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Pandas - 2.2
- pd.Series()
- pd.DataFrame()
- pd.read_pickle()
- pd.read_table()
- pd.read_csv()
- pd.read_fwf()
- pd.read_clipboard()
- pd.read_excel()
- pd.ExcelFile.parse()
- pd.read_json()
- pd.json_normalize()
- pd.read_html()
- pd.read_xml()
- pd.read_hdf()
- pd.read_feather()
- pd.read_parquet()
- pd.read_orc()
- pd.read_sql_table()
- pd.read_sql_query()
- pd.read_sql()
Creation | pd.read_pickle()
Method:
pd.read_pickle(filepath_or_buffer, compression='infer', storage_options=None)
Reads a pickled object stored in a file and returns a DataFrame.
Returns:
pandas.core.frame.DataFrame
Parameters:
filepath_or_buffer: (file_path or buffer), Optional-
Path to the pickle file or file-like object to be read.
import pandas as pd
# Writing and reading a DataFrame with pickle
data = {'Name': ['Better', 'Docs'], 'Age': [25, 30]}
df = pd.DataFrame(data)
df.to_pickle('data.pkl') # Save to pickle
# Read back from pickle
df_loaded = pd.read_pickle(filepath_or_buffer='data.pkl')
print(df_loaded)
'''
Output:
Name Age
0 Better 25
1 Docs 30
'''.to_pickle is used to pickle(serialize) object to file.
compression: str, Optional-
Specifies compression type (e.g., 'gzip', 'bz2'); detects from file extension if 'infer'.
storage_options: dict, Optional-
Dictionary of storage-specific options, such as credentials for cloud storage.