Pandas DataFrame 數(shù)據(jù)存儲格式比較

禁忌石 2023-09-23

展開全文

Pandas 支持多種存儲格式，在本文中將對不同類型存儲格式下的Pandas Dataframe的讀取速度、寫入速度和大小的進行測試對比。

創(chuàng)建測試Dataframe

首先創(chuàng)建一個包含不同類型數(shù)據(jù)的測試Pandas Dataframe。

import pandas as pdimport randomimport stringimport numpy as np# Config DFdf_length= 10**6start_date= '2023-01-01'all_string= list(string.ascii_letters + string.digits)string_length= 10**1min_number= 0max_number= 10**3# Create Columnsdate_col= pd.date_range(start= start_date, periods= df_length, freq= 'H')str_col= [''.join(np.random.choice(all_string, string_length)) for i in range(df_length)]float_col= np.random.rand(df_length)int_col= np.random.randint(min_number,max_number, size = df_length)# Create DataFramedf= pd.DataFrame({'date_col' : date_col, 'str_col' : str_col, 'float_col' : float_col, 'int_col' : int_col})df.info()df.head()

以不同的格式存儲

接下來創(chuàng)建測試函數(shù)，以不同的格式進行讀寫。

import time import osdef check_read_write_size(df, file_name, compression= None) :format= file_name.split('.')[-1]# Writebegin= time.time()if file_name.endswith('.csv') : df.to_csv(file_name, index= False, compression= compression)elif file_name.endswith('.parquet') : df.to_parquet(file_name, compression= compression)elif file_name.endswith('.pickle') : df.to_pickle(file_name, compression= compression)elif file_name.endswith('.orc') : df.to_orc(file_name)elif file_name.endswith('.feather') : df.to_feather(file_name)elif file_name.endswith('.h5') : df.to_hdf(file_name, key= 'df')write_time= time.time() - begin# Readbegin= time.time()if file_name.endswith('.csv') : pd.read_csv(file_name, compression= compression)elif file_name.endswith('.parquet') : pd.read_parquet(file_name)elif file_name.endswith('.pickle') : pd.read_pickle(file_name, compression= compression)elif file_name.endswith('.orc') : pd.read_orc(file_name)elif file_name.endswith('.h5') : pd.read_hdf(file_name)read_time= time.time() - begin# File Sizefile_size_mb = os.path.getsize(file_name) / (1024 * 1024)return [format, compression, read_time, write_time, file_size_mb]

然后運行該函數(shù)并將結(jié)果存儲在另一個Pandas Dataframe中。

test_case= [['df.csv','infer'],['df.csv','gzip'],['df.pickle','infer'],['df.pickle','gzip'],['df.parquet','snappy'],['df.parquet','gzip'],['df.orc','default'],['df.feather','default'],['df.h5','default'],]result= []for i in test_case :result.append(check_read_write_size(df, i[0], compression= i[1]))result_df= pd.DataFrame(result, columns= ['format','compression','read_time','write_time','file_size'])result_df