csv

问题 This question already has answers here : Spark Scala read csv file using s3a (1 answer) How to access s3a:// files from Apache Spark? (10 answers) S3A: fails while S3: works in Spark EMR (2 answers) Closed last year . I have been looking for a clear answer to this question all morning but couldn't find anything understandable. I just started to use pyspark (installed with pip) a bit ago and have a simple .py file reading data from local storage, doing some processing and writing results

问题 This question already has answers here : Spark Scala read csv file using s3a (1 answer) How to access s3a:// files from Apache Spark? (10 answers) S3A: fails while S3: works in Spark EMR (2 answers) Closed last year . I have been looking for a clear answer to this question all morning but couldn't find anything understandable. I just started to use pyspark (installed with pip) a bit ago and have a simple .py file reading data from local storage, doing some processing and writing results

问题 This question already has answers here : Spark Scala read csv file using s3a (1 answer) How to access s3a:// files from Apache Spark? (10 answers) S3A: fails while S3: works in Spark EMR (2 answers) Closed last year . I have been looking for a clear answer to this question all morning but couldn't find anything understandable. I just started to use pyspark (installed with pip) a bit ago and have a simple .py file reading data from local storage, doing some processing and writing results

问题 It seems that loading data from a CSV is faster than from SQL (Postgre SQL) with Pandas. (I have a SSD) Here is my test code : import pandas as pd import numpy as np start = time.time() df = pd.read_csv('foo.csv') df *= 3 duration = time.time() - start print('{0}s'.format(duration)) engine = create_engine('postgresql://user:password@host:port/schema') start = time.time() df = pd.read_sql_query("select * from mytable", engine) df *= 3 duration = time.time() - start print('{0}s'.format(duration

问题 It seems that loading data from a CSV is faster than from SQL (Postgre SQL) with Pandas. (I have a SSD) Here is my test code : import pandas as pd import numpy as np start = time.time() df = pd.read_csv('foo.csv') df *= 3 duration = time.time() - start print('{0}s'.format(duration)) engine = create_engine('postgresql://user:password@host:port/schema') start = time.time() df = pd.read_sql_query("select * from mytable", engine) df *= 3 duration = time.time() - start print('{0}s'.format(duration

问题 It seems that loading data from a CSV is faster than from SQL (Postgre SQL) with Pandas. (I have a SSD) Here is my test code : import pandas as pd import numpy as np start = time.time() df = pd.read_csv('foo.csv') df *= 3 duration = time.time() - start print('{0}s'.format(duration)) engine = create_engine('postgresql://user:password@host:port/schema') start = time.time() df = pd.read_sql_query("select * from mytable", engine) df *= 3 duration = time.time() - start print('{0}s'.format(duration

问题 I have function export_csv() to export file as .csv format. I want to change .dat or .txt format instead of .csv Current code: public function export_csv() { /** check(s) **/ if( ! $this->data) { throw new exception('unable to create xls: missing data'); } if( ! $this->path) { throw new exception('unable to create xls: missing path'); } /** output conetnts to csv **/ ob_start(); $df = fopen($this->path.'.csv', 'w'); foreach ($this->data as $row) { fputcsv($df, $row); } fclose($df); /** return

问题 I am trying to get the column names from a csv file with nearly 4000 rows. There are about 14 columns. I am trying to get each column and store it into a list and then prompt the user to enter themselves at least 5 columns they want to look at. The user should then be able to type how many results they want to see (they should be the smallest results from that column). For example, if they choose clothing_brand, "8", the 8 least expensive brands are displayed. So far, I have been able to use

问题 I am trying to get the column names from a csv file with nearly 4000 rows. There are about 14 columns. I am trying to get each column and store it into a list and then prompt the user to enter themselves at least 5 columns they want to look at. The user should then be able to type how many results they want to see (they should be the smallest results from that column). For example, if they choose clothing_brand, "8", the 8 least expensive brands are displayed. So far, I have been able to use

问题 I have the following function, which writes a vector to a CSV file: #include <math.h> #include <vector> #include <string> #include <fstream> #include <iostream> #include <iterator> using namespace std; bool save_vector(vector<double>* pdata, size_t length, const string& file_path) { ofstream os(file_path.c_str(), ios::binary | ios::out); if (!os.is_open()) { cout << "Failure!" << endl; return false; } os.precision(11); copy(pdata->begin(), pdata->end(), ostream_iterator<double>(os, ",")); os