熊猫的 DataFrame.read_pickle()方法
原文:https://www . geesforgeks . org/data frame-read _ pickle-in-method-pandas/
先决条件:T2【PD . to _ pickle method()
read_pickle() 方法用于将给定对象酸洗(序列化)到文件中。此方法使用下面给出的语法:
语法:
pd.read_pickle(path, compression='infer')
参数:
| **论据** | **类型** | **描述** | | --- | --- | --- | | 小路 | 潜艇用热中子反应堆（submarine thermal reactor 的缩写） | 将加载腌制对象的文件路径。 | | 压缩 | { '推断'，' gzip '，' bz2 '，' zip '，' xz '，' None ' }，默认为'推断' | 用于磁盘数据的动态解压缩。如果“推断”，则使用 gzip、bz2、xz 或 zip(如果路径以结尾)。gz '，. bz2 '，'。xz '，或'。“zip ”,否则不解压缩。设置为无表示不解压缩。 |下面是上述方法的实现，并附有一些例子:
例 1:
Python 3
# 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:
例 2:
Python 3
# 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:
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