Python DataFrame.read_pickle()用法及代码示例

read_pickle()方法用于将给定的对象腌制(序列化)到文件中。此方法使用以下语法：

用法:

pd.read_pickle(path, compression='infer')

参数：

参数	类型	描述
path	str	加载腌制对象的文件路径。
compression	{‘infer’，‘gzip’，“ bz2”，‘zip’，‘xz’，无}，默认值为‘infer’	对于on-the-fly，对on-disk数据进行解压缩。如果为‘infer’，则如果路径分别以“ .gz”，“。bz2”，“。xz”或“ .zip”结尾，则使用gzip，bz2，xz或zip，否则不进行解压缩。设置为无不解压。

下面是上述方法的实现和一些示例：

范例1：

Python3

# importing packages  
import pandas as pd  
  
# dictionary of data  
dct = {'ID':{0:23, 1:43, 2:12,  
            3:13, 4:67, 5:89,  
            6:90, 7:56, 8:34},  
    'Name':{0:'Ram', 1:'Deep',  
                2:'Yash', 3:'Aman',  
                4:'Arjun', 5:'Aditya',  
                6:'Divya', 7:'Chalsea',  
                8:'Akash' },  
    'Marks':{0:89, 1:97, 2:45, 3:78,  
                4:56, 5:76, 6:100, 7:87,  
                8:81},  
    'Grade':{0:'B', 1:'A', 2:'F', 3:'C',  
                4:'E', 5:'C', 6:'A', 7:'B',  
                8:'B'}  
    }  
  
# forming dataframe  
data = pd.DataFrame(dct)  
  
# using to_pickle function to form file  
# with name 'pickle_file'  
pd.to_pickle(data,'./pickle_file.pkl') 
  
# unpickled the data by using the 
# pd.read_pickle method 
unpickled_data = pd.read_pickle("./pickle_file.pkl") 
print(unpickled_data)

输出：

范例2：

Python3

# importing packages  
import pandas as pd  
  
# dictionary of data  
dct = {"f1":range(6), "b1":range(6, 12)}  
  
# forming dataframe  
data = pd.DataFrame(dct)  
  
# using to_pickle function to form file  
# with name 'pickle_data'  
pd.to_pickle(data,'./pickle_data.pkl') 
  
# unpickled the data by using the 
# pd.read_pickle method 
unpickled_data = pd.read_pickle("./pickle_data.pkl") 
print(unpickled_data)

输出：

注：本文由纯净天空筛选整理自 DataFrame.read_pickle() method in Pandas。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。