3-15 大数据处理技巧

In [1]:

import pandas as pd

gl=pd.read_csv('./Titanic_Data-master/Titanic_Data-master/train.csv')
gl.head()

Out[1]:

 

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

In [2]:

gl.shape#查看大小

Out[2]:

(891, 12)

 

1.查看基本数据信息

In [3]:

gl.info(memory_usage='deep')#查看基本信息

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 318.5 KB

 

2.查看不一样数据类型的占用空间

In [4]:

for dtype in['float64','int64','object']:
    selevtrd_dtype=gl.select_dtypes(include=[dtype])#塞出不一样数据类型
    mean_usage_b=selevtrd_dtype.memory_usage(deep=True).mean()#求对应的数据类型的内存平均值    
    mean_usage_mb=mean_usage_b/1024**2
    print('平均内存占用',dtype,mean_usage_mb)

 

平均内存占用 float64 0.004572550455729167
平均内存占用 int64 0.005685170491536458
平均内存占用 object 0.043910980224609375

In [5]:

import numpy as np
int_types=['uint8','int8','int16','int32','int64']
for it in int_types:
    print(np.iinfo(it))#简化版info，查看每种数据类型的值范围

 

Machine parameters for uint8
---------------------------------------------------------------
min = 0
max = 255
---------------------------------------------------------------

Machine parameters for int8
---------------------------------------------------------------
min = -128
max = 127
---------------------------------------------------------------

Machine parameters for int16
---------------------------------------------------------------
min = -32768
max = 32767
---------------------------------------------------------------

Machine parameters for int32
---------------------------------------------------------------
min = -2147483648
max = 2147483647
---------------------------------------------------------------

Machine parameters for int64
---------------------------------------------------------------
min = -9223372036854775808
max = 9223372036854775807
---------------------------------------------------------------

 

3.经过转换数据类型来减小数据占用内存

In [6]:

def menu_usage(pandas_obj):
    if isinstance(pandas_obj,pd.DataFrame):#isinstance() 函数来判断一个对象是不是一个已知的类型，相似 type()。
        usage_b=pandas_obj.memory_usage(deep=True).sum()#求内存占用量的总和
    else:
        usage_b=pandas_obj.memory_usage(deep=True)
    usage_mb=usage_b/1024**2
    return'{:03.2f}MB'.format(usage_mb)#规定数据格式是小数点后2位

gl_int=gl.select_dtypes(include=['int64'])#去除int64的数据
coverter_int=gl_int.apply(pd.to_numeric,downcast='unsigned')#pd.to_numeric数据转换；downcast='unsigned'向下转换成无符号
print(menu_usage(gl_int))
print(menu_usage(coverter_int))

 

0.03MB
0.01MB

In [7]:

gl_float=gl.select_dtypes(include=['float64'])#去除int64的数据
coverter_float=gl_int.apply(pd.to_numeric,downcast='float')#pd.to_numeric数据转换；downcast='unsigned'向下转换成无符号
print(menu_usage(gl_float))
print(menu_usage(coverter_float))

 

0.01MB
0.02MB

 

4.把全部数据类型转换成对应的不一样的数据类型

In [8]:

optimized_gl=gl.copy()

optimized_gl[coverter_int.columns]=coverter_int
optimized_gl[coverter_float.columns]=coverter_float
print(menu_usage(gl))
print(menu_usage(optimized_gl))

 

0.31MB
0.29MB

 

5.describe():统计各项lable的属性指标

In [9]:

gl_obj=gl.select_dtypes(include=['object']).copy()
gl_obj.describe()

Out[9]:

 

	Name	Sex	Ticket	Cabin	Embarked
count	891	891	891	204	889
unique	891	2	681	147	3
top	Turkula, Mrs. (Hedwig)	male	347082	C23 C25 C27	S
freq	1	577	7	4	644

 

6.把重复的lable放在一个空间里：即转换成category类型

In [10]:

dow=gl_obj.Sex#抽出对应lable的数据
dow.head()

Out[10]:

0      male
1    female
2    female
3    female
4      male
Name: Sex, dtype: object

 

7.经过把数据类型object=>category，来减小占用的空间

In [11]:

dow_cat=dow.astype('category')#把上面的object转成category类型
dow_cat.head()

Out[11]:

0      male
1    female
2    female
3    female
4      male
Name: Sex, dtype: category
Categories (2, object): [female, male]

In [12]:

dow_cat.head(10).cat.codes#链接字符串，查出不一样类

Out[12]:

0    1
1    0
2    0
3    0
4    1
5    1
6    1
7    1
8    0
9    0
dtype: int8

In [13]:

print(menu_usage(dow))#object类型
print(menu_usage(dow_cat))#category类型，占空间减小了

 

0.05MB
0.00MB

 

8.计算整个表经过转换成category类型后的内存

In [14]:

converted_obj=pd.DataFrame()#定义成空的DataFrame

for col in gl_obj.columns:
    num_unique_values=len(gl_obj[col].unique())
    num_total_values=len(gl_obj[col])
    if num_unique_values/num_total_values<0.5:#寻找重复量最大的一列
        converted_obj.loc[:,col]=gl_obj[col].astype('category')#astype转换类型
    else:
        converted_obj.loc[:,col]=gl_obj[col]
        

In [15]:

print(menu_usage(gl_obj))
print(menu_usage(converted_obj))

 

0.26MB
0.14MB