) 
# print df 
df2 = pd.DataFrame({“A”:1, 
‘B':pd.Timestamp(‘20130102'), 
‘C':pd.Series(1,index=list(range(4)),dtype='float32'), 
‘D':np.array([3]*4,dtype=np.int32), 
‘E':pd.Categorical([‘test','train','test','train']), 
‘F':'foo' 
}) 
# print df2.dtypes

df3 = pd.DataFrame({'col1':s1,
     'col2':s2
})
print df3

'''
2.查看df数据
'''
print df3.head(2) #查看头几条
print df3.tail(3) #查看尾几条
print df.index #查看索引
print df.info() #查看非non数据条数
print type(df.values) #返回二元数组
# print df3.values
print df.describe() #对每列数据进行初步的统计
print df3
print df3.sort_values(by=['col1'],axis=0,ascending=True) #按照哪几列排序

'''
3.选择数据
'''
ser_1 = df3['col1']
print type(ser_1) #pandas.core.series.Series
print df3[0:2] #前三行
print df3.loc[df3.index[0]] #通过index来访问
print df3.loc[df3.index[0],['col2']] #通过行index，和列名来唯一确定一个位置
print df3.iloc[1] #通过位置来访问
print df3.iloc[[1,2],1:2] #通过位置来访问
print "==="
print df3.loc[:,['col1','col2']].as_matrix() # 返回nunpy二元数组
print type(df3.loc[:,['col1','col2']].as_matrix())

'''
4.布尔索引，过滤数据
'''
print df3[df3.col1 >2]
df4 = df3.copy()
df4['col3']=pd.Series(['one','two','two','three','one','two'])
print df4
print df4[df4['col3'].isin(['one','two'])]
df4.loc[:,'col3']="five"
print df4

'''
5.缺失值处理，pandas将缺失值用nan代替
'''
print pd.isnull(df4)
print df4.dropna(axis=0,subset=['col1']) # 丢弃nan的行,subset指定查看哪几列
print df4.dropna(axis=1) # 丢弃nan的列