Pandas中的DataFrame.read_pickle()方法
read_pickle()方法被用来将给定的对象腌制(序列化)到文件中。这个方法使用下面的语法。
语法:
pd.read_pickle(path, compression='infer')
参数:
参数 | 类型 | 描述 |
---|---|---|
path | str | 将加载腌制对象的文件路径。 |
compression | {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, 默认 ‘infer’ | 用于磁盘上数据的即时解压。如果是’infer’,那么如果路径以’.gz’、’.bz2’、’.xz’或’.zip’结尾,则分别使用gzip、bz2、xz或zip,否则不进行解压。设置为None表示不解压。 |
下面是上述方法的实现和一些例子。
示例 1:
# 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 :
示例 2:
# 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 :